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Designing simple, reliable machine learning systems
About This Webinar
We know from software engineering that striving to make our software simpler can make it better. It makes it easier to reason about debugging, it increases architectural agility, it makes it function better in the real world.
Machine learning software is no different, simpler is often better. It also increases interpretability, it lets your system operate at larger scale, and gives you additional ways to incorporate humans in the loop.
What are some practical steps you can take to make your machine learning code simpler? And what trade-offs are you making by making your system simpler?
Who can view: People who attended or registered for the webinar only
Webinar Price: Free
Featured Presenters
Lead Data Scientist
Ewan is lead data scientist for the BBC Datalab team. In the Datalab we do a lot of audience facing machine learning: personalisation, recommendations, and so on.
Before working at the BBC, Ewan has worked with data and computers throught a wide range of industries, from travel, navigating ships, and online marketing.
Before working at the BBC, Ewan has worked with data and computers throught a wide range of industries, from travel, navigating ships, and online marketing.
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