DataRobot has recently acquired Agnostic, which offers an open-source platform called Covalent that focuses on distributed computing. This integration will enhance DataRobot’s machine learning operations by providing IT teams with a serverless framework. Covalent allows organizations to effectively manage resources across various platforms while simplifying cluster management and prioritizing processes based on cost and performance metrics. As AI applications multiply, the ability to allocate IT resources strategically becomes crucial. This move aims to bridge the gap between data science teams that create AI models and IT teams managing their deployment, ensuring that AI can be deployed efficiently across different environments, whether in the cloud or on-premises.
DataRobot Expands AI Capabilities with Agnostic Acquisition
In a significant move to enhance its artificial intelligence (AI) offerings, DataRobot has acquired Agnostic, a company known for its open-source distributed computing platform called Covalent. This acquisition aims to integrate Covalent with DataRobot’s machine learning operations (MLOps) framework, promising to provide organizations with a robust solution for developing and deploying AI applications.
Covalent is a Python-based orchestration platform that allows IT teams to utilize a serverless computing framework. This helps maximize the use of IT infrastructure efficiently. Venky Veeraraghavan, DataRobot’s Chief Product Officer, emphasizes that Covalent will support deploying applications at scale across hybrid IT environments. This platform simplifies managing clusters running technologies like Kubernetes, Slurm, or Nomad, thus giving IT teams a higher level of abstraction.
One critical feature of Covalent is its ability to set rules based on cost and latency thresholds. This will enable organizations to prioritize specific processes effectively. As businesses increasingly build AI agents that operate on various processors, controlling IT resource allocation becomes essential. Covalent ensures organizations remain flexible and not locked into a single infrastructure platform.
Agnostic reports over 5,000 users of Covalent, which has been downloaded more than 140,000 times. While many data science teams initially deployed it, the shifting responsibility for managing AI inference engines to IT teams signifies a growing demand for better integration between AI deployment and existing DevOps workflows.
The number of AI applications ready for production is expected to increase rapidly in the coming months, with many needing to be deployed on-premises or at the network edge to meet latency requirements. Organizations will face challenges in monitoring these AI models to ensure they remain accurate as they are exposed to new data.
As companies explore ways to merge MLOps and DevOps practices, they will undoubtedly encounter both technical and cultural hurdles. However, with solutions like Covalent, DataRobot aims to bridge the gap between data science and IT teams, paving the way for a smoother AI deployment experience.
Tags: DataRobot, Agnostic, AI Applications, Machine Learning, MLOps, Covalent, IT Infrastructure.
What is the main reason DataRobot acquired Agnostic?
DataRobot acquired Agnostic to enhance its capabilities in building AI applications. The goal is to leverage Agnostic’s Distributed Covalent Platform, which helps in developing and deploying AI solutions more effectively.
How will the acquisition benefit DataRobot’s customers?
Customers of DataRobot will gain access to advanced tools and features for AI app development. This will make it easier and faster to create, manage, and scale AI applications, improving productivity and efficiency.
What is the Distributed Covalent Platform?
The Distributed Covalent Platform is a technology that allows developers to build AI applications in a flexible and efficient way. It supports various AI models and makes it easier to integrate them into different systems and environments.
Will this acquisition change how DataRobot operates?
While DataRobot will maintain its existing operations, the acquisition will introduce new technologies and features. This means that users can expect improved services and more options for building AI apps.
What is the impact of this acquisition on the AI industry?
This acquisition signifies a trend in the AI industry towards combining resources and technologies. It strengthens DataRobot’s position in the Market and could lead to more innovations in AI application development on the whole.
