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Last updated : Apr 13, 2021
Not on the current edition
This blip is not on the current edition of the Radar. If it was on one of the last few editions it is likely that it is still relevant. If the blip is older it might no longer be relevant and our assessment might be different today. Unfortunately, we simply don't have the bandwidth to continuously review blips from previous editions of the Radar Understand more
Apr 2021
Trial ? Worth pursuing. It is important to understand how to build up this capability. Enterprises should try this technology on a project that can handle the risk.

MLflow is an open-source tool for machine-learning experiment tracking and lifecycle management. The workflow to develop and continuously evolve a machine-learning model includes a series of experiments (a collection of runs), tracking the performance of these experiments (a collection of metrics) and tracking and tweaking models (projects). MLflow facilitates this workflow nicely by supporting existing open standards and integrates well with many other tools in the ecosystem. MLflow as a managed service by Databricks on the cloud, available in AWS and Azure, is rapidly maturing, and we've used it successfully in our projects. We find MLflow a great tool for model management and tracking, supporting both UI-based and API-based interaction models. Our only growing concern is that MLflow is attempting to deliver too many conflating concerns as a single platform, such as model serving and scoring.

Oct 2020
Trial ? Worth pursuing. It is important to understand how to build up this capability. Enterprises should try this technology on a project that can handle the risk.
Veröffentlicht : Oct 28, 2020
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