At the beginning of the 2010s, the software industry came up with the 12-Factor App methodology which had a tremendous impact on the way people build software so it can be deployed and operated easily. That change in culture combined with the later introduction of Docker was a boon for advancing the DevOps maturity of teams around the globe. Ten years later the data applications world is struggling with getting pipelines to production due to problems that can be easily solved by a similar cultural shift. This talk shows how Kedro can help you build data apps in the spirit of the 12-Factor App methodology and advance the MLOps maturity of your teams.
In this episode
Machine Learning Engineer, QuantumBlack
Ivan spent the first five years of his career in Web Development in his hometown of Sofia, where he created websites in Macromedia Flash, PHP, RubyOnRails, and jQuery. Later on, Ivan worked on an insurance aggregator platform in Copenhagen based on Ruby and Java and was exposed to Docker in its early days. Ivan's brief experience with Java took him to Ocado Technology in the UK, where he worked on a cutting-edge software for their second-generation automated warehouses, a highly available distributed system based on Akka and Scala. During his time in Ocado, Ivan had a taste of running and supporting a complex system in production by joining the DevOps support rota for the whole warehouse and dealing with a couple of large-scale incidents. Five years ago Ivan joined QuantumBlack, a data science consultancy owned by McKinsey, where he applied all his previous experience in Software Engineering to bring better engineering practices to the data world. All that eventually resulted in the creation of Kedro, a framework for rapid application development of data pipelines, which Ivan currently still works on.
Demetrios is one of the main organizers of the MLOps community and currently resides in a small town outside Frankfurt, Germany. He is an avid traveller who taught English as a second language to see the world and learn about new cultures. Demetrios fell into the Machine Learning Operations world, and since, has interviewed the leading names around MLOps, Data Science, and ML. Since diving into the nitty-gritty of Machine Learning Operations he felt a strong calling to explore the ethical issues surrounding ML. When he is not conducting interviews you can find him making stone stacking with his daughter in the woods or playing the ukulele by the campfire.