Top 7 MLOps Platforms to Consider Trying in 2024

MLOps, or Machine Learning Operations, is a rapidly growing field that focuses on the deployment, monitoring, and management of machine learning models in production. As more and more companies adopt machine learning to drive business decisions, the need for robust MLOps platforms has become increasingly important. In this article, we will explore the top 7 MLOps platforms to consider trying in 2024.

1. DataRobot
DataRobot is a leading MLOps platform that offers a comprehensive suite of tools for building, deploying, and managing machine learning models. With features such as automated model selection, hyperparameter tuning, and model monitoring, DataRobot makes it easy for data scientists and engineers to streamline the machine learning workflow.

2. Databricks
Databricks is a cloud-based MLOps platform that is built on top of Apache Spark. It offers a unified analytics platform that allows users to build and deploy machine learning models at scale. With features such as automated machine learning, model versioning, and model serving, Databricks is a popular choice for companies looking to accelerate their machine learning projects.

3. MLflow
MLflow is an open-source MLOps platform that was developed by Databricks. It provides a set of tools for tracking experiments, packaging code, and deploying models. With MLflow, data scientists can easily collaborate on machine learning projects and track the performance of their models over time.

4. Kubeflow
Kubeflow is an open-source MLOps platform that is built on top of Kubernetes. It provides a set of tools for building, deploying, and managing machine learning models in a scalable and efficient manner. With features such as model serving, hyperparameter tuning, and model monitoring, Kubeflow is a popular choice for companies looking to deploy machine learning models in production.

5. Seldon
Seldon is a cloud-based MLOps platform that offers a range of tools for deploying and managing machine learning models. With features such as model serving, model monitoring, and model explainability, Seldon makes it easy for companies to deploy machine learning models at scale.

6. TFX
TFX is an open-source MLOps platform that was developed by Google. It provides a set of tools for building, deploying, and managing machine learning models in production. With features such as data validation, model analysis, and model serving, TFX is a popular choice for companies looking to streamline their machine learning workflow.

7. H2O.ai
H2O.ai is a leading MLOps platform that offers a range of tools for building, deploying, and managing machine learning models. With features such as automated machine learning, model interpretability, and model monitoring, H2O.ai makes it easy for companies to deploy machine learning models in production.

In conclusion, the field of MLOps is rapidly evolving, and there are many platforms available to help companies streamline their machine learning workflow. The top 7 MLOps platforms mentioned in this article are just a few of the options available to companies looking to deploy machine learning models in production. By considering these platforms in 2024, companies can stay ahead of the curve and drive business value through machine learning.

• Source Link: https://zephyrnet.com/7-end-to-end-mlops-platforms-you-must-try-in-2024-kdnuggets/