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| Description | MLflow is a futuristic machine learning platform that helps developers manage the entire machine learning lifecycle in an efficient manner. In addition, it also helps users to proceed with experimentation, deployment, and reproducibility of codes. Currently, MLflow offers four components to individual users. The first component is Tracking, which helps developers with logging parameters, metrics, code versions, and output files while running the machine learning code and result analytics part. The second component is Projects that include a command-line and other API tools facilitating seamless management of projects in progress. This also makes it possible for developers to bring all projects with a single workflow. The third component being Models, which can be used for packaging machine learning models, besides proceeding with deployment in diverse serving environments. Finally, Registry- a centralized model serves as a store for APIs and UI, besides helping out users to proceed with the full life cycle of an MLflow Model. Real-time integration facilities with Kubernetes, Google Cloud, and TensorFlow, enables seamless business management. Read more | Azure Machine Learning helps Empower developers and data scientists with a wide range of productive experiences for building, training, and deploying machine learning models faster. Accelerate time to market and foster team collaboration with industry-leading MLOps—DevOps for machine learning. Innovate on a secure, trusted platform, designed for responsible ML. Read more |
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MLflow Custom |
Azure Machine Learning Studio Custom |
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| Company Details | Located in: San Francisco, California | Located in: Redmond, Washington |
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Not available https://azure.microsoft.com/en-us/services/machine-learning/ |
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