Oracle University Podcast Oracle Corporation

Welcome to a new season of the Oracle University Podcast, where we delve deep into the world of OCI Container Engine for Kubernetes. Join hosts Lois Houston and Nikita Abraham as they ask senior OCI instructor Mahendra Mehra about the transformative power of containers in application deployment and why they're so crucial in today's software ecosystem.
 
Uncover key differences between virtualization and containerization, and gain insights into Docker components and commands.
 
Getting Started with Oracle Cloud Infrastructure: https://oracleuniversitypodcast.libsyn.com/getting-started-with-oracle-cloud-infrastructure-1
 
Networking in OCI: https://oracleuniversitypodcast.libsyn.com/networking-in-oci
 
OCI Identity and Access Management: https://oracleuniversitypodcast.libsyn.com/oci-identity-and-access-management
 
OCI Container Engine for Kubernetes Specialist: https://mylearn.oracle.com/ou/course/oci-container-engine-for-kubernetes-specialist/134971/210836
 
Oracle University Learning Community: https://education.oracle.com/ou-community
 
LinkedIn: https://www.linkedin.com/showcase/oracle-university/
 
X (formerly Twitter): https://twitter.com/Oracle_Edu
 
Special thanks to Arijit Ghosh, David Wright, 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
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, Principal Technical Editor. 
Nikita: Hi everyone! Welcome to a new season of the Oracle University Podcast. This time around, we’re going to delve into the world of OCI Container Engine for Kubernetes, or OKE. For the next couple of weeks, we’ll cover key aspects of OKE to help you create, manage, and optimize Kubernetes clusters in Oracle Cloud Infrastructure.
00:58
Lois: So, whether you’re a cloud native developer, Kubernetes administrator and developer, a DevOps engineer, or site reliability engineer who wants to enhance your expertise in leveraging the OCI OKE service for cloud native application solutions, you’ll want to tune in to these episodes for sure. And if that doesn’t sound like you, I’ll bet you will find the season interesting even if you’re just looking for a deep dive into this service.
Nikita: That’s right, Lois. In today’s episode, we’ll focus on concepts of containerization, laying the foundation for your journey into the world of containers. And taking us through all this is Mahendra Mehra, a senior OCI instructor with Oracle University.
01:38
Lois: Hi Mahendra! We’re so glad to start our look at containerization with you today. Could you give us an overview? Why is it important in today's software world?
Mahendra: Containerization is a form of virtualization, operates by running applications in isolated user spaces known as containers. 
All these containers share the same underlying operating system. The container engine, pivotal in containerization technologies and container orchestration platforms, serves as the container runtime environment. It effectively manages the creation, deployment, and execution of containers.
02:18
Lois: Can you simplify this for a novice like me, maybe by giving us an analogy? 
Mahendra: Imagine a container as a fully packaged and portable computing environment. It's like a digital suitcase that holds everything an application needs to run—binaries, libraries, configuration files, dependencies, you name it. And the best part, it's all encapsulated and isolated within container.
02:46
Nikita: Mahendra, how is containerization making our lives eas

Listen to Lois Houston and Nikita Abraham, along with Senior Principal Product Manager Wes Prichard, as they explore the five core components of OCI AI services: language, speech, vision, document understanding, and anomaly detection, to help you make better sense of all that unstructured data around you.
 
Oracle MyLearn: https://mylearn.oracle.com/ou/learning-path/become-an-oci-ai-foundations-associate-2023/127177
 
Oracle University Learning Community: https://education.oracle.com/ou-community
 
LinkedIn: https://www.linkedin.com/showcase/oracle-university/
 
X (formerly Twitter): https://twitter.com/Oracle_Edu
 
Special thanks to Arijit Ghosh, David Wright, Himanshu Raj, and the OU Studio Team for helping us create this episode.
 
--------------------------------------------------------
 
Episode Transcript:
 
