Uzma Barlaskar

About Me

Uzma Barlaskar is a product lead at Facebook. She has led teams in the News Feed and Marketplace orgs building out machine learning products that are used by millions of users. She has co-founded a machine learning startup called PatternEQ. She also has experience working on advertising products, building out data management platforms and previously, worked at the hedge fund D.E.Shaw.

Machine Learning for Product Managers Part I — Problem Mapping

The first principle of building a great product using machine learning is to focus on user needs. One of the common misconceptions is that people think Machine Learning somehow fundamentally changes the skill-set of a PM. Machine learning is not an end unto itself. Machine learning is a tool to solve a real user need. Many people and companies that have a cool AI technology, think that the technology alone validates its usage. If you have a cool technology to apply, think about what problems could be solved, or what experiences can be enhanced through that technology.

Machine Learning for Product Managers Part II — ML Skills

This is a continuation of the three part series on machine learning for product managers.The Part I focused on what problems are best suited for application of machine learning techniques. This note would delve into what additional skill-sets a PM needs when building products that leverage machine learning. As in Part I, the core skill sets required of a PM do not change whether you work in a machine learning driven solution space or not. Product managers typically use five core skills — customer empathy/design chops, communication, collaboration, business strategy and technical understanding. 

Machine Learning for Product Managers Part III — Caveats

What are the common mistakes made in building ML products? The goal of the note is to provide someone with limited ML understanding a general sense of the common pitfalls so that you can have a conversation with your data scientists and engineers about these. Many companies wanted to use ML and had built up ‘smart software’ strategies but didn’t have any data. You cannot use machine learning if you have no data. You can apply ML on small data sets too, but you have to be very careful so that the model is not affected by outliers and that you are not relying on overcomplicated models.

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