The Machine Learning Solutions Architect Handbook: Create machine learning platforms to run solutions in an enterprise setting

Ping, David



Build highly secure and scalable machine learning platforms to support the fast-paced adoption of machine learning solutions


Key Features:

  • Explore different ML tools and frameworks to solve large-scale machine learning challenges in the cloud
  • Build an efficient data science environment for data exploration, model building, and model training
  • Learn how to implement bias detection, privacy, and explainability in ML model development


Book Description:

With a highly scalable machine learning (ML) platform, organizations can quickly scale the delivery of ML products for faster business value realization, so there is a huge demand for skilled ML solutions architects in different industries. This hands-on ML book takes you through the design patterns, architectural considerations, and the latest technology that you need to know to become a successful ML solutions architect.

You'll start by understanding ML fundamentals and how ML can be applied to real-world business problems. Once you've explored some of the leading ML algorithms for solving different types of problems, the book will help you get to grips with data management and using ML libraries such as TensorFlow and PyTorch. You'll learn how to use open source technology such as Kubernetes/Kubeflow to build a data science environment and ML pipelines and then advance to building an enterprise ML architecture using Amazon Web Services (AWS) services. You'll then cover security and governance considerations, advanced ML engineering techniques, and how to apply bias detection, explainability, and privacy in ML model development. Finally, you'll get acquainted with AWS AI services and their applications in real-world use cases.

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.


What You Will Learn:

  • Apply ML methodologies to solve business problems
  • Design a practical enterprise ML platform architecture
  • Implement MLOps for ML workflow automation
  • Build an end-to-end data management architecture using AWS
  • Train large-scale ML models and optimize model inference latency
  • Create a business application using an AI service and a custom ML model
  • Use AWS services to detect data and model bias and explain models


Who this book is for:

This book is for data scientists, data engineers, cloud architects, and machine learning enthusiasts who want to become machine learning solutions architects. Basic knowledge of the Python programming language, AWS, linear algebra, probability, and networking concepts is assumed.



- 探索不同的機器學習工具和框架,以解決雲端中的大規模機器學習挑戰。
- 建立高效的資料科學環境,用於資料探索、模型建立和模型訓練。
- 學習如何在機器學習模型開發中實現偏差檢測、隱私和可解釋性。


您將首先了解ML的基礎知識以及如何將ML應用於實際的業務問題。在探索了一些領先的ML算法來解決不同類型問題後,本書將幫助您掌握數據管理和使用TensorFlow和PyTorch等ML庫。您將學習如何使用Kubernetes/Kubeflow等開源技術構建數據科學環境和ML流程,然後進一步使用Amazon Web Services(AWS)服務構建企業級ML架構。接著,您將涵蓋安全和治理考慮因素、高級ML工程技術以及如何在ML模型開發中應用偏差檢測、可解釋性和隱私。最後,您將熟悉AWS AI服務及其在實際用例中的應用。


- 應用ML方法解決業務問題。
- 設計實用的企業級ML平台架構。
- 實施ML工作流自動化的MLOps。
- 使用AWS建立端到端的資料管理架構。
- 訓練大規模ML模型並優化模型推論延遲。
- 使用AI服務和自定義ML模型創建業務應用程式。
- 使用AWS服務檢測數據和模型偏差並解釋模型。