00:00
The world of artificial intelligence is vast and everchanging. And with all the buzz around it lately, we figured it was the perfect time to revisit our AI Made Easy series. Join us over the next few weeks as we chat about all things AI, helping you to discover its endless possibilities. Ready to dive in? Let’s go!
00:33
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!
00:46
Nikita: Welcome to the Oracle University Podcast! I’m Nikita Abraham, Principal Technical Editor with Oracle University, and with me is Lois Houston, Director of Innovation Programs.
Lois: Hi there! In our last episode, we spoke about OCI AI Portfolio, including AI and ML services, and the OCI AI infrastructure.
Nikita: Yeah, and in today’s episode, we’re going to continue down a similar path and take a closer look at OCI AI services.
01:16
Lois: With us today is Senior Principal Product Manager, Wes Prichard. Hi Wes! It’s lovely to have you here with us. Hemant gave us a broad overview of the various OCI AI services last week, but we’re really hoping to get into each of them with you. So, let’s jump right in and start with the OCI Language service. What can you tell us about it?
Wes: OCI Language analyzes unstructured text for you. It provides models trained on industry data to perform language analysis with no data science experience needed. 
01:48
Nikita: What kind of big things can it do?
Wes: It has five main capabilities. First, it detects the language of the text. It recognizes 75 languages, from Afrikaans to Welsh. 
It identifies entities, things like names, places, dates, emails, currency, organizations, phone numbers--14 types in all. It identifies the sentiment of the text, and not just one sentiment for the entire block of text, but the different sentiments for different aspects. 
02:17
Nikita: What do you mean by that, Wes?
Wes: So let's say you read a restaurant review that said, the food was great, but the service sucked. You'll get food with a positive sentiment and service with a negative sentiment. And it also analyzes the sentiment for every sentence. 
Lois: Ah, that’s smart. Ok, so we covered three capabilities. What else?
Wes: It identifies key phrases in the text that represent the important ideas or subjects. And it classifies the general topic of the text from a list of 600 categories and subcategories. 
02:48
Lois: Ok, and then there’s the OCI Speech service... 
Wes: OCI Speech is very straightforward. It locks the data in audio tracks by converting speech to text. Developers can use Oracle's time-tested acoustic language models to provide highly accurate transcription for audio or video files across multiple languages. 
OCI Speech automatically transcribes audio and video files into text using advanced deep learning techniques. There's no data science experience required. It processes data directly in object storage. And it generates timestamped, grammatically accur

Oracle has been actively focusing on bringing AI to the enterprise at every layer of its tech stack, be it SaaS apps, AI services, infrastructure, or data.
 
In this episode, hosts Lois Houston and Nikita Abraham, along with senior instructors Hemant Gahankari and Himanshu Raj, discuss OCI AI and Machine Learning services. They also go over some key OCI Data Science concepts and responsible AI principles.
 
Oracle MyLearn: https://mylearn.oracle.com/ou/learning-path/become-an-oci-ai-foundations-associate-2023/127177
 
Oracle University Learning Community: https://education.oracle.com/ou-community
 
LinkedIn: https://www.linkedin.com/showcase/oracle-university/
 
X (formerly Twitter): https://twitter.com/Oracle_Edu
 
Special thanks to Arijit Ghosh, David Wright, Himanshu Raj, and the OU Studio Team for helping us create this episode.
 
--------------------------------------------------------
 
Episode Transcript:
 
00:00
The world of artificial intelligence is vast and everchanging. And with all the buzz around it lately, we figured it was the perfect time to revisit our AI Made Easy series. Join us over the next few weeks as we chat about all things AI, helping you to discover its endless possibilities. Ready to dive in? Let’s go!
00:33
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!
00:46
Lois: Welcome to the Oracle University Podcast! I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Principal Technical Editor.
Nikita: Hey everyone! In our last episode, we dove into Generative AI and Language Learning Models. 
Lois: Yeah, that was an interesting one. But today, we’re going to discuss the AI and machine learning services offered by Oracle Cloud Infrastructure, and we’ll look at the OCI AI infrastructure.
Nikita: I’m also going to try and squeeze in a couple of questions on a topic I’m really keen about, which is responsible AI. To take us through all of this, we have two of our colleagues, Hemant Gahankari and Himanshu Raj. Hemant is a Senior Principal OCI Instructor and Himanshu is a Senior Instructor on AI/ML. So, let’s get started!
01:36
Lois: Hi Hemant! We’re so excited to have you here! We know that Oracle has really been focusing on bringing AI to the enterprise at every layer of our stack. 
Hemant: It all begins with data and infrastructure layers. OCI AI services consume data, and AI services, in turn, are consumed by applications. 
This approach involves extensive investment from infrastructure to SaaS applications. Generative AI and massive scale models are the more recent steps. Oracle AI is the portfolio of cloud services for helping organizations use the data they may have for the business-specific uses. 
Business applications consume AI and ML services. The foundation of AI services and ML services is data. AI services contain pre-built models for specific uses. Some of the AI services are pre-trained, and some can be additionally trained by the customer with their own data. 
AI services can be consumed by calling the API for the service, passing in the data to be processed, and the service returns a result. There is no infrastructure to be managed for using AI services. 
02:58
Nikita: How do I access OCI AI services?
Hemant: OCI AI services provide multiple methods for access. The most common method is the OCI Console. The OCI Console provides an easy to use, browser-based interface that enables access to notebook sessions and all the features of all the data science, as well as AI services. 
The REST API provides access to service functionality but requires programming expertise. And API reference is provided in the product documentation. OCI also provides programming language SDKs for Java, Python, TypeScript

In this week’s episode, Lois Houston and Nikita Abraham, along with Senior Instructor Himanshu Raj, take you through the extraordinary capabilities of Generative AI, a subset of deep learning that doesn’t make predictions but rather creates its own content.
 
