The Machine Learning Solutions Architect Handbook: Create machine learning platforms to run solutions in an enterprise setting
Author: David Ping
Publisher Finelybook 出版社：Packt Publishing (January 21,2022)
pages 页数：440 pages
Build highly secure and scalable machine learning platforms to support the fast-paced adoption of machine learning solutions
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
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 Author: 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.
Author: 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