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Practical MLOps

Published by O'Reilly Media, Inc.

Beginner to intermediate content levelBeginner to intermediate

Operationalizing machine learning models

Getting your models into production is the fundamental challenge of machine learning. The faster you deliver a machine learning system that works, the faster you can focus on the business problems you're trying to crack. MLOps offers a set of proven principles aimed at solving this problem in a reliable and automated way.

Join expert Noah Gift to explore MLOps (and how it differs from DevOps) and learn how to put it into practice to operationalize your machine learning models. You’ll build a foundation in MLOps tools and methods (along with AutoML and monitoring and logging) and learn how to implement them in AWS, Microsoft Azure, and Google Cloud.

What you’ll learn and how you can apply it

By the end of this live online course, you’ll understand:

  • What MLOps is
  • How to get started using MLOps
  • Best practices for MLOps

And you’ll be able to:

  • Perform continuous integration for Python ML projects
  • Use the AWS cloud for MLOps development
  • Create containerized workflows for MLOps
  • Create Flask and CLI services for Python ML projects

This live event is for you because...

  • You’re a data scientist, software engineer, or Python programmer.
  • You work with machine learning, data, or software.
  • You want to become an MLOps practitioner.

Prerequisites

  • An AWS account is recommended to take part in exercises
  • Basic knowledge of Linux, Python, and data science

Recommended preparation:

Recommended follow-up:

Schedule

The time frames are only estimates and may vary according to how the class is progressing.

Getting started with MLOps (60 minutes)

  • Group discussion: Your experience with MLOps and cloud computing
  • Presentation: Cloud-based development environments for MLOps; cloud-based continuous integration
  • Hands-on exercises: Set up an AWS Cloud9 environment; set up GitHub and Git; set up AWS CodeBuild
  • Q&A
  • Break

Building a containerized MLOps command-line tool (60 minutes)

  • Group discussion: Your experience with containers and command-line tools
  • Presentation: Docker overview; Docker containers versus virtual machines; common issues running a Docker container
  • Hands-on exercises: Use a Docker container from Docker Hub; extend a Docker container; build a Python Click command-line tool in a container
  • Q&A
  • Break

Building containerized ML web microservice applications (60 minutes)

  • Group discussion: Your experience with container registries and running containers
  • Presentation: Flask microservice overview
  • Hands-on exercises: Build and run a Flask Docker scikit-learn prediction container in AWS Cloud9; verify inference response from Flask application using utilities you build yourself
  • Q&A
  • Break

Continuous delivery containerized app (60 minutes)

  • Group discussion: Your experience with building containers automatically
  • Hands-on exercises: Deploy a Docker scikit-learn prediction container to Docker Hub and Amazon Elastic Container Registry; deploy Flask ML microservice container via AWS App Runner in a container-as-a-service workflow
  • Q&A

Your Instructor

  • Noah Gift

    Noah Gift is lecturer and consultant in both the UC Davis Graduate School of Management’s MSBA program and Northwestern’s graduate data science program, MSDS, where he teaches and designs graduate machine learning, AI, and data science courses and consults on machine learning and cloud architecture for students and faculty. These responsibilities include leading a multicloud certification initiative for students. He’s the author of close to 100 technical publications, including two books on subjects ranging from cloud machine learning to DevOps. Noah has approximately 20 years’ experience programming in Python. He’s a Python Software Foundation Fellow, an AWS Subject Matter Expert (SME) on machine learning, an AWS Certified Solutions Architect and AWS Academy Accredited Instructor, a Google Certified Professional Cloud Architect, and a Microsoft MTA on Python. Over his career, he’s served in roles ranging from CTO, general manager, and consulting CTO to cloud architect at companies including ABC, Caltech, Sony Imageworks, Disney Feature Animation, Weta Digital, AT&T, Turner Studios, and Linden Lab. In the last 10 years, he’s been responsible for shipping many new products that generated millions of dollars of revenue and had global scale. Currently, he’s consulting startups and other companies. Noah holds an MBA from UC Davis, an MS in computer information systems from Cal State Los Angeles, and a BS in nutritional science from Cal Poly San Luis Obispo.

Skill covered

MLOps