Decrease your time to value with three simple steps to AI governance

You already know AI Governance is essential to a mature analytics and AI production pipeline, but are you unsure where to start? You may have seen great demonstrations of what solid analytics and AI Governance could look like, but are you having a difficult time projecting how this would look in your own infrastructure? Are you simply interested in understanding and mitigating risks across your production pipeline and making it more efficient? We’ll get you sorted!

Since the release of Dataiku Govern, Dataiku’s newest environment dedicated to managing analytics and AI projects at scale,  we have been supporting dozens of clients across industries, geographies, and governance maturities. Over the course of this work, we’ve identified some common challenges that organizations have to understand and address to efficiently deliver on analytics and AI Governance. One of the biggest pains organizations face is clarifying and gathering the governance requirements needed to use a governance tool, like Dataiku Govern. Where they are not well defined, these requirements embody the chasm between organizations knowing a governance solution is needed and organizations implementing an effective and relevant solution. This two-part blog post aims at helping organizations close this chasm and get to value faster.

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