Transactional Machine Learning with Data Streams and AutoML

Sebastian Maurice is founder and CTO of OTICS Advanced Analytics Inc. and has over 25 years of experience in AI and machine learning.  Previously, Sebastian served as Associate Director within Gartner Consulting focusing on artificial intelligence and machine learning. He was instrumental in developing and growing Gartner’s AI consulting business. He has led global teams to solve critical business problems with machine learning in oil and gas, retail, utilities, manufacturing, finance, and insurance. Dr. Maurice also brings deep experience in oil and gas (upstream) and was one of the first in Canada to apply machine learning to oil production optimization, which resulted in a Canadian patent: #2864265.    Sebastian is also a published author with seven publications in international peer-reviewed journals and books. One of his publications (International Journal of Engineering Education, 2004) was cited as landmark work in the area of online testing technology. He also developed the world’s first Apache Kafka connector for transactional machine learning: MAADS-VIPER. Dr. Maurice received his PhD in electrical and computer engineering from the University of Calgary, and has a master’s in electrical engineering, and a master’s in agricultural economics, with bachelors in pure mathematics and bachelors (hon) in economics.   Dr. Maurice also teaches a course on data science at the University of Toronto and actively helps to develop AI course content at the University of Toronto.  He is also active in the AI community and an avid blogger and speaker.  He also sits on the AI advisory board at McMaster University.

Chapter 1:  Introduction: Big data, Auto Machine Learning and Data Streams.- Chapter 2: Transactional Machine Learning.- Chapter 3: Industry Challenges with Data Streams and AutoML.- Chapter 4: The Business Value of Transactional Machine Learning.- Chapter 5: The Technical Components and Architecture for Transactional Machine Learning.- Overview of a TML Solution.- Chapter 6: Template for Transactional Machine Learning Solutions.- CHAPTER 7: Visualize Your TML Model Insights: Optimization, Predictions and Anomalies.- Chapter 8: Evolution and Opportunities For Transactional Machine Learning in Almost Every Industry.- Chapter 9: Conclusion and Final Thoughts.