Transactional Machine Learning with Data Streams and AutoML: Build Frictionless and Elastic Machine Learning Solutions with Apache Kafka in the Cloud Using Python

Table of Contents
About the Author
About the Technical Reviewer
Acknowledgments
Introduction
Chapter 1: Introduction: Big Data, Auto Machine Learning, and Data Streams
	Structured Data
	Semi-structured Data
	Unstructured Data
	A Quick Take on Big Data
	Data Quality
	Data Streams
	Stream Mining
	Auto Machine Learning (AutoML)
	Machine Learning Model Building Process
	Concluding Remarks
Chapter 2: Transactional Machine Learning
	Examining TML
		Features of TML
			Data Fluidity
			Joining Data Streams
			Data Stream Standardization
			Data Stream Integration with AutoML
			Low Code
		Data Stream Storage Platform (DSSP)
		MAADS-VIPER
	Algorithm and Insights Management System (AiMS) Dashboard
		AutoML Technology
		Unsupervised Learning: Detecting Anomalies
		Frictionless Machine Learning
	Concluding Remarks
Chapter 3: Overcoming Challenges to ML Adoption
	Overview of Challenges
	Understanding the Root Causes of Challenges in Adopting Advanced Technologies
	Data Decentralization
	Lack of Corporate Strategy
	Advanced Technology Costs
	Choosing ML Use Cases
	ML Change Acceptance
	Technological Barriers
		Skill Gap to Adopting ML
		Strategy Gap in Adopting ML
		Communication Gap in Adopting ML
		Approaches to Addressing the Challenges
	Discussion and Path Forward
Chapter 4: The Business Value of Transactional Machine Learning
	Conventional Machine Learning (CML)
	The TML Opportunity
	Core Areas of Value from TML
	TML Value Areas (Levers)
	Measuring Value from TML Solutions
	Choosing the Right TML Use Cases
	Benefits and Costs
	Risks and Pitfalls
	Concluding Remarks
Chapter 5: The Technical Components and Architecture for Transactional Machine Learning Solutions
	Overview of a TML Solution
	Reference Architecture of a TML Solution
	Description of Technical Components
	Technical Architecture of a TML Solution
	Unsupervised Learning
	Communication Process Between Components
	Data Flows
	Example Architecture
	TML Cost Management
	Concluding Remarks
Chapter 6: Transactional Machine Learning Solution Template with Streaming Visualization
	Overview of TML Solution Template
		Template Component Details
		Kafka Cloud via Confluent Cloud
		VIPER Environment File
	VIPER, VIPERviz, and HPDE Setup
		Kafka Topics and Data Streams
	TML Example Code
	Walmart Foot Traffic Prediction and Optimization with TML
	Unsupervised Learning for Anomaly Detection
	Anomaly Detection on Banking Transactions with TML
	Concluding Remarks
Chapter 7: Visualize Your TML Model Insights: Optimization, Predictions, and Anomalies
	Streaming Anomaly Detection Visualization
	Streaming Prediction Visualization
	Streaming Optimization Visualization
	AiMS Dashboard
	Generic Topics’ Visualization
	Visualization with WebSockets
	Concluding Remarks
Chapter 8: Evolution and Opportunities for Transactional Machine Learning in Almost Every Industry
	Areas of Further Exploration
		Faster and More Complex Decision-Making by Machines
		Broader Adoption of AutoML Techniques and Processes to Data Streams
		Stacking and Chaining Different TML Solutions
	Concluding Remarks
Chapter 9: TML Project Planning Approach and Closing Thoughts
	TML Technology Stack
	TML Project Planning Approach
	TML Value Creation
	Closing Thoughts
Definitions
References
Index