Developing Your Enterprise Machine Learning Vocabulary

We are in Rome, Italy today speaking on the development of a machine learning vocabulary. This is not a topic you might think Streamdata.io would be present at, but machine learning is in fact a growing part of our customer base. Our streaming, and event-driven technology is increasingly being used to train and produce very fine tuned machine learning capabilities across the enterprise, making the topic of AI and ML something we are increasingly paying attention to.

Machine Learning models aren’t massive main frame, or server technologies anymore. Increasingly machine learning models are trained and executed in a containerized environment, and orchestrated using solutions like Kubernetes. We find our customers using our event-driven solutions to create very fine tuned, topical based streams that are used to grow data lakes that are used to train machine learning models, or deliver real-time streams directly into models that are rebuilt every hour, or based upon other time series factors, schedules, or events.

Our talk in Rome is focused on developing of a machine learning vocabulary level so that you can more precisely train your ML models, and then organize and execute upon the ML capabilities you have developed effectively. Something that we find maps directly to other data and content based API discovery and run-time vocabularies, as well as can be used to develop and evolve your event-driven infrastructure topics, tags, and vocabularies. Providing a coherent framework that can be used to discover and organize data made available via APIs, and used to distribute data to data lakes, and define the capabilities being developed as part of machine learning efforts.

We find that enterprise organizations that can’t clearly articulate the data sources they have, aren’t able to also clearly articulate their machine learning strategy. Something that leaves enterprise organizations unable to be as agile as they need to be in the industries they operate within, but also leave them prey to machine learning and artificial intelligence vendors who aren’t selling real world solutions, and just looking to get access to valuable enterprise assets. Which is why we are on the road, helping enterprise architects and business leaders understand the importance of having a vocabulary to help them organize their data, and train, organize, and deliver meaningful machine learning infrastructure in real-time.