Key Features
- Go in-depth into the ML lifecycle, from ideation and data management to deployment and scaling
- Apply risk management techniques in the ML lifecycle and design architectural patterns for various ML platforms and solutions
- Understand the generative AI lifecycle, its core technologies, and implementation risks
Who this book is for
This book is for solutions architects working on ML projects, ML engineers transitioning to ML solution architect roles, and MLOps engineers. Additionally, data scientists and analysts who want to enhance their practical knowledge of ML systems engineering, as well as AI/ML product managers and risk officers who want to gain an understanding of ML solutions and AI risk management, will also find this book useful. A basic knowledge of Python, AWS, linear algebra, probability, and cloud infrastructure is required before you get started with this handbook.