The Machine Learning Solutions Architect Handbook: Create machine learning platforms to run solutions in an enterprise setting

• Explore different ML tools and frameworks to solve large-scale machine learning challenges in the cloud
• Build an efficient data science environment for data exploration, model building, and model training
• Learn how to implement bias detection, privacy, and explainability in ML model development

• Apply ML methodologies to solve business problems
• Design a practical enterprise ML platform architecture
• Implement MLOps for ML workflow automation
• Build an end-to-end data management architecture using AWS
• Train large-scale ML models and optimize model inference latency
• Create a business application using an AI service and a custom ML model
• Use AWS services to detect data and model bias and explain models

This book is for data scientists, data engineers, cloud architects, and machine learning enthusiasts who want to become machine learning solutions architects. You’ll need basic knowledge of the Python programming language, AWS, linear algebra, probability, and networking concepts before you get started with this handbook.