Abstract: The Agile approach of short development cycles with a goal of continually delivering customer value can work equally well for machine learning-centric projects. We will show you how a minimum viable product for an ML-centric application benefits from micro-sprints to iterate on the algorithm or model within the larger development push. This rapid iteration of a data science model requires a functioning production layer where the model can be validated and edited quickly.
We’ll demonstrate how data scientists and engineers can work with Agile principles on the Algorithmia platform to build, test, and implement models quickly.
Bio: Jose Brache is a Boston based Enterprise Sales Engineer at Algorithmia, focused on making state-of-the-art AI and ML effortlessly scalable within organizations. Previously he worked to deliver training data used in machine learning primarily for developing autonomous vehicles. Jose has a true passion for helping enterprises use machine learning and data science to solve cutting edge problems.
