Oracle University Podcast

Oracle Corporation

Oracle University Podcast delivers convenient, foundational training on popular Oracle technologies such as Oracle Cloud Infrastructure, Java, Autonomous Database, and more to help you jump-start or advance your career in the cloud.

  1. Buy or Build AI?

    -3 J

    Buy or Build AI?

    How do you decide whether to buy a ready-made AI solution or build one from the ground up? The choice is more than just a technical decision; it’s about aligning AI with your business goals.   In this episode, Lois Houston and Nikita Abraham are joined by Principal Instructor Yunus Mohammed to examine the critical factors influencing the buy vs. build debate. They explore real-world examples where businesses must weigh speed, customization, and long-term strategy. From a startup using a SaaS chatbot to a bank developing a custom fraud detection model, Yunus provides practical insights on when to choose one approach over the other.   AI for You: https://mylearn.oracle.com/ou/course/ai-for-you/152601/   Oracle University Learning Community: https://education.oracle.com/ou-community   LinkedIn: https://www.linkedin.com/showcase/oracle-university/   X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode.   ---------------------------------------------------------------   Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:26 Nikita: Welcome to the Oracle University Podcast! I’m Nikita Abraham, Team Lead: Editorial Services with Oracle University, and with me is Lois Houston, Director of Innovation Programs. Lois: Hi there! Last week, we spoke about the key stages in a typical AI workflow and how data quality, feedback loops, and business goals influence AI success. 00:50 Nikita: In today’s episode, we’re going to explore whether you should buy or build AI apps. Joining us again is Principal Instructor Yunus Mohammed. Hi Yunus, let’s jump right in. Why does the decision of buy versus build matter? Yunus: So when we talk about buy versus build matters, we need to consider the strategic business decisions over here. They are related to the strategic decisions which the business makes, and it is evaluated in the decision lens. So the center of the decision lens is the business objective, which identifies what are we trying to solve. Then evaluate our constraints based on that particular business objective like the cost, the time, and the talent. And finally, we can decide whether we need to buy or build. But remember, there is no single correct answer. What's right for one business may not be working for the other one. 01:54 Lois: OK, can you give us examples of both approaches? Yunus: The first example where we have got a startup using a SaaS AI chatbot. Now, being a startup, they have to choose a ready-made solution, which is an AI chatbot. Now, the question is, why did they do this? Because speed and simplicity mattered more than deep customization that is required for the chatbot. So, their main aim was to have it ready in short period of time and make it more simpler. And this actually lead them to get to the market fast with low upfront cost and minimal technical complexities. But in some situations, it might be different. Like, your bank, which needs to build a fraud model. It cannot be outsourced or got from the shelf. So, they build a custom model in-house. With this custom model, they actually have a tighter control, and it is tuned to their standards. And it is created by their experts. So these two generic examples, the chatbot and the fraud model example, helps you in identifying whether I should go for a SaaS product with simple choice of selecting an existing LLM endpoint and not making any changes. Or should I go with model depending on my business and organization requirement and fine tuning that model later to define a better implementation of the scenarios or conditions that I want to do which are specific to my organization. So here you decide with the reference whether I want it to be done faster, or whether I want to be more customized to my organization. So buy it, when it is generic, or build when it is strategic. The SaaS, which is basically software as a service, refers to ready to use cloud-based applications that you access via internet. You can log into the platform and use the built-in AI, there's no setup requirement for those. Real-world examples can be Oracle Fusion apps with AI features enabled. So in-house integration means embedding AI with my own requirements into your own systems, often using custom APIs, data pipelines, and hosting it. It gives you more flexibility but requires a lot of resources and expertise. So real-world example for this scenario can be a logistics heavy company, which is integrating a customer support model into their CX. 04:41 Lois: But what are the pros and cons of each approach? Yunus: So, SaaS and Fusion Applications, basically, they offer fast deployment with little to no coding required, making them ideal for business looking to get started quickly and faster. And they typically come with lower upfront costs and are maintained by vendor, which means updates, security, support are handled externally. However, there are limited customizations and are best suited for common, repeatable use cases. Like, it can be a standard chatbot, or it can be reporting tools, or off the shelf analytics that you want to use. But the in-house or custom integration, you have more control, but it takes longer to build and requires a higher initial investment. The in-house or custom integration approach allows full customization of the features and the workflows, enabling you to design and tailor the AI system to your specific needs. 05:47 Nikita: If you're weighing the choice between buying or building, what are the critical business considerations you'd need to take into account? Yunus: So let's take one of the key business consideration which is time to market. If your goal is to launch fast, maybe you're a startup trying to gain traction quickly, then a prebuilt plug and play AI solution, for example, a chatbot or any other standard analytical tool, might be your best bet. But if you have time and you are aiming for precision, a custom model could be worth the wait. Prebuilt SaaS tools usually have lower upfront costs and a subscription model. It works with putting subscriptions. Custom solutions, on the other hand, may require a bigger investment upfront. In development, you require high talent and infrastructures, but could offer cost savings in the long run. So, ask yourself a question here. Is this AI helping us stand out in the market? If the answer is yes, you may want to build something which is your proprietary. For example, an organization would use a generic recommendation engine. It's a part of their secret sauce. Some use cases require flexibility, like you want to tailor the rules to match your specific risk criteria. So, under that scenarios, you will go for customizing. So, you will go with off the shelf solutions may not give you deep enough requirements that you want to evaluate. So, you get those and you try to customize those. You can go for customization of your AI features. The other important key business consideration is the talent and expertise that your organization have. So, the question that you need to ask in the organization is, do you have an internal team who is well versed in developing AI solutions for you? Or do you have access to one of the teams which can help you build your own proprietary products? If not, you'll go with SaaS. If you do have, then building could unlock greater control over your AI features and AI models. The next core component is your security and data privacy. If you're handling sensitive information, like for example, the health care or finance data, you might not want to send your data to the third-party tools. So in-house models offer better control over data security and compliance. When we leverage a model, it could be a prebuilt or custom model. 08:50 Oracle University is proud to announce three brand new courses that will help your teams unlock the power of Redwood—the next generation design system. Redwood enhances the user experience, boosts efficiency, and ensures consistency across Oracle Fusion Cloud Applications. Whether you're a functional lead, configuration consultant, administrator, developer, or IT support analyst, these courses will introduce you to the Redwood philosophy and its business impact. They’ll also teach you how to use Visual Builder Studio to personalize and extend your Fusion environment. Get started today by visiting mylearn.oracle.com.  09:31 Nikita: Welcome back! So, getting back to what you were saying before the break, what are pre-built and custom models? Yunus: A prebuilt model is an AI solution that has already been trained by someone else, typically a tech provider. It can be used to perform a specific task like recognizing images, translating text, or detecting sentiments. You can think of it like buying a preassembled appliance. You plug it in, configure a few settings, and it's ready to use. You don't need to know how the internal parts work. You benefit from the speed, ease, and reliability of this particular model, which is a prebuilt model. But you can't easily change how it works under the hood. Whereas, a custom model is an AI solution that your organization designs and trains and tunes specifically for their business problems using their own data. You can think of it like designing your own suit. It takes more time and effort to create. It is built to your exact measurements and needs. And you have full control over how it performs and evolves. 10:53 Lois: So, when would you choose a pre-built versus a custom model? Yunus: Depending on speed, simplicity, control, and customization, you can decide on using a prebuilt or to create a custom model. Prebuilt models ar

    16 min

  2. 2 SEPT.

