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

Model training environment

Within an enterprise, a model training environment is a controlled environment with well-defined processes and policies on how it is used and who can use them. Normally, it should be an automated environment that's managed by an MLOps team, though it can be self-service enabled for direct usage by data scientists.

Automated model training and tuning are the core capabilities of the model training environment. To support a broad range of use cases, a model training environment needs to support different ML and deep learning frameworks, training patterns (such as single-node and distributed training), and hardware (different CPUs and GPUs).

The model training environment manages the life cycle of the model training process. This can include authentication and authorization, infrastructure provisioning, data movement, data preprocessing, ML library deployment, training loop management and monitoring, model persistence and registry, training job management...