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Machine Learning Engineering with MLflow

You're reading from  Machine Learning Engineering with MLflow

Product type Book
Published in Aug 2021
Publisher Packt
ISBN-13 9781800560796
Pages 248 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Natu Lauchande Natu Lauchande
Profile icon Natu Lauchande
LinkedIn
Natu Lauchande is a principal data engineer in the fintech space currently tackling problems at the intersection of machine learning, data engineering, and distributed systems. He has worked in diverse industries, including biomedical/pharma research, cloud, fintech, and e-commerce/mobile. Along the way, he had the opportunity to be granted a patent (as co-inventor) in distributed systems, publish in a top academic journal, and contribute to open source software. He has also been very active as a speaker at machine learning/tech conferences and meetups.
See other products by Natu Lauchande

Table of Contents (18) Chapters

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

Managing the model development life cycle

Managing the model life cycle is quite important when working in a team of more than one model developer. It’s quite usual for multiple model developers to try different models within the same project, and having a reviewer decide on the model that ends up going to production is quite important:

Figure 5.13 – Example of a model development life cycle

A model in its life cycle can undergo the following stages if using a life cycle similar to the one represented in Figure 5.13:

  • Development: The state where the model developer is still exploring and trying out different approaches and is still trying to find a reasonable solution to their machine learning problem.
  • Staging: The state where the model can be tested automatically with production-type traffic.
  • Production: When the model is ready to handle real-life production traffic.
  • Archive: When the model no longer serves the business...
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Authors (1)

Profile icon Natu Lauchande
LinkedIn
Natu Lauchande is a principal data engineer in the fintech space currently tackling problems at the intersection of machine learning, data engineering, and distributed systems. He has worked in diverse industries, including biomedical/pharma research, cloud, fintech, and e-commerce/mobile. Along the way, he had the opportunity to be granted a patent (as co-inventor) in distributed systems, publish in a top academic journal, and contribute to open source software. He has also been very active as a speaker at machine learning/tech conferences and meetups.
Read more
See other products by Natu Lauchande

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