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Key benefits

Explore machine learning workflows for stating ML problems in a concise and clear manner using MLflow

Use MLflow to iteratively develop a ML model and manage it

Discover and work with the features available in MLflow to seamlessly take a model from the development phase to a production environment

Description

MLflow is a platform for the machine learning life cycle that enables structured development and iteration of machine learning models and a seamless transition into scalable production environments. This book will take you through the different features of MLflow and how you can implement them in your ML project. You will begin by framing an ML problem and then transform your solution with MLflow, adding a workbench environment, training infrastructure, data management, model management, experimentation, and state-of-the-art ML deployment techniques on the cloud and premises. The book also explores techniques to scale up your workflow as well as performance monitoring techniques. As you progress, you’ll discover how to create an operational dashboard to manage machine learning systems. Later, you will learn how you can use MLflow in the AutoML, anomaly detection, and deep learning context with the help of use cases. In addition to this, you will understand how to use machine learning platforms for local development as well as for cloud and managed environments. This book will also show you how to use MLflow in non-Python-based languages such as R and Java, along with covering approaches to extend MLflow with Plugins. By the end of this machine learning book, you will be able to produce and deploy reliable machine learning algorithms using MLflow in multiple environments.

Who is this book for?

This book is for data scientists, machine learning engineers, and data engineers who want to gain hands-on machine learning engineering experience and learn how they can manage an end-to-end machine learning life cycle with the help of MLflow. Intermediate-level knowledge of the Python programming language is expected.

What you will learn

Develop your machine learning project locally with MLflow's different features

Set up a centralized MLflow tracking server to manage multiple MLflow experiments

Create a model life cycle with MLflow by creating custom models

Use feature streams to log model results with MLflow

Develop the complete training pipeline infrastructure using MLflow features

Set up an inference-based API pipeline and batch pipeline in MLflow

Scale large volumes of data by integrating MLflow with high-performance big data libraries

What do you get with a Packt Subscription?

Free for first 7 days. $15.99 p/m after that. Cancel any time!

Unlimited ad-free access to the largest independent learning library in tech. Access this title and thousands more!

50+ new titles added per month, including many first-to-market concepts and exclusive early access to books as they are being written.

Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.

Thousands of reference materials covering every tech concept you need to stay up to date.

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Frequently bought together

$48.99

Machine Learning for Time-Series with Python

$54.99

Machine Learning Engineering with MLflow

$43.99

Total $ 147.97

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Rahul Z Sep 30, 2021

As an AI/ML practitioner, I use MLFlow as an integral part of my day-to-day work coupled with Azure Databricks. This book helped me level up my understanding and comfort with the overall platform.- I can use MLFlow far more efficiently, do more with it since reading this book as I got introduced to the additional functionalities- Well documented steps, with screenshots and diagrams helped me keep track and follow along- Conventions being used throughout are documented at the beginning: which is highly appreciated and sets the tone nicely for professionals like me- Book ends with a nice chapter about advance use cases, which I definitely want to explore going forwardAll in all, great bang for buck here in the US: you can't go wrong with this purchase. However, be sure to review the description and key features being covered in the listing to ensure this copy matches your expectations going in.

Amazon Verified review

Luis Felipe Yepez Barrios Aug 27, 2021

This book introduce you to MLflow open source framework to manage all the machine learning models/projects life cycle in your organization, from tracking experiments, projects, models up to serving locally and in AWS sagemaker.It is organize in a way that you can start to understand Mlflow in detail, through several walkthrough hands-on exercises highlighting in every step all the aspects from Problem framing, Model development, experimentation, Productionalize, Monitor and also include topics developed in details like scaling machine learning workflow with Mlflow with Apache Spark, Nvidia RAPIDS, Ray platform, how to integrate Mlflow with java and R with clear and thoughtfully examples.This book provide a proper MLOps implementation streamlines the process of developing and deploying ML models, and outline several considerations that will help ensure ML applications make it to production and run smoothly. At the end of the day, that’s what it takes for a model to provide business value.As a summary is not just a great book of Machine Learning explaining how to manage the life cycle of the model/project also provide a lot of good practices not related exclusively to Mlflow, those cover all the different stages of the project development.

yl790 Aug 27, 2021

I think this book is a fairly comprehensive guide to MLflow. It covers introductory topics such as what MLflow is, why it was created, and how it can be used to manage ML lifecycle, and also explores areas such as production-readiness, and advanced use cases involving GPU, Databricks runtime, etc which are of great interest to MLflow practitioners. I also liked how the book included Docker-based recipes, which are great for beginners to get some hands-on experience with MLflow within an isolated Docker container without interfering with whatever Python site packages they might have in their host environments.

Hitesh Hinduja Sep 29, 2022

Interesting read. Very few books exist today for MLFlow and this being one of them. The book requires some pre requisite idea of MLflow but is a good technical book to read

IntegralBill Sep 11, 2021

This book gets straight to the point, being setting up your end-to-end ML pipeline. He doesn't spend much time going over machine learning in detail; so if you don't have a background in ML prior to approaching this book I would recommend getting familiar with that first. If you're looking to learn about MLOps, creating ML-pipelines, this is an excellent book!! This book is not platform dependent (can use for any major cloud platform). I'm currently using it for my projects and this book helped me get up and running quickly!! Please see my video review for more.

Machine Learning Engineering with MLflow: Manage the end-to-end machine learning life cycle with MLflow

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Machine Learning Engineering with MLflow: Manage the end-to-end machine learning life cycle with MLflow

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