This is a public repo where code samples are stored for the book Practical MLOps.

Last update: Dec 31, 2022

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Machine Learning python learning machine-learning cloud machine oreilly-books practical-mlops

Overview

Practical MLOps, an O'Reilly Book

This is a public repo where code samples are stored for the book Practical MLOps.

Tentative Outline

Chapter 1: Introduction to MLOps

Source Code Chapter 1:

Multi-cloud Github Actions Demo

Chapter 2: MLOps Foundations

Source Code Chapter 2:

Chapter 3: Machine Learning Deployment In Production Strategies

Source Code Chapter 3:

Chapter 4: Continuous Delivery for Machine Learning Models

Source Code Chapter 4:

Chapter 5: AutoML

Source Code Chapter 5:

Chapter 6: Monitoring and Logging for Machine Learning

Source Code Chapter 6:

Chapter 7: MLOps for AWS

Source Code Chapter 7:

Chapter 8: MLOps for Azure

Source Code Chapter 8:

Chapter 9: MLOps for GCP

Source Code Chapter 9:

Chapter 10: Machine Learning Interoperability

Source Code Chapter 10:

Chapter 11: Building MLOps command-line tools

Source Code Chapter 11:

Chapter 12: Machine Learning Engineering and MLOps Case Studies

Source Code Chapter 12:

Community Recipes

This section includes "community" recipes. Many "may" be included in the book if timing works out.

References

Next Steps: Take Coursera MLOps Course

Owner

Pragmatic AI Labs

Experts on cloud native Machine Learning and AI Solutions. One million trained by 2021. #onemillion2021

Pragmatic AI Labs

GitHub Repository

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