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Machine Learning Engineering with MLflow

You're reading from   Machine Learning Engineering with MLflow Manage the end-to-end machine learning life cycle with MLflow

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Product type Paperback
Published in Aug 2021
Publisher Packt
ISBN-13 9781800560796
Length 248 pages
Edition 1st Edition
Tools
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Author (1):
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Natu Lauchande Natu Lauchande
Author Profile Icon Natu Lauchande
Natu Lauchande
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Table of Contents (18) Chapters Close

Preface 1. Section 1: Problem Framing and Introductions
2. Chapter 1: Introducing MLflow FREE CHAPTER 3. Chapter 2: Your Machine Learning Project 4. Section 2: Model Development and Experimentation
5. Chapter 3: Your Data Science Workbench 6. Chapter 4: Experiment Management in MLflow 7. Chapter 5: Managing Models with MLflow 8. Section 3: Machine Learning in Production
9. Chapter 6: Introducing ML Systems Architecture 10. Chapter 7: Data and Feature Management 11. Chapter 8: Training Models with MLflow 12. Chapter 9: Deployment and Inference with MLflow 13. Section 4: Advanced Topics
14. Chapter 10: Scaling Up Your Machine Learning Workflow 15. Chapter 11: Performance Monitoring 16. Chapter 12: Advanced Topics with MLflow 17. Other Books You May Enjoy

Understanding models in MLflow

On the MLflow platform, you have two main components available to manage models:

  • Models: This module manages the format, library, and standards enforcement module on the platform. It supports a variety of the most used machine learning models: sklearn, XGBoost, TensorFlow, H20, fastai, and others. It has features to manage output and input schemas of models and to facilitate deployment.
  • Model Registry: This module handles a model life cycle, from registering and tagging model metadata so it can be retrieved by relevant systems. It supports models in different states, for instance, live development, testing, and production.

An MLflow model is at its core a packaging format for models. The main goal of MLflow model packaging is to decouple the model type from the environment that executes the model. A good analogy of an MLflow model is that it’s a bit like a Dockerfile for a model, where you describe metadata of the model, and...

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