Engineering MLOps
Packt Publishing Limited (Verlag)
978-1-80323-732-9 (ISBN)
Key Features
Design a robust and scalable microservice and API for test and production environments
Become well versed in MLOps on Azure and open-source tools, including MLFlow, KubeFlow, Docker, Kubernetes, Apache Airflow/Flink/Spark, GitHub
Implement ML, CI/CD, continuous training, ML monitoring pipelines within your organization
Learn automated Machine Learning systems and ML engineering
Book DescriptionGetting machine learning (ML) models into production continues to remain challenging using traditional software development methods. This book highlights the changing trends of software development over time and solves the problem of integrating ML with traditional software using MLOps.
In this new edition of Engineering MLOps, Emmanuel Raj demystifies MLOps to equip you with the skills needed to build your own MLOps pipelines using -of-the-art tools (MLFlow, DVC, KubeFlow, Locust.io, Docker, Kubernetes, Apache Spark, to name a few) and platforms. You will start by learning the essentials of ML engineering and build ML pipelines to train and deploy models. The book then covers how to implement an MLOps solution for a real-life business problem using Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP), as well as cloud agnostic tools. You'll also understand how to build continuous integration/deployment (CI/CD) and continuous delivery pipelines to build, test, deploy, and monitor your models.
By the end of the book, you will become proficient at building, deploying, and monitoring any ML model with the MLOps process using any tool or platform.What you will learn
Deploy ML models from the lab environment to production and customize solutions to fit your infrastructure and on-premises needs
Run ML models on Azure and on devices, including mobile phones and specialized hardware
Design a streaming service for inference in real-time with Apache Flink
Explore deployment techniques: A/B testing, phased rollouts, and shadow deployments
Formulate data governance strategies and pipelines for ML training and deployment
Who this book is forThis MLOps book is for data scientists, software engineers, DevOps engineers, machine learning engineers, and IT managers/strategists (such as CTOs and Product Managers). Business leaders in tech companies are also bound to find this book useful. Basic knowledge of machine learning as well as Python programming language is expected.
Emmanuel Raj is a Finland-based Senior Machine Learning Engineer with 6+ years of industry experience. He is also a Machine Learning Engineer at TietoEvry and a Member of the European AI Alliance at the European Commission. He is passionate about democratizing AI and bringing research and academia to industry. He holds a Master of Engineering degree in Big Data Analytics from Arcada University of Applied Sciences. He has a keen interest in R&D in technologies such as Edge AI, Blockchain, NLP, MLOps and Robotics. He believes "the best way to learn is to teach", he is passionate about sharing and learning new technologies with others.
Table of Contents
Introduction to MLOps
MLOps for Business
Basics of ML Engineering
Characterizing your Machine Learning Problem for MLOps
Machine Learning Pipelines
Model Evaluation and Packaging
Deploying your models as a batch or live endpoint
Deploying your models as a streaming service
Building robust CI and CD pipelines
API and microservice Management
Essentials of testing release
Essentials of production release
Key principles for monitoring your ML system
Model serving and metrics selection
Continuous delivery and continuous monitoring
Orchestrating ML pipelines in Azure Synapse
Governance of ML Solutions
Building Machine Learning Models
Deploying Machine Learning Models
MLOps Inspiration and Use Cases
Building a Technical Portfolio for MLOps
Monitoring and Governance
| Erscheinungsdatum | 10.02.2024 |
|---|---|
| Verlagsort | Birmingham |
| Sprache | englisch |
| Maße | 191 x 235 mm |
| Themenwelt | Mathematik / Informatik ► Informatik ► Datenbanken |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Mathematik / Informatik ► Informatik ► Web / Internet | |
| ISBN-10 | 1-80323-732-5 / 1803237325 |
| ISBN-13 | 978-1-80323-732-9 / 9781803237329 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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