Machine Learning Engineering with MLflow

Machine Learning Engineering with MLflow

eBook Details:

  • Paperback: 248 pages
  • Publisher: WOW! eBook (August 27, 2021)
  • Language:¬†English
  • ISBN-10: 1800560796
  • ISBN-13: 978-1800560796

eBook Description:

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

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.

  • 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

By the end of this Machine Learning Engineering with MLflow book, you will be able to produce and deploy reliable machine learning algorithms using MLflow in multiple environments.

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