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.

Services:
Amazon Sagemaker, AWS Lambda, AWS Step Functions, Amazon ECS, Amazon EKS, AWS Code services

Presenter:
Aashmeet Kalra, Solutions Architect, Amazon Web Services