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Machine Learning Engineering with MLflow
Machine Learning Engineering with MLflow

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

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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

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Aug 27, 2021
Length: 248 pages
Edition : 1st
Language : English
ISBN-13 : 9781800561694
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Product Details

Publication date : Aug 27, 2021
Length: 248 pages
Edition : 1st
Language : English
ISBN-13 : 9781800561694
Category :
Tools :

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Frequently bought together


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Total $ 147.97
Engineering MLOps
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Machine Learning Engineering with MLflow
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Total $ 147.97 Stars icon
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Customer reviews

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Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.2
(16 Ratings)
5 star 50%
4 star 37.5%
3 star 0%
2 star 6.3%
1 star 6.3%
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Rahul Z Sep 30, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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 Amazon
Luis Felipe Yepez Barrios Aug 27, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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.
Amazon Verified review Amazon
yl790 Aug 27, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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.
Amazon Verified review Amazon
Hitesh Hinduja Sep 29, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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
Amazon Verified review Amazon
IntegralBill Sep 11, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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.
Amazon Verified review Amazon
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