They also explore the workings of Large Language Models.
 
Oracle MyLearn: https://mylearn.oracle.com/ou/learning-path/become-an-oci-ai-foundations-associate-2023/127177
 
Oracle University Learning Community: https://education.oracle.com/ou-community
 
LinkedIn: https://www.linkedin.com/showcase/oracle-university/
 
X (formerly Twitter): https://twitter.com/Oracle_Edu
 
Special thanks to Arijit Ghosh, David Wright, and the OU Studio Team for helping us create this episode.
 
---------------------------------------------------------
 
Episode Transcript:
 
00:00
The world of artificial intelligence is vast and everchanging. And with all the buzz around it lately, we figured it was the perfect time to revisit our AI Made Easy series. Join us over the next few weeks as we chat about all things AI, helping you to discover its endless possibilities. Ready to dive in? Let’s go!
00:33
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!
00:46
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, Principal Technical Editor. 
Nikita: Hi everyone! In our last episode, we went over the basics of deep learning. Today, we’ll look at generative AI and large language models, and discuss how they work. To help us with that, we have Himanshu Raj, Senior Instructor on AI/ML. So, let’s jump right in. Hi Himanshu, what is generative AI? 
01:21
Himanshu: Generative AI refers to a type of AI that can create new content. It is a subset of deep learning, where the models are trained not to make predictions but rather to generate output on their own. 
Think of generative AI as an artist who looks at a lot of paintings and learns the patterns and styles present in them. Once it has learned these patterns, it can generate new paintings that resembles what it learned.
01:48
Lois: Let's take an example to understand this better. Suppose we want to train a generative AI model to draw a dog. How would we achieve this?
Himanshu: You would start by giving it a lot of pictures of dogs to learn from. The AI does not know anything about what a dog looks like. But by looking at these pictures, it starts to figure out common patterns and features, like dogs often have pointy ears, narrow faces, whiskers, etc. You can then ask it to draw a new picture of a dog. 
The AI will use the patterns it learned to generate a picture that hopefully looks like a dog. But remember, the AI is not copying any of the pictures it has seen before but creating a new image based on the patterns it has learned. This is the basic idea behind generative AI. In practice, the process involves a lot of complex maths and computation, and there are different techniques and architectures that can be used, such as variational autoencoders (VAs) and Generative Adversarial Networks (GANs). 
02:48
Nikita: Himanshu, where is generative AI used in the real world?
Himanshu: Generative AI models have a wide variety of applications across numerous domains. For the image generation, generative models like GANs are used to generate realistic images. They can be used for tasks, like creating artwork, synthesizing images of human faces, or transforming sketches into photorealistic images. 
For text generation, large language models like GPT 3, which are generative in nature, can create human-like text. This has applications in content creation, like writing articles, generating ideas, and again, conversatio

Did you know that the concept of deep learning goes way back to the 1950s? However, it is only in recent years that this technology has created a tremendous amount of buzz (and for good reason!). A subset of machine learning, deep learning is inspired by the structure of the human brain, making it fascinating to learn about.
 
In this episode, Lois Houston and Nikita Abraham interview Senior Principal OCI Instructor Hemant Gahankari about deep learning concepts, including how Convolution Neural Networks work, and help you get your deep learning basics right.
 
Oracle MyLearn: https://mylearn.oracle.com/ou/learning-path/become-an-oci-ai-foundations-associate-2023/127177
 
Oracle University Learning Community: https://education.oracle.com/ou-community
 
LinkedIn: https://www.linkedin.com/showcase/oracle-university/
 
X (formerly Twitter): https://twitter.com/Oracle_Edu
 
Special thanks to Arijit Ghosh, David Wright, Himanshu Raj, and the OU Studio Team for helping us create this episode.
 
