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As we survey the landscape of AI technologies, it’s easy to get caught up in the latest tools and techniques, and the startups that frequently are powering these. While these can offer powerful approaches to improve your company’s AI practice, it’s also critical to focus on the productivity of the data scientists and developers who develop the models themselves. By investing in decreasing iteration time, companies can significantly improve their returns on AI initiatives. Model development is a highly iterative process. Data scientists and engineers need to try different techniques, tools, and configurations on any model in order to drive the desired outcomes. Furthermore, just because your new model is live in production doesn’t mean that it is delivering optimal value. Each iteration of a model can improve performance, which in turn can drive big impacts to the bottom line. This talk will give an overview of areas of the ML Ops practice that should be emphasized in order to improve the productivity of your data scientists and increase the rate at which business critical models improve.

Speaker

Phillip Finucane

Ex-CEO, Pat.ai