    The AI Workflow

    Join Lois Houston and Nikita Abraham as they chat with Yunus Mohammed, a Principal Instructor at Oracle University, about the key stages of AI model development. From gathering and preparing data to selecting, training, and deploying models, learn how each phase impacts AI’s real-world effectiveness. The discussion also highlights why monitoring AI performance and addressing evolving challenges are critical for long-term success.   AI for You: https://mylearn.oracle.com/ou/course/ai-for-you/152601/252500   Oracle University Learning Community: https://education.oracle.com/ou-community   LinkedIn: https://www.linkedin.com/showcase/oracle-university/   X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode.   --------------------------------------------------------------   Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:25 Lois: Welcome to the Oracle University Podcast! I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Team Lead: Editorial Services. Nikita: Hey everyone! In our last episode, we spoke about generative AI and gen AI agents. Today, we’re going to look at the key stages in a typical AI workflow. We’ll also discuss how data quality, feedback loops, and business goals influence AI success. With us today is Yunus Mohammed, a Principal Instructor at Oracle University.  01:00 Lois: Hi Yunus! We're excited to have you here! Can you walk us through the various steps in developing and deploying an AI model?  Yunus: The first point is the collect data. We gather relevant data, either historical or real time. Like customer transactions, support tickets, survey feedbacks, or sensor logs. A travel company, for example, can collect past booking data to predict future demand. So, data is the most crucial and the important component for building your AI models. But it's not just the data. You need to prepare the data. In the prepared data process, we clean, organize, and label the data. AI can't learn from messy spreadsheets. We try to make the data more understandable and organized, like removing duplicates, filling missing values in the data with some default values or formatting dates. All these comes under organization of the data and give a label to the data, so that the data becomes more supervised. After preparing the data, I go for selecting the model to train. So now, we pick what type of model fits your goals. It can be a traditional ML model or a deep learning network model, or it can be a generative model. The model is chosen based on the business problems and the data we have. So, we train the model using the prepared data, so it can learn the patterns of the data. Then after the model is trained, I need to evaluate the model. You check how well the model performs. Is it accurate? Is it fair? The metrics of the evaluation will vary based on the goal that you're trying to reach. If your model misclassifies emails as spam and it is doing it very much often, then it is not ready. So I need to train it further. So I need to train it to a level when it identifies the official mail as official mail and spam mail as spam mail accurately.  After evaluating and making sure your model is perfectly fitting, you go for the next step, which is called the deploy model. Once we are happy, we put it into the real world, like into a CRM, or a web application, or an API. So, I can configure that with an API, which is application programming interface, or I add it to a CRM, Customer Relationship Management, or I add it to a web application that I've got. Like for example, a chatbot becomes available on your company's website, and the chatbot might be using a generative AI model. Once I have deployed the model and it is working fine, I need to keep track of this model, how it is working, and need to monitor and improve whenever needed. So I go for a stage, which is called as monitor and improve. So AI isn't set in and forget it. So over time, there are lot of changes that is happening to the data. So we monitor performance and retrain when needed. An e-commerce recommendation model needs updates as there might be trends which are shifting.  So the end user finally sees the results after all the processes. A better product, or a smarter service, or a faster decision-making model, if we do this right. That is, if we process the flow perfectly, they may not even realize AI is behind it to give them the accurate results.  04:59 Nikita: Got it. So, everything in AI begins with data. But what are the different types of data used in AI development?  Yunus: We work with three main types of data: structured, unstructured, and semi-structured. Structured data is like a clean set of tables in Excel or databases, which consists of rows and columns with clear and consistent data information. Unstructured is messy data, like your email or customer calls that records videos or social media posts, so they all comes under unstructured data.  Semi-structured data is things like logs on XML files or JSON files. Not quite neat but not entirely messy either. So they are, they are termed semi-structured. So structured, unstructured, and then you've got the semi-structured. 05:58 Nikita: Ok… and how do the data needs vary for different AI approaches?  Yunus: Machine learning often needs labeled data. Like a bank might feed past transactions labeled as fraud or not fraud to train a fraud detection model. But machine learning also includes unsupervised learning, like clustering customer spending behavior. Here, no labels are needed. In deep learning, it needs a lot of data, usually unstructured, like thousands of loan documents, call recordings, or scan checks. These are fed into the models and the neural networks to detect and complex patterns. Data science focus on insights rather than the predictions. So a data scientist at the bank might use customer relationship management exports and customer demographies to analyze which age group prefers credit cards over the loans. Then we have got generative AI that thrives on diverse, unstructured internet scalable data. Like it is getting data from books, code, images, chat logs. So these models, like ChatGPT, are trained to generate responses or mimic the styles and synthesize content. So generative AI can power a banking virtual assistant trained on chat logs and frequently asked questions to answer customer queries 24/7. 07:35 Lois: What are the challenges when dealing with data?  Yunus: Data isn't just about having enough. We must also think about quality. Is it accurate and relevant? Volume. Do we have enough for the model to learn from? And is my data consisting of any kind of unfairly defined structures, like rejecting more loan applications from a certain zip code, which actually gives you a bias of data? And also the privacy. Are we handling personal data responsibly or not? Especially data which is critical or which is regulated, like the banking sector or health data of the patients. Before building anything smart, we must start smart.  08:23 Lois: So, we’ve established that collecting the right data is non-negotiable for success. Then comes preparing it, right?  Yunus: This is arguably the most important part of any AI or data science project. Clean data leads to reliable predictions. Imagine you have a column for age, and someone accidentally entered an age of like 999. That's likely a data entry error. Or maybe a few rows have missing ages. So we either fix, remove, or impute such issues. This step ensures our model isn't misled by incorrect values. Dates are often stored in different formats. For instance, a date, can be stored as the month and the day values, or it can be stored in some places as day first and month next. We want to bring everything into a consistent, usable format. This process is called as transformation. The machine learning models can get confused if one feature, like example the income ranges from 10,000 to 100,000, and another, like the number of kids, range from 0 to 5. So we normalize or scale values to bring them to a similar range, say 0 or 1. So we actually put it as yes or no options. So models don't understand words like small, medium, or large. We convert them into numbers using encoding. One simple way is assigning 1, 2, and 3 respectively. And then you have got removing stop words like the punctuations, et cetera, and break the sentence into smaller meaningful units called as tokens. This is actually used for generative AI tasks. In deep learning, especially for Gen AI, image or audio inputs must be of uniform size and format.  10:31 Lois: And does each AI system have a different way of preparing data?  Yunus: For machine learning ML, focus is on cleaning, encoding, and scaling. Deep learning needs resizing and normalization for text and images. Data science, about reshaping, aggregating, and getting it ready for insights. The generative AI needs special preparation like chunking, tokenizing large documents, or compressing images. 11:06 Oracle University’s Race to Certification 2025 is your ticket to free training and certification in today’s hottest tech. Whether you’re starting with Artificial Intelligence, Oracle Cloud Infrastructure, Multicloud, or Oracle Data Platform, this challenge covers it all! Learn more about your chance to win prizes and see your name on the Leaderboard by visiting education.oracle.com/race-to-certification-2025. That’s education.oracle.com/race-to-certification-2025. 11:50 Nikita: Welcome back! Yunus, how does a user choose the right model to sol

