C3’s mission is to provide enterprises from a variety of industries (energy, finance, oil and gas, healthcare, etc.) with robust, scalable applications that deliver AI-driven insights. Common AI use cases within these applications involve inventory optimization, anomaly detection, predictive maintenance, and more. While C3’s ultimate vision is to provide out-of-the-box applications for these various industries and use cases, there are typically challenging custom configurations requested by customers, which makes the application development process quite challenging for internal C3 developers.
As a result, C3 developed its own internal data management, ML model evaluation, and deployment pipeline product called IDS (Integrated Development Studio). This tool was intended to provide internal developers and data scientists with GUI to help evaluate and monitor the various artifacts (data models, ML models, deployment pipelines) in their respective workflows.
While initially well-received, internal IDS usage quickly dropped off due to gaps in both functionality and overall usability. This was a serious concern since IDS was eventually poised to go-to-market publicly, with its first controlled beta release set for mid 2022. Confusing product navigation, outdated UI components, and slow performance made the usability issues fairly obvious.
The lacking functionality, however, provided a larger opportunity to fundamentally redesign the tools within IDS from the ground up. Within the various sub-tools, my team focused on data scientist workflows for preparing data, training ML models, evaluating performance, and deploying/monitoring models in production environments.