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 DescriptionThe 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 forThis 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.
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