Organizations continue to struggle pushing beyond experimentation to drive meaningful business impact. Often unable to collaborate directly with business stakeholders, prototype quickly, or support multiple projects in production, achieving (and maintaining) fully end-to-end data science projects is still a very tangible challenge for many.
During this session, John Posada, Solutions Architect, will preview the DSS platform and showcase how to build a fully functional ML flow – integrating diverse data sources, data prep, exploratory analysis, feature-handling, and using native code/visual recipes in a single, governed, Dataiku DSS workflow. You’ll see how a machine learning model can quickly move from test to production in a few clicks, how to set up A/B testing, auto-monitor model and data drift, and ways to score and publish models via APIs.
