Packt - Machine Learning Engineering With Mlflow Manage The End To End Machine Learning Lifecycle...

  Machine Learning Engineering With MLflow - Manage The End-To-End Machine
  Learning Lifecycle With MLflow
   LANGUAGE: English     ░   ISBN: 9781800560796      ░   REMOVED: DRM
https://www.worldcat.org/search?q=bn%3A9781800560796&fq=x0%3Abook+%2B+x4%3Adigit
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  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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