The Machine Learning Solutions Architect Handbook: Create machine learning platforms to run solutions in an enterprise setting

by David Ping

Build highly secure and scalable machine learning platforms to support the fast-paced adoption of machine learning solutions

Key FeaturesExplore different ML tools and frameworks to solve large-scale machine learning challenges in the cloudBuild an efficient data science environment for data exploration, model building, and model trainingLearn how to implement bias detection, privacy, and explainability in ML model developmentBook DescriptionWhen equipped with a highly scalable machine learning (ML) platform, organizations can quickly scale the delivery of ML products for faster business value realization. There is a huge demand for skilled ML solutions architects in different industries, and this handbook will help you master the design patterns, architectural considerations, and the latest technology insights you’ll need to become one.

You’ll start by understanding ML fundamentals and how ML can be applied to solve real-world business problems. Once you've explored a few leading problem-solving ML algorithms, this book will help you tackle data management and get the most out of ML libraries such as TensorFlow and PyTorch.

Using open source technology such as Kubernetes/Kubeflow to build a data science environment and ML pipelines will be covered next, before moving on to building an enterprise ML architecture using Amazon Web Services (AWS). You’ll also learn about security and governance considerations, advanced ML engineering techniques, and how to apply bias detection, explainability, and privacy in ML model development.

By the end of this book, you’ll be able to design and build an ML platform to support common use cases and architecture patterns like a true professional.

What you will learnApply ML methodologies to solve business problemsDesign a practical enterprise ML platform architectureImplement MLOps for ML workflow automationBuild an end-to-end data management architecture using AWSTrain large-scale ML models and optimize model inference latencyCreate a business application using an AI service and a custom ML modelUse AWS services to detect data and model bias and explain modelsWho this book is forThis book is for data scientists, data engineers, cloud architects, and machine learning enthusiasts who want to become machine learning solutions architects. You’ll need basic knowledge of the Python programming language, AWS, linear algebra, probability, and networking concepts before you get started with this handbook.

Table of ContentsMachine Learning and Machine Learning Solutions ArchitectureBusiness Use Cases for Machine LearningMachine Learning Algorithms Data Management for Machine LearningOpen Source Machine Learning LibrariesKubernetes Container Orchestration Infrastructure ManagementOpen Source Machine Learning PlatformsBuilding a Data Science Environment Using AWS ML ServicesBuilding an Enterprise ML Architecture with AWS ML ServicesAdvanced ML EngineeringML Governance, Bias, Explainability, and PrivacyBuilding ML Solutions with AWS AI Services

Genres: Technical

440 pages, Kindle Edition

Published January 21, 2022

Reviews

[Mark Torres · Rating 4 out of 5]

Solid book, good primer on the wide breadth of topics related to implementing machine learning solutions in production. Very thorough coverage, spanning from data management, ML training/inference, monitoring and evals, all the way to orchestration and container management. Great handbook for practitioners who have a solid understanding of specific components of ML, but want a more end-to-end overview of the process coupled with well-written hands-on examples. Also useful for those coming from a more academic background, who train ML models but don't productionize them, as well as those coming from enterprise, who generally have specialized silos of teams to handle specific steps of the ML development process. Highly recommend!