    22 min

  3. 26 AOÛT

    Core AI Concepts – Part 3

    Join hosts Lois Houston and Nikita Abraham, along with Principal AI/ML Instructor Himanshu Raj, as they discuss the transformative world of Generative AI. Together, they uncover the ways in which generative AI agents are changing the way we interact with technology, automating tasks and delivering new possibilities.   AI for You: https://mylearn.oracle.com/ou/course/ai-for-you/152601/252500   Oracle University Learning Community: https://education.oracle.com/ou-community   LinkedIn: https://www.linkedin.com/showcase/oracle-university/   X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. ------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:25 Lois: Welcome to the Oracle University Podcast! I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Team Lead of Editorial Services.   Nikita: Hi everyone! Last week was Part 2 of our conversation on core AI concepts, where we went over the basics of data science. In Part 3 today, we’ll look at generative AI and gen AI agents in detail. To help us with that, we have Himanshu Raj, Principal AI/ML Instructor. Hi Himanshu, what’s the difference between traditional AI and generative AI?  01:01 Himanshu: So until now, when we talked about artificial intelligence, we usually meant models that could analyze information and make decisions based on it, like a judge who looks at evidence and gives a verdict. And that's what we call traditional AI that's focused on analysis, classification, and prediction.  But with generative AI, something remarkable happens. Generative AI does not just evaluate. It creates. It's more like a storyteller who uses knowledge from the past to imagine and build something brand new. For example, instead of just detecting if an email is spam, generative AI could write an entirely new email for you.  Another example, traditional AI might predict what a photo contains. Generative AI, on the other hand, creates a brand-new photo based on description. Generative AI refers to artificial intelligence models that can create entirely new content, such as text, images, music, code, or video that resembles human-made work.  Instead of simple analyzing or predicting, generative AI produces something original that resembles what a human might create.   02:16 Lois: How did traditional AI progress to the generative AI we know today?  Himanshu: First, we will look at small supervised learning. So in early days, AI models were trained on small labeled data sets. For example, we could train a model with a few thousand emails labeled spam or not spam. The model would learn simple decision boundaries. If email contains, "congratulations," it might be spam. This was efficient for a straightforward task, but it struggled with anything more complex.  Then, comes the large supervised learning. As the internet exploded, massive data sets became available, so millions of images, billions of text snippets, and models got better because they had much more data and stronger compute power and thanks to advances, like GPUs, and cloud computing, for example, training a model on millions of product reviews to predict customer sentiment, positive or negative, or to classify thousands of images in cars, dogs, planes, etc.  Models became more sophisticated, capturing deeper patterns rather than simple rules. And then, generative AI came into the picture, and we eventually reached a point where instead of just classifying or predicting, models could generate entirely new content.  Generative AI models like ChatGPT or GitHub Copilot are trained on enormous data sets, not to simply answer a yes or no, but to create outputs that look and feel like human made. Instead of judging the spam or sentiment, now the model can write an article, compose a song, or paint a picture, or generate new software code.  03:55 Nikita: Himanshu, what motivated this sort of progression?   Himanshu: Because of the three reasons. First one, data, we had way more of it thanks to the internet, smartphones, and social media. Second is compute. Graphics cards, GPUs, parallel computing, and cloud systems made it cheap and fast to train giant models.  And third, and most important is ambition. Humans always wanted machines not just to judge existing data, but to create new knowledge, art, and ideas.   04:25 Lois: So, what’s happening behind the scenes? How is gen AI making these things happen?  Himanshu: Generative AI is about creating entirely new things across different domains. On one side, we have large language models or LLMs.  They are masters of generating text conversations, stories, emails, and even code. And on the other side, we have diffusion models. They are the creative artists of AI, turning text prompts into detailed images, paintings, or even videos.  And these two together are like two different specialists. The LLM acts like a brain that understands and talks, and the diffusion model acts like an artist that paints based on the instructions. And when we connect these spaces together, we create something called multimodal AI, systems that can take in text and produce images, audio, or other media, opening a whole new range of possibilities.  It can not only take the text, but also deal in different media options. So today when we say ChatGPT or Gemini, they can generate images, and it's not just one model doing everything. These are specialized systems working together behind the scenes.  05:38 Lois: You mentioned large language models and how they power text-based gen AI, so let’s talk more about them. Himanshu, what is an LLM and how does it work?  Himanshu: So it's a probabilistic model of text, which means, it tries to predict what word is most likely to come next based on what came before.  This ability to predict one word at a time intelligently is what builds full sentences, paragraphs, and even stories.  06:06 Nikita: But what’s large about this? Why’s it called a large language model?   Himanshu: It simply means the model has lots and lots of parameters. And think of parameters as adjustable dials the model fine tuned during learning.  There is no strict rule, but today, large models can have billions or even trillions of these parameters. And the more the parameters, more complex patterns, the model can understand and can generate a language better, more like human.  06:37 Nikita: Ok… and image-based generative AI is powered by diffusion models, right? How do they work?  Himanshu: Diffusion models start with something that looks like pure random noise.  Imagine static on an old TV screen. No meaningful image at all. From there, the model carefully removes noise step by step to create something more meaningful and think of it like sculpting a statue. You start with a rough block of stone and slowly, carefully you chisel away to reveal a beautiful sculpture hidden inside.  And in each step of this process, the AI is making an educated guess based on everything it has learned from millions of real images. It's trying to predict.   07:24 Stay current by taking the 2025 Oracle Fusion Cloud Applications Delta Certifications. This is your chance to demonstrate your understanding of the latest features and prove your expertise by obtaining a globally recognized certification, all for free! Discover the certification paths, use the resources on MyLearn to prepare, and future-proof your skills. Get started now at mylearn.oracle.com.  07:53 Nikita: Welcome back! Himanshu, for most of us, our experience with generative AI is with text-based tools like ChatGPT. But I’m sure the uses go far beyond that, right? Can you walk us through some of them?  Himanshu: First one is text generation. So we can talk about chatbots, which are now capable of handling nuanced customer queries in banking travel and retail, saving companies hours of support time. Think of a bank chatbot helping a customer understand mortgage options or virtual HR Assistant in a large company, handling leave request. You can have embedding models which powers smart search systems.  Instead of searching by keywords, businesses can now search by meaning. For instance, a legal firm can search cases about contract violations in tech and get semantically relevant results, even if those exact words are not used in the documents.  The third one, for example, code generation, tools like GitHub Copilot help developers write boilerplate or even functional code, accelerating software development, especially in routine or repetitive tasks. Imagine writing a waveform with just a few prompts.  The second application, is image generation. So first obvious use is art. So designers and marketers can generate creative concepts instantly. Say, you need illustrations for a campaign on future cities. Generative AI can produce dozens of stylized visuals in minutes.  For design, interior designers or architects use it to visualize room layouts or design ideas even before a blueprint is finalized. And realistic images, retail companies generate images of people wearing their clothing items without needing real models or photoshoots, and this reduces the cost and increase the personalization.  Third application is multimodal systems, and these are combined systems that take one kind of input or a combination of different inputs and produce different kind of outputs, or can even combine various kinds, be it text image in both input and output.  Text to image It's being used in e-commerce, movie concept art, and

