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Key benefits

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

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.

Who is this book 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.

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

What do you get with a Packt Subscription?

Free for first 7 days. $15.99 p/m after that. Cancel any time!

Unlimited ad-free access to the largest independent learning library in tech. Access this title and thousands more!

50+ new titles added per month, including many first-to-market concepts and exclusive early access to books as they are being written.

Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.

Thousands of reference materials covering every tech concept you need to stay up to date.

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Frequently bought together

Practical Deep Learning at Scale with MLflow

$46.99

$51.99

Machine Learning Engineering with MLflow

$43.99

Total $ 142.97

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MYLiang Jul 08, 2022

This book gives a good introduction to the full life cycle of deep learning development using MLflow. It's easy to read, and also very practical with all the code provided. The book illustrates the challenges in every step of developing and productionizing a deep learning model, and how you can deal with these challenges with MLflow, at scale and with better explainability. Recommended for everyone who works with data and want a better management of your models or projects with MLflow. You'll benefit from and be inspired by different sections of this book.

Amazon Verified review

Andrew J. Brooks Jul 25, 2022

The beauty of this work is that it threads together the most important concepts facing ML practitioners today AND actionable recommendations for tooling from the modern tech stack AND working code. There are tutorials and blog posts out there that touch on bits and pieces of these, but they are fragmented with many holes in between. The in-depth guide provided within this work connects the intuition and full ML lifecycle to these concepts in a way that the field desperately needs.Having worked directly with Yong for several years on many of these topics, I can attest that this book is not just a tome of facts and tutorials, but a trove of wisdom developed through years of experience and experimentation. For example, even seasoned ML Practitioners will likely find new insights and patterns in Chapter 7 for how to elegantly connect model and business and pre/post-processing logic that is often disjointed. Or the landscape of options and considerations for serving MLFlow models in Chapter 8. The techniques covered in this text are highly practical for anyone shipping industry-grade ML, but rarely covered with this much depth (or at all) elsewhere on the web.

QAM Chen Jul 09, 2022

Databricks with mlflow server is a very well setup environment for MLOps. The book gives you all knowledge you need for model building and MLOps. You can use it for traditional ML training or NLP transfer learning model building. I learned a lot from the book especially model deployment and model hyper-params tune.Overall: Reading the book and you can learn ML lifecycle with databricks and MLflow which is very important for model to production.

Chelsea Zhu Jul 19, 2022

MLOps is an emerging field of ML which aims to deploy and maintain machine learning models reliably and efficiently. Industries realize the importance of it, but the resource in this area is scarce and knowledge is scattered. This book covers the most important areas of MLOps with examples of deep learning models using MLFlow - ML pipelines, model explainability, model experimentation, and tracking, code and data versioning, model deployment etc. The author did an excellent job to combine all important knowledge of MLOps in a structural and practical way. Even for a veteran in ML area, you still can learn a lot from this book. Highly recommend it to everyone who is going to build ML systems and wants to follow the best practice in MLOps.

Yongguo Mei Jul 15, 2022

This book covers every stages of deep learning model development from feature data preparation, model training, to deployment, and explainability. The author has near 20 years industrial experience from multiple companies. You can follow through the code examples in each chapter and learn each topic conveniently as MLflow is a popular ML platform. This book is relatively short, but provides a rich set of real world ML problems and solutions. Whether you are new to this area or an expert ML scientist, you can benefit from reading this book.

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

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

What do you get with a Packt Subscription?

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Description

Who is this book for?

What you will learn

Product Details

What do you get with a Packt Subscription?

Product Details

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