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

Open source technologies for building ML platforms

While it is possible to run different ML tasks by creating and deploying different standalone ML containers in a Kubernetes cluster, this can become quite complex to manage when you have to do this at scale for a large number of users and ML workloads. This is where open source technologies such as Kubeflow, MLflow, Seldon Core, GitHub, and Airflow come in. Next, let's take a closer look at how these open source technologies can be used for building data science environments, model training services, model inference services, and ML workflow automation.

Using Kubeflow for data science environments

Kubeflow is a Kubernetes-based, open source ML platform that provides a number of ML-specific components. Kubeflow runs on top of Kubernetes and provides the following capabilities:

  • A central UI dashboard
  • A Jupyter notebook server for code authoring and model building
  • A Kubeflow pipeline for ML pipeline orchestration...