    23 min

  4. 19 AOÛT

    Core AI Concepts – Part 2

    In this episode, Lois Houston and Nikita Abraham continue their discussion on AI fundamentals, diving into Data Science with Principal AI/ML Instructor Himanshu Raj. They explore key concepts like data collection, cleaning, and analysis, and talk about how quality data drives impactful insights.   AI for You: https://mylearn.oracle.com/ou/course/ai-for-you/152601/252500   Oracle University Learning Community: https://education.oracle.com/ou-community   LinkedIn: https://www.linkedin.com/showcase/oracle-university/   X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. ---------------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:25 Lois: Hello and welcome to the Oracle University Podcast. I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me today is Nikita Abraham, Team Lead: Editorial Services.  Nikita: Hi everyone! Last week, we began our exploration of core AI concepts, specifically machine learning and deep learning. I’d really encourage you to go back and listen to the episode if you missed it.   00:52 Lois: Yeah, today we’re continuing that discussion, focusing on data science, with our Principal AI/ML Instructor Himanshu Raj.  Nikita: Hi Himanshu! Thanks for joining us again. So, let’s get cracking! What is data science?  01:06 Himanshu: It's about collecting, organizing, analyzing, and interpreting data to uncover valuable insights that help us make better business decisions. Think of data science as the engine that transforms raw information into strategic action.  You can think of a data scientist as a detective. They gather clues, which is our data. Connect the dots between those clues and ultimately solve mysteries, meaning they find hidden patterns that can drive value.  01:33 Nikita: Ok, and how does this happen exactly?  Himanshu: Just like a detective relies on both instincts and evidence, data science blends domain expertise and analytical techniques. First, we collect raw data. Then we prepare and clean it because messy data leads to messy conclusions. Next, we analyze to find meaningful patterns in that data. And finally, we turn those patterns into actionable insights that businesses can trust.  02:00 Lois: So what you’re saying is, data science is not just about technology; it's about turning information into intelligence that organizations can act on. Can you walk us through the typical steps a data scientist follows in a real-world project?  Himanshu: So it all begins with business understanding. Identifying the real problem we are trying to solve. It's not about collecting data blindly. It's about asking the right business questions first. And once we know the problem, we move to data collection, which is gathering the relevant data from available sources, whether internal or external.  Next one is data cleaning. Probably the least glamorous but one of the most important steps. And this is where we fix missing values, remove errors, and ensure that the data is usable. Then we perform data analysis or what we call exploratory data analysis.  Here we look for patterns, prints, and initial signals hidden inside the data. After that comes the modeling and evaluation, where we apply machine learning or deep learning techniques to predict, classify, or forecast outcomes. Machine learning, deep learning are like specialized equipment in a data science detective's toolkit. Powerful but not the whole investigation.  We also check how good the models are in terms of accuracy, relevance, and business usefulness. Finally, if the model meets expectations, we move to deployment and monitoring, putting the model into real world use and continuously watching how it performs over time.  03:34 Nikita: So, it’s a linear process?  Himanshu: It's not linear. That's because in real world data science projects, the process does not stop after deployment. Once the model is live, business needs may evolve, new data may become available, or unexpected patterns may emerge.  And that's why we come back to business understanding again, defining the questions, the strategy, and sometimes even the goals based on what we have learned. In a way, a good data science project behaves like living in a system which grows, adapts, and improves over time. Continuous improvement keeps it aligned with business value.   Now, think of it like adjusting your GPS while driving. The route you plan initially might change as new traffic data comes in. Similarly, in data science, new information constantly help refine our course. The quality of our data determines the quality of our results.   If the data we feed into our models is messy, inaccurate, or incomplete, the outputs, no matter how sophisticated the technology, will be also unreliable. And this concept is often called garbage in, garbage out. Bad input leads to bad output.  Now, think of it like cooking. Even the world's best Michelin star chef can't create a masterpiece with spoiled or poor-quality ingredients. In the same way, even the most advanced AI models can't perform well if the data they are trained on is flawed.  05:05 Lois: Yeah, that's why high-quality data is not just nice to have, it’s absolutely essential. But Himanshu, what makes data good?   Himanshu: Good data has a few essential qualities. The first one is complete. Make sure we aren't missing any critical field. For example, every customer record must have a phone number and an email. It should be accurate. The data should reflect reality. If a customer's address has changed, it must be updated, not outdated. Third, it should be consistent. Similar data must follow the same format. Imagine if the dates are written differently, like 2024/04/28 versus April 28, 2024. We must standardize them.   Fourth one. Good data should be relevant. We collect only the data that actually helps solve our business question, not unnecessary noise. And last one, it should be timely. So data should be up to date. Using last year's purchase data for a real time recommendation engine wouldn't be helpful.  06:13 Nikita: Ok, so ideally, we should use good data. But that’s a bit difficult in reality, right? Because what comes to us is often pretty messy. So, how do we convert bad data into good data? I’m sure there are processes we use to do this.  Himanshu: First one is cleaning. So this is about correcting simple mistakes, like fixing typos in city names or standardizing dates.  The second one is imputation. So if some values are missing, we fill them intelligently, for instance, using the average income for a missing salary field. Third one is filtering. In this, we remove irrelevant or noisy records, like discarding fake email signups from marketing data. The fourth one is enriching. We can even enhance our data by adding trusted external sources, like appending credit scores from a verified bureau.  And the last one is transformation. Here, we finally reshape data formats to be consistent, for example, converting all units to the same currency. So even messy data can become usable, but it takes deliberate effort, structured process, and attention to quality at every step.  07:26 Oracle University’s Race to Certification 2025 is your ticket to free training and certification in today’s hottest technology. Whether you’re starting with Artificial Intelligence, Oracle Cloud Infrastructure, Multicloud, or Oracle Data Platform, this challenge covers it all! Learn more about your chance to win prizes and see your name on the Leaderboard by visiting education.oracle.com/race-to-certification-2025. That’s education.oracle.com/race-to-certification-2025. 08:10 Nikita: Welcome back! Himanshu, we spoke about how to clean data. Now, once we get high-quality data, how do we analyze it?  Himanshu: In data science, there are four primary types of analysis we typically apply depending on the business goal we are trying to achieve.  The first one is descriptive analysis. It helps summarize and report what has happened. So often using averages, totals, or percentages. For example, retailers use descriptive analysis to understand things like what was the average customer spend last quarter? How did store foot traffic trend across months?  The second one is diagnostic analysis. Diagnostic analysis digs deeper into why something happened. For example, hospitals use this type of analysis to find out, for example, why a certain department has higher patient readmission rates. Was it due to staffing, post-treatment care, or patient demographics?  The third one is predictive analysis. Predictive analysis looks forward, trying to forecast future outcomes based on historical patterns. For example, energy companies predict future electricity demand, so they can better manage resources and avoid shortages. And the last one is prescriptive analysis. So it does not just predict. It recommends specific actions to take.  So logistics and supply chain companies use prescriptive analytics to suggest the most efficient delivery routes or warehouse stocking strategies based on traffic patterns, order volume, and delivery deadlines.   09:42 Lois: So really, we’re using data science to solve everyday problems. Can you walk us through some practical examples of how it’s being applied?  Himanshu: The first one is predictive maintenance. It is done in manufacturing a lot. A factory collects real time sensor data from machines. Data scientists first clean and organize this massive data stream, explore patterns of past failures, and

