Working with over 30 enterprise clients, in industries like financial services, insurance, publishing, and retail, the Fast Forward Labs team has had ample opportunity to observe the challenges of doing data science in practice. By now, most organizations have moved beyond traditional waterfall software development process to adopt more risk-tolerant and agile methodologies. But directly applying agile to data science can create friction, as data products require more leeway for experimentation and exploration, as well as open communication between business, science, and engineering teams.
With so many data teams looking for guidance, we’re excited to see O’Reilly’s new (free!) eBook, Development Workflows for Data Scientists (PDF), which features insights from our Friederike Schuur. The book includes guidance on structuring teams, designing workflows, optimizing processes to learn from previous work, documenting outcomes, and communicating results to non-technical colleagues. Friederike, for example, contrasts the value documentation in standard software development versus experimental data product development:
In data science and machine learning you’re doing so many things before you know what actually works. You can’t just document the working solution. It’s equally valuable to know the dead ends. Otherwise, someone else will take the same approach.
Check out the book, and feel free to contact us at email@example.com with questions about your own data science processes!
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Apr 14 2017
by — In this post we are going to look at an interactive visualization that clusters movies together based on their ratings by a set of users. This visualization will give us a glimpse into the aesthetic tastes of a community of cinephiles.
Mar 13 2017
by — Micha’s talk demystifies deep learning. View the slides. Our research team spent last week in London hosting sessions and workshops about applied machine learning at the QCon conference. Micha Gorelick gave a talk about building a working product with Keras, a high level deep learning framework. He started by explaining deep learning at a conceptual level (describing neural networks like uni...