Transactional machine learning with data streams and AutoML : build frictionless and elastic machine learning solutions with Apache Kafka in the cloud using Python

1. Introduction: Big Data, Auto Machine Learning, and Data Streams 2. Transactional Machine Learning 3. Overcoming Challenges to ML Adoption 4. The Business Value of Transactional Machine Learning 5. The Technical Components and Architecture for Transactional Machine Learning Solutions 6. Transactional Machine Learning Solution Template with Streaming Visualization 7. Visualize Your TML Model Insights: Optimization, Predictions, and Anomalies 8. Evolution and Opportunities for Transactional Machine Learning in Almost Every Industry 9. TML Project Planning Approach and Closing Thoughts

This book will strengthen your knowledge of the inner workings of TML solutions using data streams with auto machine learning integrated with Apache Kafka. Transactional Machine Learning with Data Streams and AutoML introduces the industry challenges with applying machine learning to data streams. You will learn the framework that will help you in choosing business problems that are best suited for TML. You will also see how to measure the business value of TML solutions. You will then learn the technical components of TML solutions, including the reference and technical architecture of a TML solution. This book also presents a TML solution template that will make it easy for you to quickly start building your own TML solutions.