Session Title

Doing MLOps

Abstract

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 talk 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 session gives you a head start. Following are the takeaways from this session;

• 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

Bio

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.