Zustand: Sehr gut | Sprache: Englisch | Produktart: B�cher | Get up and running, and productive in no time with MLflow using the most effective machine learning engineering approach Key Features: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 Book 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. 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 Who this book is 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.

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Detalles bibliogr�ficos

T�tulo

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

Editor

Packt Publishing

Fecha de publicaci�n

2021

Idioma

Ingl�s

ISBN 10

1800560796

ISBN 13

9781800560796

Encuadernaci�n

Encuadernaci�n de tapa blanda

Estado

Sehr gut

Cat�logos de vendedores

B�cher

Acerca de

Sinopsis

Get up and running, and productive in no time with MLflow using the most effective machine learning engineering approach

Key Features

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

Book 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.

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

Who this book is 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.

Introducing MLflow
Your Machine Learning Project
Your Data Science Workbench
Experiment Management in MLflow
Managing Models with MLflow
Introducing ML Systems Architecture
Data and Feature Management
Training Models with MLflow
Deployment and Inference with MLflow
Scaling Up Your Machine Learning Workflow
Performance Monitoring
Advanced Topics with MLflow

Acerca del autor

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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