Lilia has been thinking about, designing, and shipping data-driven products for enterprise customers for more than 10 years. As the product owner for the Data Experience team at PagerDuty, she’s passionate about helping people learn from data to make decisions and solve problems. In her spare time, Lilia plays keyboard in the PagerDuty band (the OnCalls), volunteers for Code2040, and watches any movie Keanu Reeves is in
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?
for free. You
can too.
