Session Overview

We make data for Machine Learning. Our clients are industry leaders in autonomous vehicles, geospatial analytics, robotics, and tracking technology. They have set their sights on providing driverless and contactless solutions, and evaluating what is happening on and to the world at global scale. In theory, making data is really straightforward. The client gives us data and instructions; we then put the data in a tool and follow the instructions. However, in reality, it’s a collaboration between many different people. There are unexpected twists and turns that determine the quality, quantity, accuracy, precision, breadth, depth and timeliness of the data - the data that will ultimately fuel the next generation of how we live and operate. In my talk I will point out the common pitfalls I’ve identified after working on countless use cases, across industries, with clients around the world. I hope my insights will enable and guide you to create the best possible datasets for your unique application.


Overview

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    Maximizing Dataset Potential: Challenges, Considerations & Best Practices

    • Abstract & Bio

    • Maximizing Dataset Potential: Challenges, Considerations & Best Practices

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