Meetup #30

Path to Production and Monetizing Machine Learning

The concept of machine learning products is a new one for the business world. There is a lack of clarity around key elements: Product Roadmaps and Planning, the Machine Learning Lifecycle, Project and Product Management, Release Management, and Maintenance. In this talk, I will cover a framework specific to Machine Learning products. I will discuss the improvements businesses can expect to see from a repeatable process. I will also cover the concept of monetization and integrating machine learning into the business model. 


Steps in the data science lifecycle. Maintenance and continuous improvement. The difference between releasing ML to production and monetizing ML. Building a ML product roadmap and calculating ROI.

In this episode

Vin  Vashishta

Vin Vashishta

Chief Data Scientist, Data By V-Squared

I am an applied data scientist and I teach companies to monetize machine learning. I am currently working on a ML based decision support product as well as my strategy consulting practice.



Demetrios Brinkmann

Demetrios Brinkmann


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.