The Machine Learning Solutions Architect Handbook. Create machine learning platforms to run solutions in an enterprise setting (Audiobook) Kolonowskie

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 audiobook, you’ll be able to design and build an ML platform to support common use cases and architecture patterns like a true professional. Spis treści: 1. Opening Credits2. Contributors3. Preface4. Chapter 1: Machine Learning and Machine Learning Solutions Architecture5. Chapter 2: Business Use Cases for Machine Learning6. Chapter 3: Machine Learning Algorithms7. Chapter 4: Data Management for Machine Learning8. Chapter 5: Open Source Machine Learning Libraries9. Chapter 6: Kubernetes Container Orchestration Infrastructure Management10. Chapter 7: Open Source Machine Learning Platforms11. Chapter 8: Building a Data Science Environment Using AWS ML Services12. Chapter 9: Building an Enterprise ML Architecture with AWS ML Services13. Chapter 10: Advanced ML Engineering14. Chapter 11: ML Governance, Bias, Explainability, and Privacy15. Chapter 12: Building ML Solutions with AWS AI Services16. Closing Credits O autorze: David Ping is a senior technology leader with over 25 years of experience in the technology and financial services industry. His technology focus areas include cloud architecture, enterprise ML platform design, large-scale model training, intelligent document processing, intelligent media processing, intelligent search, and data platforms. He currently leads an AI/ML solutions architecture team at AWS, where he helps global companies design and build AI/ML solutions in the AWS cloud. Before joining AWS, David held various senior technology leadership roles at Credit Suisse and JPMorgan. He started his career as a software engineer at Intel. David has an engineering degree from Cornell University.