CSC 6605 - Machine Learning Production Systems

• Design, implement, deploy, and operate a machine learning-powered RESTful service and supporting backend systems 
• Design and implement data processing systems that use batch and stream processing patterns and technologies for ingesting and processing event data 
• Apply design patterns and data storage systems to design and implement data storage services to support batch and streaming data processing, model training, and model evaluation 
• Create distributed systems to support model training and evaluation 
• Automate and monitor model training, evaluation, deployment, and operation 
• Evaluate and compare architectures of ML-powered systems 

• Cloud computing
• Microservices
• Machine learning 

• Distributed batch and streaming data processing platforms and associated programming paradigms 
• Data representation and storage patterns to support batch and streaming data pipelines and services 
• Reliability, consistency, and scalability challenges and solutions for big data systems 
• Patterns for handling model training and evaluation on data that changes or updates over time 
• Tradeoffs between batch and streaming systems in terms of efficiency, computational capabilities, and time until updates are available based on new data 
• Model serving and deployment 
• Monitoring and alerting for services and performance of machine learning model predictions  
• Implementation of systems for common machine learning tasks such as classification/regression and recommendations