MLOps Community

What's so enticing about enterprise software? It's incredible to see George's idea and vision to invest in generationally enduring companies. Let's look at the way how George likes to structure deals with companies while he's reviewing them and let's look at the MLOps ecosystem through the eyes of the investors.

From our experience helping customers in the Data and AI field, we learned that the most challenging part of Machine Learning is deploying it. Putting models into production is complex and requires additional pieces of infrastructure as well as specialized people to take care of it - this is especially true if we are talking about real-time REST APIs for serving ML models. With Databricks Model Serving V2, we introduce the idea of Serverless REST endpoints to the platform. This allows teams to easily deploy their ML models in a production-grade platform with a few mouse clicks (or lines of code 😀).

The R&D workflow of an AI-based product is inherently characterized by the experimental nature of the deep-tech research process. Adding to the challenges of edge technology - working on various ARM-based SOMs with multiple GPUs and DSP types, the inevitable conclusion is that a bespoke R&D methodology is required. This talk discusses SightX AI's design and successful application of an end-to-end MLOPS methodology. The proposed design enabled us to tackle the management of deep learning research aimed to be deployed on various platforms and to become faster and better with every version release. SightX AI recently added a feedback loop to this methodology which gets us a step closer to the holy grail of automated and continuously learning R&D workflow for edge AI.