Best MLOps Platforms - Page 11

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Researched and written by Bijou Barry

Machine learning operationalization (MLOps) platforms allow users to manage and monitor machine learning models as they are integrated into business applications. In addition, many of these tools facilitate the deployment of these models. With these tools, businesses can implement machine learning models and algorithms built by data scientists and developers. MLOps software automates deployment, monitors models' health, performance, and accuracy, and iterates on those models. Some tools provide features for doing this collaboratively. This enables businesses to scale machine learning across the company and make a tangible business impact.

Additionally, these products may provide security, provisioning, and governing capabilities to ensure that only those authorized to make version changes or deployment adjustments can do so. The tools can differ regarding what part of the machine learning journey or workflow they focus on, including explainability, hyper optimization, feature engineering, model risk, model selection, model monitoring, and experiment tracking.

These tools are usually language agnostic, so they can be successfully deployed no matter how an algorithm is built. However, some may focus specifically on languages like R or Python, among others. Some of these products are dedicated to tracking machine learning experiments to better understand the performance of models. In addition, some products provide the ability to augment users’ training datasets in order to improve model training.

Some MLOps solutions offer a way to manage all machine learning models across the entire business in a single location. Although similar to data science and machine learning platforms, this software differs since it focuses on the maintenance and monitoring of models instead of deployment.

To qualify for inclusion in the MLOps Platforms category, a product must: