The competitive advantage of computer vision companies is no longer in model architectures but in their training data and the ML-Ops behind it. How do AI-first companies compete? We explored a number of emerging computer vision cases where models become fixed elements, re-trained on continuously evolving datasets as a company's deployments grow. This calls for the need for a CRM-like experience for training data, where ML-Ops tools can apply changes from multiple sources, and enable complex labeling or inference workflows to occur. We talked about how V7 has tackled this problem, what the needs for the MLOps community are, and how to standardize our work to enable further collaboration.
In this episode
Alberto Rizzoli is the co-Founder of V7, an ML-Ops platform for deep learning teams to create labeling workflows for computer vision through models and humans, centralizing their training data in one place. V7 manages over 50,000 labeled datasets across hundreds of AI teams. The company's goal is to enable a labeling experience that self-automates until it is indistinguishable from inference. Alberto founded his first startup at age 19 becoming MakerFaire’s 20under20. In 2015 founded Aipoly with Simon Edwardsson, one of the first engines capable of running large deep neural networks on smartphones, leading to the creation of an app enabling the blind to identify 5,000 objects through their phone camera used over 3 billion times. Alberto's work on AI granted him an award and personal audience by Italian President Sergio Mattarella, as well as Italy’s Premio Gentile for Science and Innovation. V7's underlying technology won the CES Best of Innovation in 2017 and 2018.
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.