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

What are AI services?

AI services are pre-built fully managed services that perform a particular set of ML tasks out of the box, such as facial analysis or text analysis. The primary target users for AI services are application developers who want to build AI applications without the need to build ML models from scratch. In contrast, the target audiences for ML platforms are data scientists and ML engineers, who need to go through the full ML life cycle to build and deploy ML models. For an organization, AI services mainly solve the following key challenges:

  • Lack of high-quality training data for ML model development: To train high-quality models, you need a large amount of high-quality curated data. For many organizations, data poses many challenges in data sourcing, data engineering, and data labeling.
  • Lack of data science skills for building and deploying custom ML models: Data science and ML engineering skills are scarce in the market and expensive to acquire.
  • ...