De-Risk Your AI Efforts by Removing Friction From Your MLOps Processes

Presented by

Catalina Herrera, Principle Sales Engineer, and Chris Helmus, Sr. Sales Engineer @ Dataiku

About this talk

According to McKinsey, building ML into processes enables leading organizations to increase their process efficiency by 30% or more while also increasing revenues by up to 10%. However, it’s not that simple. Several blockers prevent organizations from overcoming the difficulties encountered when industrializing AI. As a result, it can take up to nine months for teams to go from the proof of concept stage to production. In this context, how do you remove friction from your MLOps process and make your model processes trusted, agile, and controlled, so that you can finally deliver more value from your analytics and model faster? In this session, you’ll learn how Dataiku’s MLOps framework can help you to: -Increase agility and solve difficulties in handoffs between business, data scientists, and IT -Make your models trusted from the get go (and, therefore, reduce risk) -Apply model control and approvals to enable, not disable, your AI projects
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Dataiku is the world’s leading platform for Everyday AI, systemizing the use of data for exceptional business results. Organizations that use Dataiku elevate their people (whether technical and working in code or on the business side and low- or no-code) to extraordinary, arming them with the ability to make better day-to-day decisions with data. More than 450 companies worldwide use Dataiku to systemize their use of data and AI, driving diverse use cases from fraud detection to customer churn prevention, predictive maintenance to supply chain optimization, and everything in between.