As organizations begin to test out machine learning use cases and develop models, ML teams need to be thinking long-term. How will they deploy, operate, and manage models once developed? Machine learning has specific operational needs that need to be supported within existing SDLC processes. Doing so often begins with a fundamental question: should I build or buy a machine learning management platform to operate my ML lifecycle? Determining whether to build or buy a machine learning management platform will drastically impact the effectiveness of an organization’s investment in ML and their competitive standing in its industry.
In this episode
CEO & Co-Founder, Algorithmia
Diego Oppenheimer is co-founder and CEO of Algorithmia. Previously, he designed, managed, and shipped some of Microsoft’s most used data analysis products including Excel, Power Pivot, SQL Server, and Power BI. He holds a Bachelor’s degree in Information Systems and a Master’s degree in Business Intelligence and Data Analytics from Carnegie Mellon University.
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.