Continuous Learning for safer and better ML models

Continuous Integration (CI) and Continuous Deployment (CD) are vital practices in software development, providing a safety net to ensure fast and safe code deployment by running various tests and alerting developers to any issues. These practices are being adapted for Machine Learning (ML) development due to the critical nature of ML applications in fields like healthcare and autonomous vehicles. At Sematic, a platform developed by former Cruise engineers, the focus is on automating ML training pipelines through two main dimensions: data and code. Regression Testing in ML parallels functional testing in software, establishing a dataset of critical scenarios to ensure no regressions when code changes occur. However, training pipelines can be costly, leading to periodic regression testing rather than continuous. Beyond maintaining performance, improving model accuracy is crucial, requiring feedback loops that integrate incorrect predictions back into the training dataset for continuous learning. Sematic supports these processes with an open-source platform that automates end-to-end ML pipelines, facilitating the transition from prototype to production while enabling error mining and retraining, thus enhancing model performance over time.