Valohai is an MLOps platform that automates and manages machine learning operations at scale. It supports the entire machine learning workflow from data preparation to deployment.
Paperspace Gradient is a cloud computing platform offering a suite of tools to support machine learning and AI workflows, facilitating the management of AI infrastructure with ease. It provides scalable compute resources and an intuitive interface for model development and deployment.
Domino Data Lab provides an enterprise MLOps platform that accelerates research, increases collaboration, and optimizes the lifecycle of data science models. It is designed to manage and scale data science work and infrastructure seamlessly in enterprises.
CNVRG.io is a full-stack machine learning platform that helps manage and automate AI infrastructure, enabling the deployment and monitoring of models at scale.
DataRobot MLOps provides AI infrastructure management, helping organizations deploy, monitor, and manage machine learning models in production environments efficiently.
Seldon provides an open-source platform for deploying, scaling, and managing machine learning models through Kubernetes. It enables organizations to integrate machine learning models into their existing infrastructure seamlessly.
Algorithmia provides an AI-based infrastructure management platform that focuses on deploying, managing, and scaling AI/ML models. It serves as a marketplace and service for AI models and algorithms, facilitating seamless integration of AI capabilities into existing applications.
Determined AI provides an open-source deep learning training platform that makes building models fast and easy, allowing developers to train models efficiently at scale with powerful tools for hyperparameter tuning, distributed training, and more.
Run:ai provides an AI-driven platform for simplifying and accelerating AI infrastructure management. This solution allows organizations to manage and optimize compute resources for AI workloads, improving efficiency and reducing costs.
Qubole is a cloud-based data platform that provides AI-driven solutions for managing and optimizing data processing infrastructure. It helps in automating and scaling big data workloads, making it ideal for AI infrastructure management.
Spell is an AI-focused infrastructure management platform that provides tools for training and deploying machine learning models. It offers collaborative workspaces and automated workflows to streamline the development process.
MLflow is an open-source platform for managing the complete machine learning lifecycle, including experimentation, reproducibility, and deployment. It is widely used for tracking experiments, packaging code into reproducible runs, and sharing and deploying models. It supports any machine learning library or algorithm and can be run on any cloud platform.
Kubeflow is an open-source platform designed to make deployments of machine learning (ML) workflows on Kubernetes simple, portable, and scalable. It aims to provide a straightforward way to deploy best-of-breed open-source systems for ML to diverse infrastructures.
H2O.ai provides an open-source AI platform that supports big data and machine learning applications. It is designed to help businesses streamline their AI model deployment and management processes.
Grid.ai provides scalable and efficient infrastructure for machine learning teams, allowing them to easily train large models on the cloud with minimal configuration. It focuses on simplifying AI infrastructure management and optimizing resource usage.
Flyte is a structured programming and distributed processing platform that enables highly concurrent, scalable, and maintainable workflows for machine learning and data processing. It is specifically designed to manage complex AI infrastructure efficiently.
ClearML is an open-source platform that provides tools for managing and automating the entire machine learning lifecycle, from data collection to model deployment. It is designed to streamline workflows with features like experiment management, version control, and scalable data processing.
Weights & Biases is a platform that provides tools for experiment tracking, model visualization, and collaboration for machine learning projects. It helps data teams to track their models, datasets, and experiments to build better models faster. The platform supports a wide range of machine learning frameworks and provides seamless integration with existing workflows.
Neptune.ai is a metadata store for MLOps, built for teams that run a lot of experiments. It is used to keep track of machine learning experiments, manage metadata, and improve collaboration among data scientists. This solution is tailored for research and production teams that need to control the experimentation process effectively.
Polyaxon is an AI infrastructure management platform that provides tools to manage, monitor, and optimize machine learning experiments and workflows. It is designed for data scientists and machine learning engineers to streamline their MLOps processes, enabling seamless collaboration and deployment of AI models.
KubeSphere is an open-source container platform that provides a powerful and flexible solution for managing cloud-native applications using Kubernetes. It integrates various AI capabilities to simplify DevOps workflows, microservices governance, and multi-cloud and multi-cluster management, making it an ideal choice for AI infrastructure management.
Cognite AI provides AI-driven solutions for industrial data management. It helps organizations in sectors like oil and gas, manufacturing, and utilities to optimize operations by integrating and analyzing big data from various industrial sources.
Azure Machine Learning is a cloud-based environment from Microsoft, which enables data scientists and developers to build, deploy, and manage machine learning models. It offers scalability, automation, and orchestration capabilities.
Dataiku is an advanced AI-based platform that provides an end-to-end solution for data preparation, machine learning, and MLOps. It helps businesses in managing AI infrastructure by offering tools for data scientists and engineers to develop, deploy, and monitor AI models efficiently.
NimbleBox.ai provides a complete platform for building, training, and managing machine learning models. It offers AI infrastructure management with tools for deploying and scaling AI models, making it easier for data scientists to focus on model development without worrying about the underlying infrastructure.
Cortex is an open-source platform for deploying machine learning models to production. It is designed to manage infrastructure for deploying models as APIs in production environments. The platform automates the infrastructure management tasks and scales models to handle production-level workloads efficiently.
Mesosphere DC/OS is an open-source distributed operating system that provides a platform for deploying, managing, and scaling applications and services. It is designed for data-intensive applications and IoT, offering support for containerized and non-containerized workloads, making it suitable for AI infrastructure management.