In the current golden days of big data, it seems that data scientists have unlimited access to all the data they need to create always better performing models and data products, and artificial intelligence is starting to outperform humans in multiple areas. However, the race to collect data is also raising an awareness of how this data relates to concerns about privacy and intellectual property. While the amount of data we collect is constantly growing, the challenges related to its fullness and accessibility are now requiring the big data community to become ever more creative and find new ways to extract value from it while respecting users’ right to privacy. This phenomenon is spurring what can be appropriately called “data science of the gaps.” Developing predictive models is now like putting a jigsaw puzzle together—trying to establish the big picture while potentially missing some essential pieces.
As a leader in the software industry, Atlassian takes this matter very seriously and is developing various ways to design intelligent features for its products while ensuring that its customers’ privacy is respected. Jennifer Prendki explains how Atlassian develops machine learning models even if the data is protected by privacy and compliance laws and cannot be used without anonymizing, covering techniques ranging from contextual bandits to document vector representation.
Jennifer Prendki is the vice president of machine learning at Figure Eight, the essential human-in-the-loop AI platform for data science and machine learning teams. 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.
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