According to Cognilytica, the MLOps market is expected to grow by nearly $4 billion by 2025. MLOPs capabilities have been built into the platforms of Amazon, Google, Microsoft, IBM, H2O, Domino, DataRobot, and Grid.ai. 

The majority of businesses use MLOPs for pipeline automation, monitoring, lifecycle management, and governance. According to Algorithmia, nearly 22% of companies had machine learning models in production for 1-2 years last year, indicating a significant transition toward product ionization, even if most companies are still in the early stages. 

Algorithmia- Algorithmia is a single-platform solution for all stages of machine learning operations (MLOps) and management. It allows ML and operations teams to collaborate in one central location on complex machine learning applications. The platform is currently used by over 100,000 engineers and data scientists, including staff from the United Nations and a number of Fortune 500 companies.  

Comet ML- Data scientists and teams can use Comet to track, compare, explain, and improve experiments and models throughout their entire lifecycle. On GitHub, you can find all of the examples and libraries. 

DVC- For machine learning projects, DVC is an open-source’ version control system.’ It keeps track of data sets and machine learning models. The platform was created with the goal of making machine learning models more shareable and reproducible. Large files, data sets, machine learning models, metrics, and code are all handled by DVC. 

Kubeflow- The Kubeflow project aims to make ML workflow ‘deployments’ on Kubernetes as simple, portable, and scalable as possible. It includes components for every stage of the machine learning lifecycle, from discovery to training and deployment. 

Metaflow- Metaflow was created at Netflix to help data scientists working on a variety of projects, from classical statistics to SOTA deep learning, be more productive. It is a Python/R library that assists scientists and engineers in the development and management of real-world data science projects. 

MLFlow- MLFlow is an open-source platform that allows users to manage the machine learning lifecycle, including experimentation, reproducibility, deployment, and a central model registry. There are four parts to it right now: tracking, projects, models, and registry. 

Neptune.ai- Neptune is an MLOps metadata store designed for teams that conduct a lot of experiments. All model building metadata can be logged, stored, displayed, organised, compared, and queried in one place. The MLOps platform is also used for experiment tracking, model registry, and live monitoring of machine learning runs. 

Polyaxon- Polyaxon is a Kubernetes machine learning platform (also used as MLOps tools for experimentation and automation). Large-scale deep learning applications can be built, trained, and monitored using the platform. Polyazon manages workloads with smart container and node management, making deep learning application development faster, easier, and more efficient. It transforms GPU servers into self-service resources for both individuals and businesses. 

Valohai- Valohai is a modeller, programmer, and data analyst. It enables users to run powerful cloud machines with just a single click (UI) or command (CLI & API). It can be set up on any cloud vendor or on-premise to automatically orchestrate machines. 

Weights & Biases- Weights & Biases is a machine learning ‘developer tool.’ Users can build better models faster with this feature, which also includes experiment tracking, dataset versioning, and model management. 

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