Qwak Raises $12M for All-in-One MLOps Platform

Are you struggling to build high-performing ML models and deploy them into production? The Israeli startup Qwak has the solution for you. In this blog post, we’ll explore how Qwak is revolutionizing the MLOps space and how its end-to-end MLOps platform can help enterprises build and deploy models at scale. Read on to find out more!

The Challenges of MLOps

Building ML models and deploying them into production is no easy feat. Data science teams have to navigate a highly fragmented environment, integrating with different stakeholders and utilizing specialized tools. This takes up significant time and resources, and many projects do not even make it to production. And, for the few models that do, deployment can take a long time, followed by the need to constantly monitor them for quality and efficiency.

Qwak: The End-to-End MLOps Platform

Alon Lev, previously the VP of data at Payoneer, noticed the challenges of MLOps and found that only the largest and most advanced companies had the resources to build their own internal ML platforms. This led him and fellow cofounders from AWS, ironSource and Wix to launch Qwak as a unified MLOps platform.

Qwak integrates all parts of the MLOps life cycle in one place, allowing the data science team to operate independently, from the stage of building the models to transferring them to the production environment and driving monitoring efforts. The platform is fully managed, meaning that data science teams do not need to install packages or maintain infrastructure.

Qwak’s Impact

Since its launch in December 2020, Qwak has seen 10-fold year-on-year growth with dozens of enterprises signing up for its platform. The platform allows data science teams to be more effective, significantly shortening the model development time and allowing teams to iterate faster.

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. Qwak differentiates from these players by offering all the components and integrating them together, providing a streamlined experience for data scientists and improving visibility and collaboration.

Future Plans

With this round of funding, Qwak plans to further develop its product and eventually set up a “machine-learning cloud” for enterprises. The company also plans to expand its team in the U.S. and European markets.

In conclusion, Qwak is revolutionizing the MLOps space by providing a unified platform that integrates all components and allows data science teams to operate independently. With this platform, teams can quickly develop and deploy models and improve collaboration. Be sure to keep an eye on Qwak as the company continues to expand and develop its product.

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