In this course you will learn how to train and deploy ML models in the Amazon Web Services (AWS) Cloud. This includes how to use Amazon S3 to store your data, AWS Sagemaker to train ML models, how to deploy these models as a REST Endpoint, and how to use Amazon Lambda serverless features to connect these models to an external URL to use with your applications. We will also test out the REST endpoint using a Python application.
For this exercise we will use an example of Bank Churn (predicting whether a bank customer is likely to leave the bank). We will provide sample datasets and python programs to run with this use case.
This workshop is fun and exciting and will show you how to use Cloud AI services to not just train your models but also how to deploy them and use them in real life for your smart applications. You can use these techniques to add ML to your development projects, add ML to your applications, or to build new applications.
- Session 1: Aug 29th 9am-12pm PT
- 3 Hours/ 1 Session
- Lectures / hands-on code labs
- Live session and real time interaction
- Watch session replay anywhere any time
Check the content tab for full course outlines.
WHO SHOULD LEARN:
Developers, data scientists, students.
Basic familiarity with machine learning
AWS account (its free to sign up)
If you miss the live session or want to learn again, you can watch recorded sessions any time, along with interactive learning tools, slides, course notes
founder of Pyxeda AI. Previously, Nisha co-founded ParallelM which pioneered the MLOps practice of managing machine learning in production. Nisha is a recognized leader in the operational machine learning space. Nisha was previously a Fellow at SanDisk and Fellow/Lead Architect at Fusion-io, where she worked on innovation in non-volatile memory technologies and applications.