There are many challenges with implementing machine learning effectively into your business. It’s estimated that only about 20 percent of ML models make it into production today, but digging a bit deeper, the challenges go beyond just the models themselves. Businesses need to embrace the full ML lifecycle—from data streams to production environments—to eliminate silos and drive business impact effectively.
In this session, we will explore how your business can better serve decision makers with transparent, explainable, and unified workflows that empower business users to collaborate with data scientists and take confident action that drives business impact. We will demonstrate an integrated, end-to-end ML development lifecycle, including:
- Data access, pre-processing, modeling, and experiment tracking
- Deploying, operating, and governing ML models in production
- Integrating ML models with self-service visual apps to surface actionable insights