JFrog to Acquire Qwak AI

JFrog announced it has entered into a definitive agreement to acquire Qwak AI Ltd., creators of the AI and MLOps platform. With the acquisition, JFrog aims to offer a unified and scalable solution for DevOps, Security, and MLOps stakeholders. This advanced, industry-leading MLOps functionality is designed to free data scientists and developers from infrastructure concerns, accelerating the creation and delivery of AI-powered applications. JFrog is the single system of record for all software packages (binaries), including models stored in Artifactory. Enhancing its machine learning (ML) model capabilities will enable users to streamline models from development to deployment.

Shlomi Ben Haim, CEO and Co-founder of JFrog, said, “Next-generation Software Supply Chain platforms will need to expand and natively include MLOps solutions to better serve development organizations. We’re excited to combine Qwak’s MLOps solution with our platform to empower our customers’ AI journey. Qwak’s solution, powered by JFrog Artifactory as the model registry of choice and JFrog Xray for scanning and securing ML models, will enhance user efficiency and provide a unified platform experience for DevOps, DevSecOps, MLOps, and MLSecOps. We’re looking forward to leaping higher with Qwak’s team!” Ben Haim added.

As part of the JFrog Platform, Qwak technology will deliver a straightforward and hassle-free user experience for bringing models to production, combined with the level of trust and provenance enterprises expect from JFrog as they deliver AI-powered applications. This combination leverages Qwak’s advanced model training and serving capabilities to manage the previously-siloed and complex lifecycle of models, alongside model storage management and security scanning of models provided by JFrog.

The acquisition follows a successful integration between JFrog and Qwak solutions announced earlier this year, based on JFrog’s “model as a package” approach. The holistic solution aims to eliminate the need for separate tools, separate compliance efforts and will offer full traceability in a single solution.

Alon Lev, CEO and Co-Founder of Qwak, said, “We’re beyond excited to join the JFrog family and to help customers accelerate their AI initiatives. Our founding vision for Qwak was to change the way software development teams and Data Scientists work together to bring AI assets into production. With the power of the JFrog Software Supply Chain Platform to deliver secure software components at scale, we’re creating a whole new experience that will pave the way for unified digital delivery teams to bring responsible, secured models into their applications much more simply and predictably.”

Speed to market and a secure flow of ML models – the fuel behind artificial intelligence – is the key driver behind modern MLOps initiatives as companies attempt to deliver AI-powered applications. According to Gartner, MLOps plays a critical role in the operationalization of AI, with 75% of companies shifting from piloting to operation of AI by the end of 2024 (Gartner Top 10 Trends in Data and Analytics, 2020 [client access only]).

“Data scientists and ML engineers currently use tools that are mostly disconnected from standard DevOps and Security processes within companies, delaying release timeframes and eroding trust,” said Gal Marder, Executive Vice President of Strategy, JFrog. “A unified system of record across Dev, Sec, ML and Ops will alleviate this pain for digital teams and the business.”

Today’s market demands a single platform experience across the software supply chain to accelerate development processes and that treats the fuel of AI – machine learning models and their metadata – accordingly. Like any other software component, ML models must be stored, built, traced, versioned, signed, secured and efficiently delivered across systems in order to deliver AI at scale. Utilizing DevOps practices in a unified solution addresses these market expectations.

The acquisition of Qwak will expand JFrog solutions with the following capabilities:

• One Platform for DevSecOps & MLSecOps, offering a holistic ML software supply chain from traditional models to LLMs and GenAI
• Fast and straightforward model serving into production with simplified model development and deployment and serving processes, improving AI initiatives
• Model training and monitoring with OOTB dataset management and feature store support
• Manage models as a package allowing you to version, manage, and secure models the same way you do any other software package with DevSecOps best practices
• Ensure provenance and security of AI naturally in the development workflows
• Pull from a governed, secure source of truth that marries ML models with the other building blocks such as containers and Python packages
• Trace models back to their source for easy recall, retraining and redeployment if something goes wrong with production models