About this book
A highly scalable machine learning platform enables organizations to quickly scale the delivery of ML products for faster business value realization. There is also a huge demand for skillful ML solutions architects in different industries.
This handbook 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 core machine learning fundamentals, and how ML can be applied to real-world business problems. Next, you’ll explore some of the leading machine learning and deep learning algorithms for different types of ML problems. The book will further cover data management and architecture considerations for building data science environments using ML libraries such as scikit-learn, Spark, TensorFlow, and PyTorch. You’ll then implement Kubernetes containers for orchestration infrastructure management and later build a data science environment and enterprise ML architecture using AWS ML services. Toward the end, you’ll go through security and compliance considerations, advanced ML engineering techniques, and how to apply ML bias, fairness, and explainability in the end-to-end ML cycle.
By the end of this book, you’ll be able to design and build an ML platform to support ML use cases and architecture patterns.
- Publication date:
- December 2021