The Machine Learning Solutions Architect Handbook
Practical strategies and best practices on the ML lifecycle, system design, MLOps, and generative AI
Created by David Ping
Explore the practical side of machine learning architecture with a focus on real-world business solutions. Learn how to design secure, scalable ML platforms, manage AI risk, and dive into the latest generative AI technologies. Gain hands-on experience with tools and strategies used by professionals in the field.
Packt | Apr 2024 | 602 min
What You Will Learn
You will work through practical examples and real-world scenarios to connect technical concepts with business needs. Guided by expert insights, you will use cloud services and open-source tools to build, deploy, and manage ML systems. Along the way, you will tackle the full ML lifecycle, from ideation to scaling and risk management.
Key Features
- Design robust ML architectures for cloud-based business applications
- Apply risk management and governance to AI and ML projects
- Implement generative AI solutions using proven architecture patterns
Target Audience
This content is ideal for solutions architects, ML engineers, and MLOps professionals looking to deepen their expertise in machine learning system design. Data scientists, analysts, and AI product managers aiming to bridge technical and business skills will also benefit. A working knowledge of Python, AWS, and basic ML concepts is recommended.
