JFrog to Acquire Qwak AI to Streamline AI Models from Development to Production

JFrog's solution will be expanding to deliver advanced MLOps capabilities to organizations, enabling building, deployment, management, and monitoring of AI workflows from GenAI and LLMs to classic ML models, all within a unified platform.

JFrog Ltd. (Nasdaq: FROG), the Liquid Software company and creators of the JFrog Software Supply Chain Platform that offers DevOps and DevSecOps solutions, today announced it has entered into a definitive agreement to acquire Qwak AI Ltd., creators of the AI and MLOps platform.

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Union will expand JFrog's Platform to deliver advanced MLOps capabilities to organizations, enabling them to build, deploy, manage, and monitor AI workflows. (Graphic: Business Wire)

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.

“Next-generation Software Supply Chain platforms will need to expand and natively include MLOps solutions to better serve development organizations,” said Shlomi Ben Haim, CEO and Co-founder of JFrog. “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.

“We’re beyond excited to join the JFrog family and to help customers accelerate their AI initiatives,” said Alon Lev, CEO and Co-Founder of Qwak. “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

JFrog’s MLOps Road Map

As part of the acquisition and integration process, JFrog plans to assimilate Qwak’s talent into JFrog, rapidly growing the MLOps-centric team. JFrog will also accelerate the process of technology integration to bring Qwak technology into the JFrog Platform, across JFrog DevOps and Security products. JFrog and Qwak will work together with customers to ensure business continuity and streamlined migration to future jointly-developed and supported offerings.

MLOps Ecosystem & Partner Integrations

Earlier this year, JFrog announced integrations with AWS Sagemaker and DataBricks-developed MLflow. As part of the company’s approach to universality, JFrog will continue to deliver integrations with other leading MLOps ecosystem partners to provide developers and ML Engineers with the freedom of choice and avoiding vendor lock-in.

For a deeper look at how the union of JFrog and Qwak is expected to help build, train, secure, deploy and monitor ML models and GenAI in a unified experience, visit our solution page, read this blog and join us for a webinar the week of July 22.

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About JFrog

JFrog Ltd. (Nasdaq: FROG) is on a mission to create a world of software delivered without friction from developer to device. Driven by a “Liquid Software” vision, the JFrog Software Supply Chain Platform is a single system of record that powers organizations to build, manage, and distribute software quickly and securely, to aid in making it available, traceable, and tamper-proof. The integrated security features also help identify, protect, and remediate against threats and vulnerabilities. JFrog’s hybrid, universal, multi-cloud platform is available as both self-hosted and SaaS services across major cloud service providers. Millions of users and 7K+ customers worldwide, including a majority of the Fortune 100, depend on JFrog solutions to securely embrace digital transformation. Once you leap forward, you won’t go back! Learn more at jfrog.com and follow us on X (formerly Twitter): @jfrog.

Cautionary Note About Forward-Looking Statements

This press release contains “forward-looking” statements, as that term is defined under the U.S. federal securities laws, including, but not limited to, statements regarding JFrog’s future financial performance, our reiterated guidance for the quarter ending June 30, 2024 and fiscal year ending December 31, 2024, our leadership position in the markets in which we participate, our ability to meet market demands and our expectations regarding the proposed acquisition of Qwak by us, including our ability to complete the potential transaction, the proposed timetable for completing the potential transaction, our ability to successfully integrate the proposed acquisition into our business operations, including the DevOps platform, and realize anticipated benefits and synergies from the proposed acquisition.

These forward-looking statements are based on our current assumptions, expectations and beliefs and are subject to substantial risks, uncertainties, assumptions and changes in circumstances that may cause JFrog’s actual results, performance or achievements to differ materially from those expressed or implied in any forward-looking statement. There are a significant number of factors that could cause actual results, performance or achievements, to differ materially from statements made in this press release, including but not limited to risks detailed in our filings with the Securities and Exchange Commission, including in our annual report on Form 10-K for the year ended December 31, 2023, our quarterly report on Form 10-Q for the quarter ended March 31, 2024, and other filings and reports that we may file from time to time with the Securities and Exchange Commission. Forward-looking statements represent our beliefs and assumptions only as of the date of this press release. We disclaim any obligation to update forward-looking statements except as required by law.

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