And if you’re a fresh Head of ML tasked with building a team from scratch, how can you make sure you haven’t missed any steps?
More importantly, how do you ensure that your initiatives end up in production creating value to your company instead of in the trash can?
During this session, we talked about AI strategy, team building, data science roles, data product development, data productization, the most common bottlenecks to avoid and more!
Benjamin Biering, our next Tech Leaders Hub guest, can tell you all about it—he’s done it more than once.
Benjamin’s machine learning journey has taken him through various roles (Digital Analyst, Data Product Manager, Head of Data Science) at various companies (@Creuna, @Ebay, @2021.AI) all the way to becoming Head of Machine Learning at Podimo.
If you haven’t heard of Podimo yet, they’re on the path to building the equivalent of Netflix for the audio stories space: their platform offers podcasts, audiobooks, news, and more.
We discussed about:
– The specific questions you need to ask yourself before building a machine learning team
– How to synchronize your ML team with the overall strategy of the business
– How to hire the right ML talent
– How to support teamwork between your ML team and the rest of the organization
– How to create the right conditions for success
– The importance of thinking about productizing ML-based data products
Dive into our library of Tech Leaders Hub sessions full of advice that will help you make better decisions and avoid common mistakes as a tech executive.
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