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)
Section 1: Solving Business Challenges with Machine Learning Solution Architecture
Section 2: The Science, Tools, and Infrastructure Platform for Machine Learning
Section 3: Technical Architecture Design and Regulatory Considerations for Enterprise ML Platforms

Core components of an ML platform

An ML platform is a complex system as it consists of multiple environments for running different tasks and has complex workflow processes to orchestrate. In addition, an ML platform needs to support many roles, such as data scientists, ML engineers, infrastructure engineers, and operation team members. The following are the core components of an ML platform:

  • Data science environment: The data science environment provides data analysis tools, such as Jupyter notebooks, code repositories, and ML frameworks. Data scientists and ML engineers mainly use the data science environment to perform data analysis and data science experiments, and also to build and tune models.
  • Model training environment: The model training environment provides a separate infrastructure for different model training requirements. While data scientists and ML engineers can run small-scale model training directly inside their local Jupyter environment, they need a separate...