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

Summary

In this chapter, you learned about the core architecture components of a typical ML platform and their capabilities. We also discussed various open source technologies such as Kubeflow, MLflow, TensorFlow Serving, Seldon Core, Apache Airflow, and Kubeflow Pipelines. You have also built a data science environment using Kubeflow notebooks, tracked experiments and models using MLflow, and deployed your model using Seldon Core. And finally, you learned how to automate multiple ML workflow steps using Kubeflow Pipelines, including data processing, model training, and model deployment. While these open source technologies provide features for building potentially sophisticated ML platforms, it still takes significant engineering effort and know-how to build and maintain such environments, especially for large-scale ML platforms. In the next chapter, we will start looking into fully managed, purpose-built ML solutions for building and operating ML environments.