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.

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Spis treści

The Machine Learning Solutions Architect Handbook. Create machine learning platforms to run solutions in an enterprise setting eBook -- spis treści

• 1. Machine Learning and Machine Learning Solutions Architecture
• 2. Business Use Cases for Machine Learning
• 3. Machine Learning Algorithms
• 4. Data Management for Machine Learning
• 5. Open Source Machine Learning Libraries
• 6. Kubernetes Container Orchestration Infrastructure Management
• 7. Open Source Machine Learning Platforms
• 8. Building a Data Science Environment Using AWS ML Services
• 9. Building an Enterprise ML Architecture with AWS ML Services
• 10. Advanced ML Engineering
• 11. ML Governance, Bias, Explainability, and Privacy
• 12. Building ML Solutions with AWS AI Services