Practical Deep Learning at Scale with 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.
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.

Type
ebook
Category
Data
publication date
2022-07-08
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

no of pages
288
duration
576
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
approach
Complete with step-by-step explanations of essential concepts, practical examples, and project-based hands-on learning, you will master deep learning full life-cycle development by implementing scalable and reproducible end-to-end AI solutions starting from local offline experimentation to online production in the cloud using best practices with MLflow and state-of-the-art deep learning frameworks.
audience
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.
meta description
Train, test, run, track, store, tune, deploy, and explain provenance-aware deep learning models and pipelines at scale with reproducibility using MLflow
short description
This book teaches you how to use MLflow to support deep learning life cycle development with step-by-step instructions. You’ll build NLP solutions from scratch and implement scalable deep learning pipelines from initial offline experimentation to production with coherent provenance tracking for code, data, models, and explainability.
subtitle
Bridge the gap between offline experimentation and online production
keywords
mlflow, deep learning, experimentation, model pipelines, data versioning, training, testing, tuning, deployment, NLP
Product ISBN
9781803241333