Practical Deep Learning at Scale with MLflow | Bridge the gap between offline experimentation and online production

Yong Liu

ISBN 10: 1803241330 / ISBN 13: 9781803241333

Editore: Packt Publishing, 2022

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Practical Deep Learning at Scale with MLflow | Bridge the gap between offline experimentation and online production | Yong Liu | Taschenbuch | Kartoniert / Broschiert | Englisch | 2022 | Packt Publishing | EAN 9781803241333 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

Codice articolo 122465722

Dati bibliografici

• Titolo: Practical Deep Learning at Scale with MLflow | Bridge the gap between offline experimentation and online production
• Editore: Packt Publishing
• Data di pubblicazione: 2022
• Lingua: Inglese
• ISBN 10: 1803241330
• ISBN 13: 9781803241333
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 235 L x 191 A x 16 P (millimetri) 235 millimetri di larghezza per 191 millimetri di altezza per 16 millimetri di profondità
• Peso: 543 grammi
• Cataloghi dei venditori:
  • Bücher

Descrizione articolo

Riassunto

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.

Table of Contents

1. Deep Learning Life Cycle and MLOps Challenges
2. Getting Started with MLflow for Deep Learning
3. Tracking Models, Parameters, and Metrics
4. Tracking Code and Data Versioning
5. Running DL Pipelines in Different Environments
6. Running Hyperparameter Tuning at Scale
7. Multi-Step Deep Learning Inference Pipeline
8. Deploying a DL Inference Pipeline at Scale
9. Fundamentals of Deep Learning Explainability
10. Implementing DL Explainability with MLflow

Informazioni sull'autore

Yong Liu has been working in big data science, machine learning, and optimization since his doctoral student years at the University of Illinois at Urbana-Champaign (UIUC) and later as a senior research scientist and principal investigator at the National Center for Supercomputing Applications (NCSA), where he led data science R&D projects funded by the National Science Foundation and Microsoft Research. He then joined Microsoft and AI/ML start-ups in the industry. He has shipped ML and DL models to production and has been a speaker at the Spark/Data+AI summit and NLP summit. He has recently published peer-reviewed papers on deep learning, linked data, and knowledge-infused learning at various ACM/IEEE conferences and journals.