Practical Deep Learning at Scale with MLflow

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Practical Deep Learning at Scale with MLflow

Practical Deep Learning at Scale with MLflow


This eBook includes

Formats : PDF (Read Only)

Pages : 288

ISBN : 9781803242224

Language : English

About the Book

Book description

Train, test, run, track, store, tune, deploy, and explain provenance-aware deep learning models and pipelines at scale with reproducibility using MLflow

Key Features

  • Focus on deep learning models and MLflow to develop practical business AI solutions at scale
  • Ship deep learning pipelines from experimentation to production with provenance tracking
  • Learn to train, run, tune and deploy deep learning pipelines with explainability and reproducibility

Book Description

The book starts with an overview of the deep learning (DL) life cycle and the emerging Machine Learning Ops (MLOps) field, providing a clear picture of the four pillars of deep learning: data, model, code, and explainability and the role of MLflow in these areas.

From there onward, it guides you step by step in understanding the concept of MLflow experiments and usage patterns, using MLflow as a unified framework to track DL data, code and pipelines, models, parameters, and metrics at scale. You’ll also tackle running DL pipelines in a distributed execution environment with reproducibility and provenance tracking, and tuning DL models through hyperparameter optimization (HPO) with Ray Tune, Optuna, and HyperBand. As you progress, you’ll learn how to build a multi-step DL inference pipeline with preprocessing and postprocessing steps, deploy a DL inference pipeline for production using Ray Serve and AWS SageMaker, and finally create a DL explanation as a service (EaaS) using the popular Shapley Additive Explanations (SHAP) toolbox.

By the end of this book, you’ll have built the foundation and gained the hands-on experience you need to develop a DL pipeline solution from initial offline experimentation to final deployment and production, all within a reproducible and open source framework.

What you will learn

  • Understand MLOps and deep learning life cycle development
  • Track deep learning models, code, data, parameters, and metrics
  • Build, deploy, and run deep learning model pipelines anywhere
  • Run hyperparameter optimization at scale to tune deep learning models
  • Build production-grade multi-step deep learning inference pipelines
  • Implement scalable deep learning explainability as a service
  • Deploy deep learning batch and streaming inference services
  • Ship practical NLP solutions from experimentation to production

Who this book is for

This book is for machine learning practitioners including data scientists, data engineers, ML engineers, and scientists who want to build scalable full life cycle deep learning pipelines with reproducibility and provenance tracking using MLflow. A basic understanding of data science and machine learning is necessary to grasp the concepts presented in this book.

Practical Deep Learning at Scale with MLflow

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Author Details

Packt Publishing

Packt Publishing

Founded in 2004 in Birmingham, UK, Packt's mission is to help the world put software to work in new ways, through the delivery of effective learning and information services to IT professionals.

Working towards that vision, we have published over 6,500 books and videos so far, providing IT professionals with the actionable knowledge they need to get the job done - whether that's specific learning on an emerging technology or optimizing key skills in more established tools.

As part of our mission, we have also awarded over $1,000,000 through our Open Source Project Royalty scheme, helping numerous projects become household names along the way.

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