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

Adopting MLOps for ML workflows

Similar to the DevOps practice, which has been widely adopted for the traditional software development and deployment process, the MLOps practice is intended to streamline the building and deployment processes of ML pipelines and improve the collaborations between data scientists/ML engineers, data engineering, and the operations team. Specifically, an MLOps practice is intended to deliver the following main benefits in an end-to-end ML life cycle:

  • Process consistency: The MLOps practice aims to create consistency in the ML model building and deployment process. A consistent process improves the efficiency of the ML workflow and ensures a high degree of certainty in the input and output of the ML workflow.
  • Tooling and process reusability: One of the core objectives of the MLOps practice is to create reusable technology tooling and templates for faster adoption and deployment of new ML use cases. These can include common tools such as code...