    13 min

  5. 12 AOÛT

    Core AI Concepts – Part 1

    Join hosts Lois Houston and Nikita Abraham, along with Principal AI/ML Instructor Himanshu Raj, as they dive deeper into the world of artificial intelligence, analyzing the types of machine learning. They also discuss deep learning, including how it works, its applications, and its advantages and challenges. From chatbot assistants to speech-to-text systems and image recognition, they explore how deep learning is powering the tools we use today.   AI for You: https://mylearn.oracle.com/ou/course/ai-for-you/152601/252500   Oracle University Learning Community: https://education.oracle.com/ou-community   LinkedIn: https://www.linkedin.com/showcase/oracle-university/   X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. ------------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:25 Lois: Hello and welcome to the Oracle University Podcast. I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Team Lead: Editorial Services. Nikita: Hi everyone! Last week, we went through the basics of artificial intelligence. If you missed it, I really recommend listening to that episode before you start this one. Today, we’re going to explore some foundational AI concepts, starting with machine learning. After that, we’ll discuss the two main machine learning models: supervised learning and unsupervised learning. And we’ll close with deep learning. Lois: Himanshu Raj, our Principal AI/ML Instructor, joins us for today’s episode. Hi Himanshu! Let’s dive right in. What is machine learning?  01:12 Himanshu: Machine learning lets computers learn from examples to make decisions or predictions without being told exactly what to do. They help computers learn from past data and examples so they can spot patterns and make smart decisions just like humans do, but faster and at scale.  01:31 Nikita: Can you give us a simple analogy so we can understand this better? Himanshu: When you train a dog to sit or fetch, you don't explain the logic behind the command. Instead, you give this dog examples and reinforce correct behavior with rewards, which could be a treat, a pat, or a praise. Over time, the dog learns to associate the command with the action and reward. Machine learning learns in a similar way, but with data instead of dog treats. We feed a mathematical system called models with multiple examples of input and the desired output, and it learns the pattern. It's trial and error, learning from the experience.  Here is another example. Recognizing faces. Humans are incredibly good at this, even as babies. We don't need someone to explain every detail of the face. We just see many faces over time and learn the patterns. Machine learning models can be trained the same way. We showed them thousands or millions of face images, each labeled, and they start to detect patterns like eyes, nose, mouth, spacing, different angles. So eventually, they can recognize faces they have seen before or even match new ones that are similar. So machine learning doesn't have any rules, it's just learning from examples. This is the kind of learning behind things like face ID on your smartphone, security systems that recognizes employees, or even Facebook tagging people in your photos. 03:05 Lois: So, what you’re saying is, in machine learning, instead of telling the computer exactly what to do in every situation, you feed the model with data and give it examples of inputs and the correct outputs. Over time, the model figures out patterns and relationships within the data on its own, and it can make the smart guess when it sees something new. I got it! Now let’s move on to how machine learning actually works? Can you take us through the process step by step? Himanshu: Machine learning actually happens in three steps. First, we have the input, which is the training data. Think of this as showing the model a series of examples. It could be images of historical sales data or customer complaints, whatever we want the machine to learn from. Next comes the pattern finding. This is the brain of the system where the model starts spotting relationships in the data. It figures out things like customer who churn or leave usually contacts support twice in the same month. It's not given rules, it just learns patterns based on the example. And finally, we have output, which is the prediction or decision. This is the result of all this learning. Once trained, the computer or model can say this customer is likely to churn or leave. It's like having a smart assistant that makes fast, data-driven guesses without needing step by step instruction. 04:36 Nikita: What are the main elements in machine learning? Himanshu: In machine learning, we work with two main elements, features and labels. You can think of features as the clues we provide to the model, pieces of information like age, income, or product type. And the label is the solution we want the model to predict, like whether a customer will buy or not.  04:55 Nikita: Ok, I think we need an example here. Let’s go with the one you mentioned earlier about customers who churn. Himanshu: Imagine we have a table with data like customer age, number of visits, whether they churned or not. And each of these rows is one example. The features are age and visit count. The label is whether the customer churned, that is yes or no. Over the time, the model might learn patterns like customer under 30 who visit only once are more likely to leave. Or frequent visitors above age 45 rarely churn. If features are the clues, then the label is the solution, and the model is the brain of the system. It's what's the machine learning builds after learning from many examples, just like we do. And again, the better the features are, the better the learning. ML is just looking for patterns in the data we give it. 05:51 Lois: Ok, we’re with you so far. Let’s talk about the different types of machine learning. What is supervised learning? Himanshu: Supervised learning is a type of machine learning where the model learns from the input data and the correct answers. Once trained, the model can use what it learned to predict the correct answer for new, unseen inputs. Think of it like a student learning from a teacher. The teacher shows labeled examples like an apple and says, "this is an apple." The student receives feedback whether their guess was right or wrong. Over time, the student learns to recognize new apples on their own. And that's exactly how supervised learning works. It's learning from feedback using labeled data and then make predictions. 06:38 Nikita: Ok, so supervised learning means we train the model using labeled data. We already know the right answers, and we're essentially teaching the model to connect the dots between the inputs and the expected outputs. Now, can you give us a few real-world examples of supervised learning? Himanshu: First, house price prediction. In this case, we give the model features like a square footage, location, and number of bedrooms, and the label is the actual house price. Over time, it learns how to predict prices for new homes. The second one is email: spam or not. In this case, features might include words in the subject line, sender, or links in the email. The label is whether the email is spam or not. The model learns patterns to help us filter our inbox, as you would have seen in your Gmail inboxes. The third one is cat versus dog classification. Here, the features are the pixels in an image, and the label tells us whether it's a cat or a dog. After seeing many examples, the model learns to tell the difference on its own. Let's now focus on one very common form of supervised learning, that is regression. Regression is used when we want to predict a numerical value, not a category. In simple terms, it helps answer questions like, how much will it be? Or what will be the value be? For example, predicting the price of a house based on its size, location, and number of rooms. Or estimating next quarter's revenue based on marketing spend.  08:18 Lois: Are there any other types of supervised learning? Himanshu: While regression is about predicting a number, classification is about predicting a category or type. You can think of it as the model answering is this yes or no, or which group does this belong to.  Classification is used when the goal is to predict a category or a class. Here, the model learns patterns from historical data where both the input variables, known as features, and the correct categories, called labels, are already known.  08:53 Ready to level-up your cloud skills? The 2025 Oracle Fusion Cloud Applications Certifications are here! These industry-recognized credentials validate your expertise in the latest Oracle Fusion Cloud solutions, giving you a competitive edge and helping drive real project success and customer satisfaction. Explore the certification paths, prepare with MyLearn, and position yourself for the future. Visit mylearn.oracle.com to get started today. 09:25 Nikita: Welcome back! So that was supervised machine learning. What about unsupervised machine learning, Himanshu? Himanshu: Unlike supervised learning, here, the model is not given any labels or correct answers. It just handed the raw input data and left to make sense of it on its own.  The model explores the data and discovers hidden patterns, groupings, or structures on its own, without being explicitly told what to look for. And it's more like a student learning from observations and making their own infe

    20 min

  6. 5 AOÛT

    What is AI?

