Why do some machine learning projects succeed while others fall down completely? In this discussion, we will discuss the real-world challenges that Enterprises face in deploying ML solutions, focussing on challenges with existing, legacy dev-ops environments and how certain patterns of success emerge to help combat failure.
In this episode
Scientific and AI Consultant, Calculation Consulting
Dr Martin runs a boutique consultancy in San Francisco, California that supports organizations looking to research, build, and deploy data science, machine learning, and AI products. He has worked with clients like eBay, Blackrock, GoDaddy as well as widely successful startups such as Aardvark (acquired by Google) and Demand Media (the first public Billion dollar IPO after Google). He is a world-renowned researcher, collaborating with UC Berkeley on the WeightWatcher project, and has taught at UC Berkeley and Stanford, and spoken at KDD, ICML, etc. He is also currently a scientific advisor to the Page family’s Anthropocene Institute, consulting on areas including modern nuclear and quantum technologies and their response to the current pandemic.
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.