The Top 10 AI Machine Learning Operations (MLOps) Solutions

Anyscale is a leading MLOps company that delivers scalable, open-source frameworks for rapid AI development, and a fully managed platform for Ray. This is an open-source ML project, used by AI market leaders OpenAI to train ChatGPT and other large-scale ML models with millions of users. The company was founded in 2019 and is based in San Francisco. They have raised $99m USD in investment to-date.=

Anyscale enables teams to create highlight scalable ML deployments, assisted with automatic detection of bugs and rapid remediation. Anyscale’s managed platform enables teams to build, deploy, and manage highly scalable AI and Python applications. It provides an intuitive user experience for developers and AI teams, enabling faster deployments and more scalable workloads.

Aporia enables teams to create high performing AI models, enabling monitoring, and visualization over the development process. Aporia is trusted by leading brands, including Nvidia, and has raised over $25m USD in funding to-date. The company was founded in 2019 and is based out of San Jose, California.

Aporia’s customizable dashboards provide detailed visibility into model performance, with insights into missing values, potential losses caused by incorrect model predictions, and more to help teams ensure models are performing as they should. Aporia enables teams to quickly discover biases and data integrity issues, which can occur in even the top performing AI models. Deployment is straightforward, and organizations can also configure custom metrics to track goals aligned to business objectives.

Azure Machine Learning is Microsoft’s leading platform for data scientists and developers to create, launch, and manage ML models. It also provides integrations with applications and open-sources tools to help teams create responsible AI applications.

With Azure Machine Learning, teams can prepare data and datasets with analytics, build and train models with automated machine learning, and create drag and drop development interfaces. Teams can create and run experiments and built-in tools from open-source libraries and frameworks. Finally, teams can validate and deploy their tools with ongoing management and monitoring. A key benefit of Microsoft’s offering is their security and compliance expertise – they have more than 3,500 internal security experts.

Comet is a machine learning platform that enables development teams to manage, visualize, streamline, and optimize their AI models across the whole machine learning model lifecycle. Comet is trusted by thousands of users and by multiple Fortune 100 companies, including Shopify and Uber. To-date, Comet has raised more than $50m USD in investment. The company was launched in 2017 and is headquartered in New York City.

Comet helps AI teams to build reliable AI models, enabling teams to track, compare, deploy, and optimize their models easily and efficiently, with insights and data to improve productivity and visibility. The tools easily integrate with your ML environment, allowing you to quickly start tracking code and metrics. Teams can create their own custom data visualizations, manage models, trigger deployments, and monitor models once they are in production.

Galileo is a data intelligence platform powered by machine learning, designed for data scientists working with unstructured data. This platform allows teams to instantly debug, track, optimize, and monitor machine learning data, from initial training through to production. Galileo powers ML teams for both start-ups and Fortune 500 companies like Buzzfeed, Google AI, and Uber. Galileo launched in 2021 and is based out of San Francisco. They have raised $18m USD in investment to-date.

Galileo’s platform provides a range of tools to enable teams to quickly find and fix data errors, reducing time spent going through data and debugging. It automatically shifts through data and flags error patterns and data gaps within your models. The intuitive dashboards enable you to compare and monitor runs in one location and easily collaborate or share reports across your team.

Iterative supports machine learning model development for teams, with a range of developer tools. They aim to reduce the complexity of managing large ML data sets and infrastructure and improve lifecycle management. Iterative is trusted by hundreds of ML teams of all sizes, from start-ups to the Fortune 500. Customers vary from banks such as UBS, to the United Kingdom Hydrographic Office. Iterative was founded in 2021 and is headquartered in San Francisco. To-date they have raised $20m USD in investment.

With Iterative, teams can automate workflows, improve collaboration, and more easily manage data cross the whole ML lifecycle. Teams can develop their models and automate training, with built-in collaboration tools and custom dashboards to help improve productivity. Iterative also helps teams track and monitor the progress of their projects and experiments, ensuring that compliance and regulatory requirements are being met.

LightningAI is an MLOps platform that enables teams to build and publish workflow templates and modules, allowing for more efficient, optimized ML development cycles and reducing costs for developers. LightningAI, formerly known as PyTorch Lightning, is headquartered in New York, and was launched in 2019. To-date, the company has raised $59m USD in funding.

LightningAI is a community-based service, that allows teams and start-ups to build AI-powered products from community-based app and component templates. Using the platform, you can train, test, and deploy models with fully customizable and modular pre-built components, to improve speed and performance of models and reduce costs. The service also provides enhanced security controls and auditing to reduce potential risks.

OctoML automates and streamlines ML deployment for any hardware, allowing teams to deploy faster and with reduced costs. The platform is used by leading AI and ML teams across the world, including companies such as AWS, Sony, Microsoft, and VMWare. OctoML has been backed by leading venture capital firms, raising $132m USD in funding to-date. The company was launched in 2019 and is headquartered in San Francisco.

OctoML enables teams to build intelligent applications, while improving efficiency and spend. The platform allows for much faster development cycles, removing the need to hand-tune models, and optimizing to reduce latency, thereby improving the end-user experience. The tool can hugely improve speeds and reduce workload costs by automating hardware independence.

Robust Intelligence is a software provider that creates end-to-end tools to improve the reliability and performance of AI models. Robust Intelligence allows organizations to rapidly develop intelligent applications, and the platform is trusted by ML developers at PayPal, Expedia, and the US Department of Defense. Founded by data scientists in 2019, Robust Intelligence has raised $59m USD in backing to-date. The company is based out of San Francisco, CA.

Robust Intelligence’s platform integrates into ML models to detect vulnerabilities, highlight incorrect data, and detect data issues. It helps to “stress test” AI models before deployment, enabling teams to discover any weaknesses and remediate them, which, combined with continuous testing over time, helps to ensure ML integrity. The platform is automated to help you to rapidly build production ready AI models, with autogenerated reports and insights to ensure compliance, prevent production failures, and track custom business metrics over time.

Weights & Biases is an MLOps platform that helps developers to build ML models faster, with improved experiment tracking, dataset versioning, and model management. Weights & Biases is a market leader in the space, trusted by over 400,000 ML practitioners including teams at OpenAI, Lyft, Samsung, and Toyota. Weights & Biases was founded in 2017 and is headquartered in San Francisco. To-date, they have raised $200m USD in funding.

Weights & Biases offers developers tools to make models scalable and accurate by optimizing, visualizing, and standardizing models and datasets across all frameworks, environments, and workflows. Key use cases for this service include experiment tracking, generating collaborative dashboards, dataset, and model versioning, hyperparameter optimization, and automating ML workflows. The platform can be quickly integrated and is free for academic and open-source projects.