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. And 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 like a true professional.
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