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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

Machine Learning

Author (1):

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

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Technical requirements

For this chapter, you will need the following prerequisites:

The latest version of Docker installed on your machine. If you don't already have it installed, please follow the instructions at https://docs.docker.com/get-docker/.
Access to a Bash terminal (Linux or Windows).
Access to a browser.
Python 3.5+ installed.
MLflow installed locally as described in Chapter 1, Introducing MLflow.

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Authors (1)

Natu Lauchande

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.

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