    In this episode, hosts Lois Houston and Nikita Abraham, together with Senior Cloud Engineer Nick Commisso, break down the basics of artificial intelligence (AI). They discuss the differences between Artificial General Intelligence (AGI) and Artificial Narrow Intelligence (ANI), and explore the concepts of machine learning, deep learning, and generative AI. Nick also shares examples of how AI is used in everyday life, from navigation apps to spam filters, and explains how AI can help businesses cut costs and boost revenue.   AI for You: https://mylearn.oracle.com/ou/course/ai-for-you/152601/252500   Oracle University Learning Community: https://education.oracle.com/ou-community   LinkedIn: https://www.linkedin.com/showcase/oracle-university/   X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. ----------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:25 Nikita: Hello and welcome to the Oracle University Podcast. I’m Nikita Abraham, Team Lead of Editorial Services with Oracle University, and with me is Lois Houston, Director of Innovation Programs. Lois: Hi everyone! Welcome to a new season of the podcast. I’m so excited about this one because we’re going to dive into the world of artificial intelligence, speaking to many experts in the field. Nikita: If you've been listening to us for a while, you probably know we’ve covered AI from a bunch of different angles. But this time, we’re dialing it all the way back to basics. We wanted to create something for the absolute beginner, so no jargon, no assumptions, just simple conversations that anyone can follow. 01:08 Lois: That’s right, Niki. You don’t need to have a technical background or prior experience with AI to get the most out of these episodes. In our upcoming conversations, we’ll break down the basics of AI, explore how it's shaping the world around us, and understand its impact on your business. Nikita: The idea is to give you a practical understanding of AI that you can use in your work, especially if you’re in sales, marketing, operations, HR, or even customer service.  01:37 Lois: Today, we’ll talk about the basics of AI with Senior Cloud Engineer Nick Commisso. Hi Nick! Welcome back to the podcast. Can you tell us about human intelligence and how it relates to artificial intelligence? And within AI, I know we have Artificial General Intelligence, or AGI, and Artificial Narrow Intelligence, or ANI. What’s the difference between the two? Nick: Human intelligence is the intellectual capability of humans that allow us to learn new skills through observation and mental digestion, to think through and understand abstract concepts and apply reasoning, to communicate using language and understand non-verbal cues, such as facial expressions, tone variation, body language. We can handle objections and situations in real time, even in a complex setting. We can plan for short and long-term situations or projects. And we can create music, art, or invent something new or have original ideas. If machines can replicate a wide range of human cognitive abilities, such as learning, reasoning, or problem solving, we call it artificial general intelligence.  Now, AGI is hypothetical for now, but when we apply AI to solve problems with specific, narrow objectives, we call it artificial narrow intelligence, or ANI. AGI is a hypothetical AI that thinks like a human. It represents the ultimate goal of artificial intelligence, which is a system capable of chatting, learning, and even arguing like us. If AGI existed, it would take the form like a robot doctor that accurately diagnoses and comforts patients, or an AI teacher that customizes lessons in real time based on each student's mood, pace, and learning style, or an AI therapist that comprehends complex emotions and provides empathetic, personalized support. ANI, on the other hand, focuses on doing one thing really well. It's designed to perform specific tasks by recognizing patterns and following rules, but it doesn't truly understand or think beyond its narrow scope. Think of ANI as a specialist. Your phone's face ID can recognize you instantly, but it can't carry on a conversation. Google Maps finds the best route, but it can't write you a poem. And spam filters catch junk mail, but it can't make you coffee. So, most of the AI you interact with today is ANI. It's smart, efficient, and practical, but limited to specific functions without general reasoning or creativity. 04:22 Nikita: Ok then what about Generative AI?  Nick: Generative AI is a type of AI that can produce content such as audio, text, code, video, and images. ChatGPT can write essays, but it can't fact check itself. DALL-E creates art, but it doesn't actually know if it's good. Or AI song covers can create deepfakes like Drake singing "Baby Shark."  04:47 Lois: Why should I care about AI? Why is it important? Nick: AI is already part of your everyday life, often working quietly in the background. ANI powers things like navigation apps, voice assistants, and spam filters. Generative AI helps create everything from custom playlists to smart writing tools. And while AGI isn't here yet, it's shaping ideas about what the future might look like. Now, AI is not just a buzzword, it's a tool that's changing how we live, work, and interact with the world. So, whether you're using it or learning about it or just curious, it's worth knowing what's behind the tech that's becoming part of everyday life.  05:32 Lois: Nick, whenever people talk about AI, they also throw around terms like machine learning and deep learning. What are they and how do they relate to AI? Nick: As we shared earlier, AI is the ability of machines to imitate human intelligence. And Machine Learning, or ML, is a subset of AI where the algorithms are used to learn from past data and predict outcomes on new data or to identify trends from the past. Deep Learning, or DL, is a subset of machine learning that uses neural networks to learn patterns from complex data and make predictions or classifications. And Generative AI, or GenAI, on the other hand, is a specific application of DL focused on creating new content, such as text, images, and audio, by learning the underlying structure of the training data.  06:24 Nikita: AI is often associated with key domains like language, speech, and vision, right? So, could you walk us through some of the specific tasks or applications within each of these areas? Nick: Language-related AI tasks can be text related or generative AI. Text-related AI tasks use text as input, and the output can vary depending on the task. Some examples include detecting language, extracting entities in a text, extracting key phrases, and so on.  06:54 Lois: Ok, I get you. That’s like translating text, where you can use a text translation tool, type your text in the box, choose your source and target language, and then click Translate. That would be an example of a text-related AI task. What about generative AI language tasks? Nick: These are generative, which means the output text is generated by the model. Some examples are creating text, like stories or poems, summarizing texts, and answering questions, and so on. 07:25 Nikita: What about speech and vision? Nick: Speech-related AI tasks can be audio related or generative AI. Speech-related AI tasks use audio or speech as input, and the output can vary depending on the task. For example, speech to text conversion, speaker recognition, or voice conversion, and so on. Generative AI tasks are generative, i.e., the output audio is generated by the model (for example, music composition or speech synthesis). Vision-related AI tasks can be image related or generative AI. Image-related AI tasks use an image as the input, and the output depends on the task. Some examples are classifying images or identifying objects in an image. Facial recognition is one of the most popular image-related tasks that's often used for surveillance and tracking people in real time. It's used in a lot of different fields, like security and biometrics, law enforcement, entertainment, and social media. For generative AI tasks, the output image is generated by the model. For example, creating an image from a textual description or generating images of specific style or high resolution, and so on. It can create extremely realistic new images and videos by generating original 3D models of objects, such as machine, buildings, medications, people and landscapes, and so much more. 08:58 Lois: This is so fascinating. So, now we know what AI is capable of. But Nick, what is AI good at? Nick: AI frees you to focus on creativity and more challenging parts of your work. Now, AI isn't magic. It's just very good at certain tasks. It handles work that's repetitive, time consuming, or too complex for humans, like processing data or spotting patterns in large data sets.  AI can take over routine tasks that are essential but monotonous. Examples include entering data into spreadsheets, processing invoices, or even scheduling meetings, freeing up time for more meaningful work. AI can support professionals by extending their abilities. Now, this includes tools like AI-assisted coding for developers, real-time language translation for travelers or global teams, and advanced image analysis to help doctors interpret medical scans much more accurately. 10:00 Nikita: And what would you say is AI's sweet spot? Nick: That would be tasks that are both doable and valuable. A few examples of tasks that are feasible technically and h

