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Before your company can harness machine learning hotness or take advantage of big data, you need to gather, prepare and manage the data as well as establish metrics to guide your work. In our experience, the actual machine learning component is relatively small compared to the investment in all of the surrounding infrastructure of a proper data pipeline.
Factual SVP of Engineering, Boris Shimanovsky, will discuss practical considerations, how to choose appropriate tools that are right-sized for the work, and why we invest in experimentation velocity.
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