Doing MLOps

Getting your models into production is the fundamental challenge of machine learning. MLOps offers a set of proven principles aimed at solving this problem in a reliable and automated way. This training takes you through what MLOps is (and how it differs from DevOps) and shows you how to put it into practice to operationalize your machine learning models. Current and aspiring machine learning engineers--or anyone familiar with data science and Python--will build a foundation in MLOps tools and methods (along with AutoML and monitoring and logging), then learn how to implement them in AWS, Microsoft Azure, and Google Cloud. The faster you deliver a machine learning system that works, the faster you can focus on the business problems you're trying to crack. This training gives you a head start.

Take-aways

MLOps, Kaizen, Cloud - What MLOps is, the motivation behind it, and why it’s the next frontier in applied machine learning. - Learn how to harness cloud technologies like AWS AppRunner to deploy and monitor machine learning models in production. - Summary of use cases and challenges in MLOps, and how to begin the MLOps journey in your organization. SESSION OUTLINE: Doing MLOps INTRODUCTION (5 minutes) In this section of your talk, DataCamp will set expectations for Q&A, engage the audience with questions on what they want out of this session, and introduce you! MOTIVATION (5-10 minutes) - Why do we need MLOps and what is it? - How the Covid19 crisis revealed the need for MLOps - How to get started with MLOps - What are major MLOPs platforms DEEP-DIVE (30-35 minutes) I - Doing MLOps - Learn to set up a Python project for CI/CD - Setup project scaffolding: Makefile, tests, and linting - Configure testing with Github Actions - Learn to build Microservices using Python MLOps Cookbook - Setup Python command-line tools - Setup Python Flask Microservice - Learn to deploy to AWS Cloud using AWS App runner - Setup an AWS App Runner project - Finalize a Continuous Deployment project - Verify a Machine Learning Prediction works CLOSING TALK & Q&A (10-15 minutes) - Summary of use cases and challenges - How to implement MLOps in your organization - Learn to implement CI/CD - Learn to perform Data Engineering Best Practices - Use KaizenML as a best practice - Q&A

In this episode

Noah Gift

Founder , Pragmatic AI Labs

Noah Gift is the founder of Pragmatic A.I. Labs and lectures on cloud computing at top universities globally, including the Duke and Northwestern graduate data science programs. He designs graduate machine learning, MLOps, A.I., and data science courses, consults on machine learning and cloud architecture for AWS, and is a massive advocate of AWS Machine Learning and putting machine-learning models into production. Noah has authored several books, including Practical MLOps, Pragmatic AI, Python for DevOps, and Cloud Computing for Data Analysis. He has created content around AWS for top course providers including Udacity, O'Reilly, Pearson, and DataCamp. You can find many AWS examples from Noah by following him on LinkedIn.

Demetrios Brinkmann

Host

Demetrios is one of the main organizers of the MLOps community and currently resides in a small town outside Frankfurt, Germany. He is an avid traveller who taught English as a second language to see the world and learn about new cultures. Demetrios fell into the Machine Learning Operations world, and since, has interviewed the leading names around MLOps, Data Science, and ML. Since diving into the nitty-gritty of Machine Learning Operations he felt a strong calling to explore the ethical issues surrounding ML. When he is not conducting interviews you can find him making stone stacking with his daughter in the woods or playing the ukulele by the campfire.