    18 min
  7. Modernize Your Business with Oracle Cloud Apps – Part 2

    29 JUILL. · CONTENU BONI

    Modernize Your Business with Oracle Cloud Apps – Part 2

    In this episode, hosts Lois Houston and Nikita Abraham welcome back Cloud Delivery Lead Sarah Mahalik for a detailed tour of the four pillars of Oracle Fusion Cloud Applications: ERP, HCM, SCM, and CX.   Discover how Oracle weaves AI, analytics, and automation into every layer of enterprise operations. Plus, learn how Oracle Modern Best Practice is redefining digital workflows.   Oracle Fusion Cloud Applications: Process Essentials https://mylearn.oracle.com/ou/course/oracle-fusion-cloud-applications-foundation-hcm/146870 https://mylearn.oracle.com/ou/course/oracle-fusion-cloud-applications-foundations-enterprise-resource-planning-erp/146928/241047 https://mylearn.oracle.com/ou/course/oracle-fusion-cloud-applications-foundation-scm/146938 https://mylearn.oracle.com/ou/course/oracle-fusion-cloud-applications-foundation-cx/146972   Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. ------------------------------------------------------------- Episode Transcript:   00:00   Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started!   00:25   Lois: Hello and welcome to the Oracle University Podcast! I’m Lois Houston, Director of Innovation Programs with Oracle University, and joining me is Nikita Abraham, Team Lead: Editorial Services.     Nikita: Hi everyone! Last week, we spoke about Oracle Cloud Apps and the Redwood design system. Today, we’ll take a closer look at the four key pillars of Oracle Cloud Apps.    Lois: And we’re so excited to have Sarah Mahalik back with us. Sarah is a Cloud Delivery Lead here at Oracle. Hi Sarah! In the last episode, we briefly spoke about the various Oracle Cloud Apps offerings and their capabilities. For anyone who missed that episode, can you give us a quick introduction?   01:06   Sarah: Oracle Cloud Applications is an incredibly broad suite that covers many of the most important business functions, from Human Capital Management, Supply Chain Management, to Enterprise Resource Planning and Customer Experience. The products in the Oracle Fusion Cloud Applications suite are organized by functional groups or pillars. All of these applications sit on Oracle Cloud Infrastructure, a foundation built from scratch to support mission-critical applications.    Oracle Fusion Applications deliver a single source of truth, enabling quick responses to disruptions and market opportunities. With unified data and consistent business rules, teams can build streamlined end-to-end processes, access real time analytics, and make faster data-driven decisions for improved outcomes.   01:52   Nikita: Ok, let’s actually get into each of these areas. I think we can start with Human Capital Management.   Sarah: Oracle Human Capital Management is an end-to-end solution that allows you to manage all aspects of people data from hire to retire. It all starts with recruiting, or requisitions are used to advertise vacant positions, and candidates are managed through the hiring process. After recruitment, successful candidates are transferred to the human resources module.   You can configure the organization structure to mirror that of your business. And this allows for easy reorganization whenever the structure changes. People data is a staple element of HCM. Therefore, as part of this product, an HR specialist can manage everything about the employee life cycle, including promotions, transfers, general assignment changes, and terminations.   A robust self-service offering allows employees and managers to take ownership and responsibility for the data pertaining to themselves and their teams. By removing the burden of simple data processing from the HR specialists, it not only eases the pressure on the HR department but allows them to concentrate on more specialized tasks.   03:00   Lois: And how are the core products of HCM categorized?   Sarah: The core products of Human Capital Management are categorized into four main groupings according to their logical purpose. First up, we have our human resources. This grouping includes the elements for implementing and maintaining the enterprise and workforce structure and employee life cycle data. This is where you would configure the organization structure as well as manage an employee's data from the HR specialist point of view. In addition, modules such as benefits, work life, workforce modeling and planning, and advanced HCM controls also sit within this category.   This brings us to talent management. This category is one of the largest because it includes recruiting, learning, goals and performance management, career development, succession planning, talent reviews, and compensation. In addition to that, dynamic skills and opportunity marketplace are also included in this grouping. Within workforce management, you'll find absence management and time and labor.   These naturally sit together because most organizations that implement both configure it so that an employee can enter both work time and absences on a time card, instead of having to visit two different entry points. You'll also find workforce health and safety here. And finally, payroll. All aspects of payroll are included here, whether you're simply using global payroll or localizations, such as UK, Canada, and Mexico.   It also encompasses payroll interface for those organizations that run their payroll from another system, and just need to extract and migrate the relevant data from Fusion HCM Cloud. When talking about HCM systems, we cannot forget the employee self-service aspect of the product. For this, there's an employee experience module called Oracle Me. Here you'll find options, such as HCM communicate, touchpoints, journeys, HR help desk, and Oracle digital assistant.   All of these combined enable an employee to take control and ownership of their own data, and use the many self-help options to get the information they need quickly and efficiently. In order to control how the system behaves and how users interact with it and perform the various processes, there are configuration options. These options allow organizations to define such things as the user experience, workflows, and approval policies based on their business requirements. And to meet the constant need for reporting, there's analytics, planning, and data modeling.   And in addition to all of that, you can use configuration options, such as extensibility, integration, or import and extracts, security, and adaptive intelligence to help enhance the system and have it working and looking the way you need. Of course, much of these latter configuration items are not exclusive to HCM but are available for the Oracle Fusion Cloud as a whole.   05:47   Lois: That’s great. Ok, let’s move on to Oracle Enterprise Resource Planning, or ERP.   Sarah: This is a complete modern Cloud ERP suite that provides your teams with advanced capabilities, such as AI, to automate the manual processes that slow them down, analytics to react to market shifts in real time, and automatic updates to stay current and gain a competitive advantage.   Oracle Cloud ERP automates the entire Record to Report process and provides a common repository of information for global financial reporting and compliance. Within ERP, we have the broadest and deepest suite offering everything you need, from financials, project management, enterprise performance management, risk management and compliance, and analytics.    06:34   Nikita: Sarah, could you break down the different modules within ERP?   Sarah: First, we have Financials, which is a global financial platform that connects and automates your financial management processes, including payables, receivables, fixed assets, expenses, and reporting for a clear view into your total financial health. Oracle Project Management offers a single project cloud solution designed to help you gain a complete picture of your organization's project finances and operations. It's seamlessly integrated across the enterprise with the Oracle Fusion Cloud ERP, HCM, and SCM applications.   Oracle Fusion Cloud Enterprise Performance Management, or EPM, helps you model and plan across finance, HR, supply chain and sales, streamline the financial close process, and drive better decisions. Oracle Fusion Cloud Risk Management and Compliance is a security and audit solution that controls user access to your Oracle Cloud ERP financial data, monitors user activity, and makes it easier to meet compliance regulations through automation.   Oracle Risk Management Compliance uses AI and ML to strengthen financial controls to help prevent cash leaks, enforce audit, and protect against emerging risks, saving you hours of manual work. Oracle Analytics for Cloud ERP complements the embedded analytics in Cloud ERP to provide pre-packaged use cases, predictive analysis, and KPIs based on variance analysis and historical trends.    08:06   Lois: And what about Supply Chain Management?   Sarah: Oracle Supply Chain Management empowers organizations to plan, source, make, deliver, and service goods with agility and resilience. It offers a solution that integrates advanced capabilities, such as AI/ML and blockchain, to optimize the supply chain life cycle from start to finish.   08:31   Adopting a multicloud strategy is a big step towards future-proofing your business and we’re here to help you navigate this complex landscape. With our suite of courses, you'll gain insights into network connectiv

