Data Scientists and ML developers need more than a Jupyter notebook to create a ML model, test it, put into production and integrate it with a portal and/or a basic web/mobile application in a reliable and flexible way. There are three basic questions that you should consider when you start developing a ML model for a real Business Case: How can machine learning be implemented and not just remain a research project in an organisation? How long would it take your organisation to deploy a change that involves a single line of code? Can you do this on a repeatable, reliable basis? So, if you're not happy with the answers, MLOps is a concept that can help you: a) to create or improve the organisation culture for CI/CD; b) to create an automated infrastructure that will support your processes. This session will take you through a journey of operational models that will allow builders to innovate faster while reducing time errors and cost.
What you will learn:
How to get started with machine learning? How to automate machine learning delivery end-to-end? How can Developers, DevOps, Data- Engineers and Data-Scientists collaborate to build an effective outcome for Machine learning projects? Tools and tips for building, deploying and monitoring machine learning projects.
Amazon Sagemaker, AWS Lambda, AWS Step Functions, Amazon ECS, Amazon EKS, AWS Code services
Aashmeet Kalra, Solutions Architect, Amazon Web Services