You can’t throw a rock without running into a talk about the basics of data science and best practices for data hygiene. What’s next? Shipping a data science-powered feature takes more than data collection, experimentation, and analysis - it takes magic. This talk will teach the magic based on the many ~mistakes~ learning opportunities that my team has experienced so that any team will feel inspired to build a customer-facing data science-based feature.
What is different about designing, developing, shipping, supporting, and going on-call for a data science-powered feature? How can you deliver customer value and, yes, make money with data science? Data science models provide insights; a product provides value. You don’t have to be a data scientist to develop a data science-based feature. You’ll leave this talk inspired and prepared to build a successful data science-based feature with these five crucial considerations:
What are potential features your team could build that use data science and deliver value to our customers?
What does it take to productionalize a data science model?
How do you go on call for a data science-based feature that is (by nature) different for every customer?
What are the design considerations for a feature that has unpredictable (non-deterministic) behavior?
How do you develop a feature that relies on math you (probably) do not completely understand?