    16 min
  8. Modernize Your Business with Oracle Cloud Apps – Part 1

    22 JUILL. · CONTENU BONI

    Modernize Your Business with Oracle Cloud Apps – Part 1

    Join hosts Lois Houston and Nikita Abraham, along with Cloud Delivery Lead Sarah Mahalik, as they unpack the core pillars of Oracle Fusion Cloud Applications—ERP, HCM, SCM, and CX.   Learn how Oracle’s SaaS model, Redwood UX, and built-in AI are reshaping business productivity, adaptability, and user experience. From quarterly updates to advanced AI agents, discover how Oracle delivers agility, lower costs, and smarter decision-making across departments.   Oracle Fusion Cloud Applications: Process Essentials https://mylearn.oracle.com/ou/course/oracle-fusion-cloud-applications-foundation-hcm/146870 https://mylearn.oracle.com/ou/course/oracle-fusion-cloud-applications-foundations-enterprise-resource-planning-erp/146928/241047 https://mylearn.oracle.com/ou/course/oracle-fusion-cloud-applications-foundation-scm/146938 https://mylearn.oracle.com/ou/course/oracle-fusion-cloud-applications-foundation-cx/146972   Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. -------------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:25 Nikita: Welcome to the Oracle University Podcast! I’m Nikita Abraham, Team Lead: Editorial Services with Oracle University, and with me is Lois Houston, Director of Innovation Programs.  Lois: Hi everyone! In our last two episodes, we explored the Oracle Cloud Success Navigator platform. This week and next, we’re diving into Oracle Fusion Cloud Applications with Sarah Mahalik, a Cloud Delivery Lead here at Oracle. We’ll ask Sarah about Oracle’s cloud apps suite, the Redwood design system, and also look at some of Oracle’s AI capabilities.  01:02 Nikita: Yeah, let’s jump right in. Hi Sarah! How does Oracle approach the SaaS model? Sarah: Oracle's Cloud Applications suite is a complete enterprise cloud designed to modernize your business. Our cloud suite of SaaS applications, which includes Enterprise Resource Planning, or ERP, Supply Chain Management, or SCM, Human Capital Management, or HCM, and Customer Experience, or CX, brings consistent processes and a single source of truth across the most important business functions.  At Oracle, we own all of the technology stacks that power our suite of cloud applications. Oracle Cloud Applications are built on Oracle Cloud Infrastructure and ensure the performance, resiliency, and security that enterprises need. Your business no longer needs to worry about maintaining a data center, hardware, operating systems, database, network, or all of the security. With deep integrations, a common data model, and a unified user interface, these applications help improve customer engagement, increase agility, and accelerate response to change. Oracle's Cloud Applications are updated quarterly with new features and improvements. These updates are based on our deep understanding of customer's functional needs, as well as modern technologies such as artificial intelligence, machine learning, blockchain, and digital assistants. Expectations for user experience only go up. Oracle's Redwood User Experience methodology ensures those expectations are matched and exceeded by including powerful and predictive search, a look and feel that actually helps users see what they need to in the order they need to see it, and by providing conversational and micro-interactions. Oracle, as a SaaS provider, puts the customer first by having enough dedicated resources to ensure zero downtime and increasing the speed of implementation by eliminating much of the hardware and software setup activity. 02:59 Nikita: What are the advantages of adopting Oracle Cloud Apps? Sarah: First off, Oracle provides automatic quarterly updates, and they're usable immediately. Customers can focus on leveraging the new functionality instead of spending cycles on installing it. There's much more accessibility because Oracle hosts the heavy part of the applications and customers access it via thin clients. The applications can be used from nearly anywhere and on a wide range of devices, including smartphones and tablets. Another great advantage is speed and agility. A lot of the benefits you see here result from Oracle's provider model. That means customers aren't spending time on customization, application testing, and report development. Instead, they work on the much lighter and faster tasks of configuration, validation, and leveraging embedded analytics. And finally, it's just better economics. Because of the pricing model, it is easy to compare an on-premises implementation cost. While upfront costs are almost always lower, overall operational costs and risk are usually lower as well. This translates to better total cost of ownership and improved overall economics and agility for your business. 04:10 Lois: Sarah, in your experience, why do customers love Oracle Cloud Apps? Sarah: At Oracle, we empower you with embedded AI that drives real breakthroughs in productivity and efficiency, helping you stay ahead of the curve. With the power of Oracle Cloud Infrastructure, you get the best of performance, security, and scalability, making it the perfect foundation for your business. Our modern user experience is intuitive and designed with your needs in mind, while our relentless innovation is focused on what truly matters to you. Above all, our commitment to your success is unwavering. We're here to support you every step of the way, ensuring you thrive and grow with Oracle. 04:49 Lois: Let’s talk about Oracle’s Redwood design system. What is it? And how does it enhance the user experience?  Sarah: Redwood is the name of Oracle's next-generation user experience. Redwood design system is a collection of prefabricated components, templates, and patterns to enable developers to quickly create very sophisticated and polished interactions that are upgrade safe. It provides a consumer grade-plus experience, where you have high-quality functionality that can be used across multiple devices. You have access to insightful data readily at your fingertips for quick access and decision making, with the option to personalize your application to create your own state of the art experience. Processes and entry time will now be more efficient and streamlined by having fewer clicks and faster downloads, which will lead to high productivity in areas that matter the most. The Redwood design is intelligent, meaning you have access to AI, where you will receive recommendations and guidance based on your preferences and business processes. It's also adaptable, allowing you to use the same tools to create new experiences by using the Business Rule Framework with modern UX components. Oracle's Redwood user experience will help you to be more productive, efficient, and engaged with a highly personalized experience. 06:11 Are you keen to stay ahead in today's fast-paced world? We’ve got your back! Each quarter, Oracle rolls out game-changing updates to its Fusion Cloud Applications. And to make sure you’re always in the know, we offer New Features courses that give you an insider’s look at all of the latest advancements. Don't miss out! Head over to mylearn.oracle.com to get started.  06:37 Nikita: Welcome back! Sarah, you said the Redwood design system is adaptable. Can you elaborate on what you mean by that?  Sarah: In a nutshell, this means that developers can extend their applications using the same development platform that Oracle Cloud Applications are built on. Oracle Visual Builder Studio is a robust application development platform that enables users to rapidly create and extend web, mobile, and progressive web interfaces using a visual development environment. It streamlines application development and reduces coding, while also providing flexibility and support for popular build and testing frameworks. With Oracle Visual Builder Studio, users can build apps for the web, create progressive web apps, and develop on-device mobile apps. The tool also offers access to REST services and allows for planning and managing development processes, as well as managing the code lifecycle. Additionally, Oracle Visual Builder Studio provides hosting for apps along with easy publishing and version management. Changes made using Visual Builder Studio are called Application Extensions.  Visual Builder Studio Express Mode has two key components: Business Rules and Constants. Use Business Rules, which is the Redwood equivalent to Transaction Design Studio for responsive pages, to leverage delivered best practices or create your own rules based on various criteria, such as country and business unit. Make fields and regions required or optional, read-only or editable, and show or hide fields in regions, depending on specific criteria. Use the various delivered Constants to customize your Redwood pages to best fit your specific business needs, such as hide the evaluation panel and connections or reorder the columns in the person search result table. 08:23 Lois: Sarah, here’s a question that's probably on everyone's mind—what about AI for Fusion Applications? Sarah: Oracle integrates AI into Fusion Applications, enabling faster, better decision making and empowering your workforce. With both classic and generative AI embedded, customers can access AI-driven insights seamlessly within their everyday software environment. In HCM, AI helps to automate routine tasks. It's also used to attract and manage talent more efficiently by doing things like r

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Oracle University Podcast delivers convenient, foundational training on popular Oracle technologies such as Oracle Cloud Infrastructure, Java, Autonomous Database, and more to help you jump-start or advance your career in the cloud.

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  • Créateur
    Oracle Corporation
  • Années d’activité
    2023 - 2025
  • Épisodes
    133
  • Classement
    Tout public
  • Copyright
    © 2023 Oracle Corporation
  • Site Web de l’émission
    Oracle University Podcast

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