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How to land your AI product

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Ritendra Datta

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What you'll learn

Create a technical strategy to go from launching to landing

Five key steps that will take your just-launched AI product or feature to a successful landing.

Define clear metrics and tactics for landing

Develop a scientifically sound method to evaluate successful landing of your product. Define clear steps to get there.

Learn what's distinct about AI products

Hear about unexpected technical and non-technical gotchas including an example of a failed launch and its recovery path.

Why this topic matters

We celebrate user-facing AI product and feature launches with fanfare, but they don't always land well. Factors distinguishing AI products from others include the degree of 'cold start', the stochasticity of AI outputs, and the criticality of feedback-driven development. Therefore, launch is just the start; we need to quickly and carefully design and execute steps to ensure successful landing.

You'll learn from

Ritendra Datta

Head of Applied AI at Databricks

Ritendra is a Senior Director of Engineering at Databricks building out the Applied AI team. He's previously built and scaled large teams at Facebook/Meta and Google, and also worked at legendary research labs IBM TJ Watson Research Center and Xerox PARC.

He has helped build and scale many successful AI products and features in search, recommendations, and Generative AI, such as Google Shopping, Facebook Reels, and the Databricks Assistant.

Besides engineering and machine learning, he loves Probability and Statistics, Filmmaking, Stage- and Screen-writing, Theater Production, Music, Vacuum Cleaners, and Woodworking. Having an active life outside work, he's an advocate for work-life harmony.

When time permits, Ritendra actively does career coaching and mentoring over at MentorCruise. When the mentoring is effective, it's a tremendous source of joy.

Previously at


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