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Jennifer Prendki

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VP of Machine Learning

Jennifer is the founder and CEO of Alectio. The company is the direct product of her beliefs that good models can only be built with good data, and that the brute force approach that consists in blindly using ever larger training sets is the reason why the barrier to entry into AI is so high. Her favorite slogans are: "not all data is created equal", "data is the new plastic" and "Smart Data > Big Data". Prior to starting Alectio, Jennifer was the VP of Machine Learning at Figure Eight, the pioneer in data labeling, Chief Data Scientist at Atlassian and Senior Manager of Data Science in the Search team at Walmart Labs. She has spent most of her career creating a data-driven culture wherever she went, succeeding in sometimes highly skeptical environments. She is particularly skilled at building and scaling high-performance machine learning teams and is known for enjoying a good challenge. Trained as a particle physicist (she holds a PhD in particle physics from Sorbonne University), she likes to use her analytical mind not only when building complex models but also as part of her leadership philosophy. She is pragmatic yet detail oriented. Jennifer also takes great pleasure in addressing both technical and nontechnical audiences alike at conferences and seminars and is passionate about attracting more women to careers in STEM.

Jennifer Prendki

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    • How much data is actually enough for machine learning? Even more important, how much data is too much? We don’t hear this question frequently in machine learning circles, but we should.

    • The IoT enables massive amounts of data needed for new machine learning applications, but how do you navigate this tremendous amount of actionable data? Here are a few items to keep in mind as you develop a training set for your next smart IoT application.

    • Transfer learning is critical to the successful deployment of IoT deep learning applications that require complex machine-generated information of such volume and velocity that it would be simply impossible to ever find enough human experts to label it and train new models from scratch in a reasonable amount of time.