Talking AWS for Datascience

Kalicharan m
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  • التكنولوجيا
  • يتم التحديث أسبوعيًا
Talking AWS for Datascience

Implementing Data science on AWS could be a daunting task, but if you know the right kind of tools to use then then life of a data scientist becomes very easy. In this podcast, two data science experts Kali and Deepti having more than 2 decades of software development experience talk about our experience of implementing successful data science projects with the help of AWS Cloud. Hopefully our conversions on using the AWS services will help you become a great data scientist. Please give your feedback by sending an email to mkalicharan42@gmail.com

  1. Demand Forecast made easy

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    Demand Forecast made easy

    Forcasting has huge number of use cases across industries. Be it Inventory forcasting, product sales or man power, forcasting helps us in eliminating unwanted expenses. Todays episode we talk about forcasting on aws. How to upload the timeseries data and forcast for each product also about additional benefits like what if analysis

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  2. How can managers save time on Datascience Projects?

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    How can managers save time on Datascience Projects?

    Managing your datascience projects is different from managing your typical IT projects. Here we provide tips on how managers can use AWS Sagemaker Feature store to save time and streamline the entire process of feature engineering across their datascience projects.

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    Talking to CEO of an IIOT based AI Startup

    Todays episode, I will be talking to the founder and CEO of an AI Startup called MeghaAI (www.meghaai.com). Meghani has build a product where he claims to have automated the entire datascience pipeline for collecting industrial IOT data to building anomaly detection on it. This he claims helps many industries perform automated machine learning without the need of hiring datascientists. Lets listen to him talk about how he started his journey and how AWS has helped him build the product.

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  4. Understanding Bias and Variance

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    Understanding Bias and Variance

    Todays episode we introduce you to machine learning models that have prediction errors, and these prediction errors are usually known as Bias and Variance. In machine learning, there will always be a deviation between the model predictions and actual predictions. The main aim of ML/data scientists is to reduce these errors in order to get more accurate results. In this episode we are going to discuss bias and variance, Bias-variance trade-off, Underfitting and Overfitting. Also, we would take a quick look on how AWS Sagemaker clarify helps us to understand data and model bias

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  5. Monitor ML models in Production

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    Monitor ML models in Production

    Machine learning models are very different from code. When you deploy code you don't really need to monitor it on how it is delivering the results. However, ML models are different, we need to monitor their input data and measure them to a baseline. This is what we talk about in todays episode and talk on Services like AWS Sagemaker, Model Monitor, Model Drift and data collection. The process of Model Monitor is part of the MLOps lifecycle

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  6. Machine Learning with Zero Code

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    Machine Learning with Zero Code

    If you are someone with zero programming skills and would still like to build ML models. Sagemaker Auto Pilot is the right tool for you. In this podcast me and deepti discuss a simple usecase of how to build a text classifier using Service Now dump. Listen on

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  7. Implementing MLOps on AWS

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    Implementing MLOps on AWS

    MLOps is a set of practices for collaboration and communication between data scientists and operations professionals. Applying these practices increases the quality, simplifies the management process, and automates the deployment of Machine Learning and Deep Learning models in large-scale production environments. We talk about using AWS Sagemaker, Jenkins, Github and Stepfunction along with Sagemaker Pipelines

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  8. How to build data pipelines on AWS?

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    How to build data pipelines on AWS?

    In computing, a pipeline, also known as a data pipeline, is a set of data processing elements connected in series, where the output of one element is the input of the next one. Here in AWS we talk about the data pipeline service and also talk about other services datascientists can use to build the end to end data processing pipelines for machine learning. The services we talk about are DynamoDB AWS S3 EC2 Data Pipelines Sagemaker

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مشاهدة الكل (١٣)

حول

Implementing Data science on AWS could be a daunting task, but if you know the right kind of tools to use then then life of a data scientist becomes very easy. In this podcast, two data science experts Kali and Deepti having more than 2 decades of software development experience talk about our experience of implementing successful data science projects with the help of AWS Cloud. Hopefully our conversions on using the AWS services will help you become a great data scientist. Please give your feedback by sending an email to mkalicharan42@gmail.com

المعلومات

  • صناع العمل
    Kalicharan m
  • سنوات النشاط
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  • الحلقات
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  • التقييم
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  • حقوق النشر
    © Kalicharan m
  • موقع البرنامج على الويب
    Talking AWS for Datascience

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