Israeli startup Qwak, which provides enterprises with an conclude-to-conclusion MLOps system to construct and deploy designs at scale, right now declared $12 million in a fresh round of funding. The enterprise plans to use the funds to further create its product and sooner or later established up a “machine-mastering cloud” for enterprises.
While equipment learning (ML) has been a speaking issue for a long time, the calendar year 2022 observed it go mainstream with the launch of generative AI purposes like Dall-E, MidJourney and ChatGPT. Enterprises these days are aggressively racing to build ML types to unlock value throughout features, be it serious-time customer guidance, fraud detection or defining a pricing tactic.
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However, when it comes to actually building high-performing models and integrating them into products, things get complicated. Data science teams have to deal with a highly fragmented environment where they have to integrate with different stakeholders like DevOps and data engineers and utilize specialized tools to build a simple model pipeline. This takes up significant time and resources – to the point where many projects do not even make it to production. And, for the few models that make it to the production stage, deployment can take a long time, followed by the immediate need to constantly monitor them for quality and efficiency.
Alon Lev, who previously led as the VP of data at Payoneer, saw similar challenges and found that only the largest and most advanced companies had the resources to build their own internal ML platforms. The rest of the industry struggled to efficiently turn ideas into ML models. This led him and fellow cofounders from AWS, ironSource and Wix to launch Qwak as a unified MLOps platform.
How does it work?
As Lev explained, Qwak integrates all parts of the MLOps life cycle in one place, allowing the data science team to operate independently, right from the stage of building the models, comparing performance and analyzing changes to transferring them to the production environment and driving monitoring efforts.
The platform is fully managed (hosted either on Qwak’s or the customer’s cloud), which means that data science teams do not need to install packages or maintain infrastructure, and the product takes care of all the operational infrastructure.
“At the end of the day, Qwak allows data science teams to be more effective, and to significantly shorten the model development time. Instead of many months, the whole process can be cut down to a few hours, allowing teams to iterate faster and improving quality testing of the ML models and their behavior,” Lev noted.
Since its launch in December 2020, Qwak claims to have witnessed 10-fold year-on-year growth with dozens of enterprises signing up for its platform, including NetApp, Lightricks, Yotpo, JLL, Guesty and OpenWeb.
Competition in MLOps
The MLOps space has grown significantly with multiple open-source tools and vendors looking to help enterprises build and deploy production-grade models, including Deci, Domino Data Labs and H2O AI.
Qwak, for its part, claims to differentiate from these players by offering all the components and integrating them together.
“While there are many [vendors] that cover various components of Qwak — including feature store, model registry, serving, monitoring and ML pipeline orchestrators — the real power lies in creating a unified platform where all these parts are seamlessly integrated. By doing this, we provide a streamlined experience for data scientists, eliminating the friction of connecting multiple tools every time a model needs to be built or upgraded,” Lev noted.
This also improves visibility and facilitates the sharing of ML components between team members, improving collaboration and boosting productivity, he added.
With this round, which was led by Bessemer Venture Partners, the company will continue to build out this all-in-one offering and move toward its long-term vision of building a comprehensive machine-learning cloud. It also plans to expand its team in the U.S. and European markets.
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