Join 30K developers in learning how to responsibly deliver value with ML. Learn the foundations of machine learning through intuitive explanations, clean code and visualizations. Learn how to combine machine learning with software engineering to build production-grade applications. Over the past 7 years, I've worked on ML and product at a large company (Apple), a startup in the oncology space (Ciitizen) and ran my own startup in the rideshare space (HotSpot). Throughout my journey, I've worked with brilliant engineering and product teams and learned how to responsibly develop, deploy and iterate on ML systems across various industries, stacks and scale I currently work closely with teams from early-stage/F500 companies in helping them deliver value with ML while diving into the best and bespoke practices of this rapidly evolving space.

As more and more industries bring ML use cases to production, the need for consistent practices for managing ML in Production and optimizing ML Lifecycle iteration has grown rapidly. Last year, a few of us partnered with USENIX to drive the first-ever Industry/Academic conference dedicated to the challenges of and innovations in managing ML in Production. OpML 2019 was a great success - bringing together experts, practitioners, engineers, and researchers to discuss the latest and greatest in ML Ops. You can find a summary of OpML 2019 here. This year, due to COVID19, OpML 2020 became a virtual conference with video presentations and open discussions on Slack.

One of the most exciting things in machine learning (ML) today, for me at least, is not at the bleeding-edge of deep learning or reinforcement learning. Rather it has more to do with how models are managed and how data scientists and data engineers effectively collaborate as teams. Navigating those waters will lead organisations towards a more effective and sustainable application of ML. Sadly, there is a divide between "scientist" and "engineer." "Building production machine learning applications is challenging because there is no standard way to record experiments, ensure reproducible runs, and manage and deploy models," says Databricks.