Established to assist ML teams in efficiently managing the entire ML data lifecycle is Superb AI, by providing its flagship product, the Superb AI Suite, an enterprise SaaS platform. Developed by a team of expert researchers, academics, engineers, and passionate problem solvers, the solution enables seamless collaboration between various teams in the ML lifecycle. Being able to serve as a central repository to store, label, discuss, analyze, and visualize data, the solution helps manage complex ML workflows by providing ML teams with complete control over the data. “We are on a mission to redefine the ML data management process—once relied solely on manually-driven models—by leveraging our in-depth know-how in the industry,” says Hyun Kim, the CEO of Superb AI.
Superb AI Suite is designed to primarily serve ML teams who have a unique set of requirements in the ML data management process. It serves as the most sought-after solution for product leaders by helping them seamlessly create new training datasets without any engineering support. By providing a powerful labeling tool for computer vision, Superb AI Suite allows them to easily spin up custom data labeling projects. The solution’s unmatched data management capabilities help assign tasks, track issues, and control data status while providing data distribution, workforce and labeler activity, and annotation statistics within one platform.
Additionally, Superb AI Suite helps ML teams assign, review, and resolve issues quickly by collaborating with all stakeholders within the ML lifecycle. The solution’s role-based access controls allow them to easily share datasets, charts, and dashboards while managing access permissions to increase productivity and efficiency.
The Superb AI Suite also permits the use of developer tools such as CLI, SDK, and API’s to build an enterprise-grade training data pipeline.
This allows ML teams to automatically gather data, eliminating the need for time-consuming and inefficient manually-driven workflows. What makes Superb AI Suite unique is its advanced auto-labeling potential based on proprietary technologies using Bayesian Deep Learning and active learning. The auto-labeled data is then verified by a human user to eliminate all possible errors. The solution’s comprehensive potential to review and analyze labeled data provides scores to data, allowing human users to identify the dataset that requires visual inspection. “With such a powerful value proposition, the solution empowers clients to create and label datasets up to 10 times faster,” adds Hyun.
We are on a mission to redefine the ML data management process
To further elaborate on Superb AI’s value proposition, Hyun recalled their recent collaboration with an AR/VR company that wanted a ML pipeline to identify and filter out their clients’ personal information. With the traditional manually-driven model, the client was spending a lot of time managing their ever-growing data volumes. Superb AI helped them automate the pipeline by deploying its state-of-the-art solution in their ecosystem. Therefore, instead of manually downloading, uploading, sending, and sharing data across teams, they use its SDK and API to integrate the whole pipeline. When customers use their product, data is automatically stored in the solution, then labeled using their AI models and assigned to the labeling team. As a result, the solution helped them streamline their ML lifecycle to bring better results at lesser cost and time.
With many such instances of client success, Superb AI envisions enhancing its solutions’ capability to embrace more diverse data types, Including text, LIDAR, speech, to name a few. The company is currently extending its footprints into the ML lifecycle ecosystem by incorporating abilities to deploy solutions and monitor them to provide better outcomes. To do this, they are collaborating with existing players in the industry to integrate its Superb AI Suite with their solutions. In terms of geography, Superb AI is expanding its footprint across the U.S and several countries in the APAC region. “Bringing in a constant enhancement in the ML production lifecycle is always in our roadmap to make ML teams equipped with all necessary tools to develop groundbreaking AI solutions,” concludes Hyun.