Today data science is a field that is an aggregation of people from various backgrounds - econometrics, statistics, engineering, business analysts, and data engineers. Each of these groups has different expectations from a Machine Learning platforms. But, each group faces problems that have some common challenges - improving reproducibility, reducing technical debt, reducing the time to try new experiments. The challenge before any MLOps system is to create platforms and processes that address the needs of each of these groups. The title is borrowed from an old Indian story.
In this episode
Director, Data Science at Freshworks
I am a tinkerer in the Machine Learning world with experience in the design and development of ML applications and processes. In the past few years, I have been focused on improving the processes and tools around the Machine Learning teams. I explore the ideas of Auto ML, ML Ops, and model evaluation. I help customers adopt and use the best tools and processes that allow them to scale their Data Science or Machine Learning tools. I have development experience in the open stack ML platforms and the of late the managed ML services from Azure and AWS.
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.