Succeeding in AI – how well-managed AI companies do infrastructure right

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 you 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 life cycle for enterprises. It allows you to run experiences and work with any library, framework, or language. The main benefits are experiment tracking (so you can see how your models are doing between experiments), model management (to manage all versions of your model between teammates), and model deployment (to have a quick view of deployment in view in the tool).
• Google has TensorFlow Extended (TFX): This is Google’s newest product built on TensorFlow and it’s an end...