Book Image

The Machine Learning Solutions Architect Handbook

By : David Ping
Book Image

The Machine Learning Solutions Architect Handbook

By: David Ping

Overview of this book

When 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.
Table of Contents (17 chapters)
1
Section 1: Solving Business Challenges with Machine Learning Solution Architecture
4
Section 2: The Science, Tools, and Infrastructure Platform for Machine Learning
9
Section 3: Technical Architecture Design and Regulatory Considerations for Enterprise ML Platforms

Model hosting environment deep dive

An enterprise-grade model hosting environment needs to support a broad range of ML frameworks in a secure, performant, and scalable way. It should come with a list of pre-built inference engines that can serve common models out of the box behind a RESTful API or via the gRPC protocol. It also needs to provide flexibility to host custom-built inference engines for unique requirements. Users should also have access to different hardware devices, such as CPU, GPU, and purpose-built chips, for the different inference needs.

Some model inference patterns demand more complex inference graphs, such as traffic split, request transformations, or model ensemble support. A model hosting environment can provide this capability as an out-of-the-box feature or provide technology options for building custom inference graphs. Other common model hosting capabilities include concept drift detection and model performance drift detection. Concept drift occurs when...