In this article, you will be reading about the list of 10 MLOps platforms to improve ML projects.

MLOps is the intersection of machine learning, DevOps, and data engineering. It's a set of methods for automating the lifecycle of ML algorithms in production. MLOps platforms are increasingly being applied to ML models and the teams that build them to optimize and standardize the procedures that go into model lifecycle management. MLOps offers exciting opportunities for advancement and standardizing best practices in machine learning development. MLOps is a set of practices that teams of data scientists and IT engineers obey to increase the speed of machine learning models' deployment in real-world projects. Read on to learn about some of the top 10 MLOps platforms on the market.

Amazon SageMaker: It is a leading MLOps platform for many reasons, but its focus on monitoring and drift management helps teams most. SageMaker accelerates your experiments with purpose-built tools, including labeling, data preparation, training, tuning, hosting monitoring, and much more.

MLflow: MLflow is an open-source platform for controlling the overall workflow of a machine learning pipeline. It supports experimentation, reproducibility, and deployment of various ML models alongside maintaining a central model registry with the help of four components: MLflow Tracking, Projects, Models, and Model Registry.

Domino Data Lab: This platform is a popular platform for teams that focus on data management, especially because it focuses on creating centralized storage and visualization spaces for MLOps data. Domino track experiments, reproduce and compare results and find, discuss, and re-use work in one place.

Azure Machine Learning: it is a cloud-based platform that can be that train, deploy, automate, manage, and monitor all your ML experiments platform. This platform supports both Python, R, Jupyter Lab, and R studios with automated ML.  

HPE Ezmeral: It offers operational machine learning at an enterprise scale using containers. It is a Hewlett Packard service that offers machine learning operations at the enterprise level. It provides a container-based solution for the ML lifecycle – build, train, deploy and monitor the ML models.

Metaflow: Metaflow helps you design your workflow, run it at scale, and deploy it to production. It versions and tracks all your experiments and data automatically. Metaflow was recently open-sourced by Netflix and AWS. It can integrate with Amazon SageMaker, Python-based machine learning and deep learning libraries, and big data systems.

Google Cloud AI Platform: It is an end-to-end fully managed platform for machine learning and data science. It has features that help you manage service faster and seamlessly. This platform allows training models using a wide range of different customization options

Iguazio: It includes many of the same features that other full-service MLOps platforms advertise, but it particularly shines with its feature engineering solutions. This Platform transforms AI projects into real-world business outcomes. Accelerate and scale development, deployment, and management of AI applications with end-to-end automation of ML pipelines.

Cloudera Data Platform: It is a platform with several subcategories, such as Machine Learning and Shared Data Experience. It is a hybrid data cloud designed for any cloud, any analytics, and any data.

Paperspace: It is a high-performance cloud computing and ML development platform for building, training, and deploying machine learning models. It has a cloud-hosted web UI for managing your projects, data, users, and account.