Practical Deep Learning at Scale with MLflow

Practical Deep Learning at Scale with MLflow

eBook Details:

  • Paperback: 288 pages
  • Publisher: WOW! eBook (July 8, 2022)
  • Language: English
  • ISBN-10: 1803241330
  • ISBN-13: 978-1803241333

eBook Description:

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

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.

  • 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

By the end of this Practical Deep Learning at Scale with MLflow 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.

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