--------------------------------------------------------
 
Episode Transcript:
 
00:00
The world of artificial intelligence is vast and everchanging. And with all the buzz around it lately, we figured it was the perfect time to revisit our AI Made Easy series. Join us over the next few weeks as we chat about all things AI, helping you to discover its endless possibilities. Ready to dive in? Let’s go!
00:33
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!
00:47
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, Principal Technical Editor.
Nikita: Hi everyone! 
Lois: Today, we’re going to focus on the basics of deep learning with our Senior Principal OCI Instructor, Hemant Gahankari.
Nikita: Hi Hemant! Thanks for being with us today. So, to get started, what is deep learning?
01:14
Hemant: Hi Niki and hi Lois. So, deep learning is a subset of machine learning that focuses on training Artificial Neural Networks, abbreviated as ANN, to solve a task at hand. Say, for example, image classification. A very important quality of the ANN is that it can process raw data like pixels of an image and extract patterns from it. These patterns are treated as features to predict the outcomes. 
Let us say we have a set of handwritten images of digits 0 to 9. As we know, everyone writes the digits in a slightly different way. So how do we train a machine to identify a handwritten digit? For this, we use ANN. 
ANN accepts image pixels as inputs, extracts patterns like edges and curves and so on, and correlates these patterns to predict an outcome. That is what digit does the image has in this case. 
02:17
Lois: Ok, so what you’re saying is given a bunch of pixels, ANN is able to process pixel data, learn an internal representation of the data, and predict outcomes. That’s so cool! So, why do we need deep learning?
Hemant: We need to specify features while we train machine learning algorithm. With deep learning, features are automatically extracted from the data. Internal representation of features and their combinations is built to predict outcomes by deep learning algorithms. This may not be feasible manually. 
Deep learning algorithms can make use of parallel computations. For this, usually data is split into small batches and processed parallelly. So these algorithms can process large amount of data in a short time to learn the features and their combinations. This leads to scalability and performance. In short, deep learning complements machine learning algorithms for complex data for which features cannot be described easily. 
03:21
Nikita: What can you tell us about the origins of deep learning?
Hemant: Som

Does machine learning feel like too convoluted a topic? Not anymore!
 
Listen to hosts Lois Houston and Nikita Abraham, along with Senior Principal OCI Instructor Hemant Gahankari, talk about foundational machine learning concepts and dive into how supervised learning, unsupervised learning, and reinforcement learning work.
 
Oracle MyLearn: https://mylearn.oracle.com/ou/learning-path/become-an-oci-ai-foundations-associate-2023/127177
 
Oracle University Learning Community: https://education.oracle.com/ou-community
 
LinkedIn: https://www.linkedin.com/showcase/oracle-university/
 
X (formerly Twitter): https://twitter.com/Oracle_Edu
 
Special thanks to Arijit Ghosh, David Wright, Himanshu Raj, and the OU Studio Team for helping us create this episode.
 
--------------------------------------------------------
 
Episode Transcript:
 
00:00
The world of artificial intelligence is vast and everchanging. And with all the buzz around it lately, we figured it was the perfect time to revisit our AI Made Easy series. Join us over the next few weeks as we chat about all things AI, helping you to discover its endless possibilities. Ready to dive in? Let’s go!
00:33
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!
00:47
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, Principal 
Technical Editor. 
Nikita: Hi everyone! Last week, we went through the basics of artificial intelligence and we’re going to take it a step further today by talking about some foundational machine learning concepts. After that, we’ll discuss the three main types of machine learning models: supervised learning, unsupervised learning, and reinforcement learning. 
01:18
Lois: Hemant Gahankari, a Senior Principal OCI Instructor, joins us for this episode. Hi Hemant! Let’s dive right in. What is machine learning? How does it work?
Hemant: Machine learning is a subset of artificial intelligence that focuses on creating computer systems that can learn and predict outcomes from given examples without being explicitly programmed. It is powered by algorithms that incorporate intelligence into machines by automatically learning from a set of examples usually provided as data. 
01:54
Nikita: Give us a few examples of machine learning… so we can see what it can do for us.
Hemant: Machine learning is used by all of us in our day-to-day life. 
When we shop online, we get product recommendations based on our preferences and our shopping history. This is powered by machine learning. 
We are notified about movies recommendations based on our viewing history and choices of other similar viewers. This too is driven by machine learning. 
While browsing emails, we are warned of a spam mail because machine learning classifies whether the mail is spam or not based on its content. In the increasingly popular self-driving cars, machine learning is responsible for taking the car to its destination. 
02:45
Lois: So, how does machine learning actually work?
Hemant: Let us say we have a computer and we need to teach the computer to differentiate between a cat and a dog. We do this by describing features of a cat or a dog. 
Dogs and cats have distinguishing features. For example, the body color, texture, eye color are some of the defining features which can be used to differentiate a cat from a dog. These are collectively called as input data. 
We also provide a corresponding output, which is called as a label, which can be a dog or a cat in this case. By describing a specific set of features, we can say that it is a cat or a dog. 
Machine learning model is first trained with the data set. Training data set consists of a set of featur