Factory Pattern for Building and Scaling ML Solutions

As the deployment of machine learning (ML) assets rapidly expands, real-time observability and performance monitoring becomes crucial for managing these assets effectively. Data science teams require frameworks that automate and streamline the monitoring and maintenance of ML models in production, aiming to enhance performance and reduce costs. In this session, we will explore how the Tiger MLCore platform accelerates ML lifecycle through automation, observability and monitoring. By leveraging pre-built code templates, Tiger MLCore facilitates best coding practices and minimises development effort. It integrates MLOps principles to automate workflows from development to production, ensuring robust governance and reliability. The observability and monitoring layer provides a single pane of glass view of ML assets across the enterprise.