Cortex Labs is Joining Databricks to Accelerate Model Serving and MLOps

As enterprises grow their investments in data platforms, they increasingly want to go beyond using data for internal analytics and start integrating predictions from machine learning (ML) models to create a competitive advantage for their products and services. For example, financial institutions deploy ML models to detect fraudulent transactions in real-time, and retailers use ML models to personalize product recommendations for each customer.

These mission-critical applications require an MLOps platform that can scale to process millions of predictions per second at low latency and with high availability while providing visibility into how models are performing in production. This becomes even more of a challenge with compute-intensive deep learning models that power natural language processing and computer vision applications.

To accelerate model serving and MLOps on Databricks, we are excited to announce that Cortex Labs, a Bay Area-based MLOps startup, has joined Databricks. Cortex Labs is the maker of Cortex, a popular open-source platform for deploying, managing, and scaling ML models in production. Cortex Labs was backed by leading infrastructure software investors Pitango Venture Capital, Engineering Capital, Uncorrelated Ventures, at.inc/, and Abstraction Capital, as well as angels Jeremey Schneider and Lior Gavish.

Cortex enables engineers and data scientists to deploy ML models in production without worrying about DevOps or cloud infrastructure. Companies from cybersecurity, biotechnology, retail, and other industries use Cortex to scale production ML workloads reliably, securely, and cost-effectively.

Databricks already provides advanced capabilities for developing models, and with the team behind Cortex, Databricks will augment its platform with capabilities to scale and monitor ML workloads in production. We’re thrilled to welcome Co-founders Omer Spillinger and David Eliahu to the Databricks team. Together, we’ll be working to realize our shared vision of an end-to-end, multi-cloud platform that empowers enterprises to deliver machine learning applications to their customers. Stay tuned for more updates!

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