Succeeding in AI

AI companies’ efficient infrastructure

It’s indicative of the complexity of ML systems that many large technology companies that depend heavily on ML have dedicated teams and platforms that focus on building, training, deploying, and maintaining ML models. The following are a few examples of options we can take when building an ML/AI program:

Databricks has MLflow

MLflow is an open-source platform developed by Databricks to help manage the complete ML lifecycle for enterprises. It allows us to run experiences and work with any library, framework, or language. The main benefits are experiment tracking—so we can see how our models are doing between experiments, model management—to manage all versions of our model between teammates, and model deployment---to have a quick view of deployment in view in the tool.