ClearML Announces General Availability for Enterprise
ClearML, a unified MLOps platform, has announced the general availability of ClearML for enterprise customers.
The platform was previously offered as invite-only to a select group of customers but is now broadly available to enterprise organizations in industries such as healthcare, retail, and manufacturing.
According to the company, ClearML was purpose-built for MLOps and empowers teams to execute, manage, monitor, audit, and analyze the entire MLOps process from one fully integrated platform with only two lines of code.
The company’s website explains how it works: “ClearML automates task creation as you code and with only a 2 lines-of-code integration, both outputs (Console/TB/Matplotib, etc.) and development environment (Git/Uncommitted changes/Python packages/Args, etc.) are automatically logged.” Tasks within the experiment manager can be cloned, modified, and placed in an execution queue for a remote ClearML agent to pull, set up the environment, and execute the code while monitoring the process.
“ClearML is proud to be the only unified, end-to-end, frictionless MLOps platform supporting enterprises,” said Moses Guttmann, CEO and co-founder of ClearML, previously known as Allegro AI. “In a category dominated by closed point solutions and fragmented semi-platforms, ClearML delivers an open-sourced, comprehensive offering that enables companies to scale their MLOps while successfully bridging the innovation and revenue gaps with our unified end-to-end platform.”
ClearML lists the key features of the platform as follows:
- ClearML Experiment – ClearML Experiment allows users to track each part of the ML experimentation process and automate tasks. With it, users can log, share and version all experiments and instantly orchestrate pipelines.
- ClearML Orchestrate – With ClearML Orchestrate, DevOps and data scientists are empowered through autonomy and control over compute resources. The cloud native solution also enables Kubernetes and bare-metal resource scheduling with a simple and unified interface to control costs and workloads.
- ClearML DataOps – ClearML DataOps delivers data store automation. Automate data collection into searchable, accessible, and ML-ready data repositories through cutting-edge MLOps technology.
- ClearML Hyper-Datasets – ClearML Hyper-Datasets allow MLOps teams to build data-centric AI workflows. Make the most out of unstructured data using queryable datasets, made possible through ClearML Hyper-Datasets.
- ClearML Deploy – ClearML Deploy delivers a unifying model repository, custom pipelines, and model serving. This allows MLOps teams to Transition from model development to production and gain full workflow visibility with seamless integration to the experiment manager and orchestration.
The company says each component of ClearML seamlessly integrates with the others to deliver cross-department visibility in R&D and production. ClearML is also open source and available for mid-market organizations as an a la carte offering.
“Many machine learning projects fail because of closed-off, point tools that lead to an inability to collaborate and scale,” said Guttmann. “Customers are forced to invest in multiple tools to accomplish their MLOps goals, creating a fragmented experience for data scientists and ML engineers. Through our offerings, customers experience the full potential and business impact of machine learning.”