We did a deep dive into Agile processes for Data Science and Machine Learning. We found quite a bit of insight and wanted to share our takeaways. It’s a long read but we hope you’ll enjoy the journey with us.
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We’re continuously working on our process and ethos of applying agile software engineering practice to our machine learning development. Before we go further with that, there are some design decisions that have already gone into our SWE process that we’d like to share.
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Most advocates and practitioners view ML as a dark art, or alchemy, a point of view that insinuates it cannot be tamed, and should not be approached with normal software engineering (SWE) process and rigor. At TWOSENSE.AI, we’ve embarked on the journey to apply and adapt agile software engineering processes to data science, and here’s why.
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The EU made 2 decisions recently that we predict will catapult behavioral biometrics to prominence by this time next year. Here’s why …
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