Modernization and improvement of Machine Learning infrastructure.

• There wasn’t enough visibility and monitoring of production predictions of ML models.
• No infrastructure for A/B testing ML models and parameters in production.
• The ML model deployment process was neither standardized nor well documented

Novacomp provided a Staff Augmentation service to the Machine Learning and Data Science team.

• Novacomp team integrated into the company native team, participating in each of their stand-ups, Sprint planning, and sync meetings.
• ML team collaborated with other Novacomp staff-augmented teams like Data Engineering and Analytics.
• Novacomp team brought expertise in ML ops technologies and best practices, helping design and implement a new state of the art ML architecture using FastAPI, Split.io, Weights and Biases, Docker, and Apache Pulsar.

• Implemented a micro service ML architecture that allowed parallel deployments of models for AB testing purposes.
• Developed an Event-driven monitoring system for ML predictions using Apache Pulsar.
• Documented the deployment process, allowing the Data Science team to deploy changes on their own.
• Improved ML services latency by refactoring old code and making asynchronous calls.