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 firstname.lastname@example.org with questions about your own data science processes!
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Apr 14 2017
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Sep 11 2017
by — We’re pleased to share the recording of our recent webinar on machine learning interpretability and accompanying resources. We were joined by guests Patrick Hall (Senior Director for Data Science Products at H2o.ai, co-author of Ideas on Interpreting Machine Learning) and Sameer Singh (Assistant Professor of Computer Science at UC Irvine, co-creator of LIME). We spoke for an hour and got ...