Founded Year

2018

Stage

Series A | Alive

Total Raised

$10.98M

Last Raised

$9.3M | 1 yr ago

Mosaic Score

+30 points in the past 30 days

What is a Mosaic Score?
The Mosaic Score is an algorithm that measures the overall financial health and market potential of private companies.

About Superb AI

Superb AI uses AI to customize training data for large tech companies. The company's Superb AI Suite is an enterprise SaaS platform built to help ML engineers, product teams, researchers, and data annotators create efficient training data workflows through its filter &search, auto-labeling AI, and ML Ops integration solutions.

Superb AI Headquarter Location

400 Concar Drive

San Mateo, California, 94404,

United States

Superb AI's Product Videos

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ESPs containing Superb AI

The ESP matrix leverages data and analyst insight to identify and rank leading companies in a given technology landscape.

EXECUTION STRENGTHMARKET STRENGTHLEADERHIGHFLIEROUTPERFORMERCHALLENGER
Enterprise IT / AI

Well-labeled training data is the foundation of AI projects, such as teaching an autonomous car to identify pedestrians and traffic signs or a diagnostic system to differentiate between normal and anomalous medical scans. Companies in this space are streamlining a traditionally human-intensive workflow with AI-assisted labeling platforms.

Superb AI named as Challenger among 14 other companies, including Sama, V7, and Scale AI.

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Superb AI's Products & Differentiation

See Superb AI's products and how their products differentiate from alternatives and competitors

  • The Suite: Automate

    Superb AI provides the ability to automatically detect and label objects in image and video datasets at scale using either a pre-trained automation model provided by Superb AI or a custom model fine-tuned to your exact use case (known as custom auto-label, or CAL). Also included is Uncertainty Estimation AI which highlights labels you should audit, allowing you to more effectively sample data for review. Furthermore, CAL allows you to implement rapid active learning workflows into your ongoing annotation process by surfacing hard examples near the decision boundary. This speeds up iteration loops by reducing reliance on easy examples that don’t do much to improve model performance. All you have to do is take this newly labeled dataset, perfected with minimal QA, and retrain CAL as needed to hit your target metrics.

    Differentiation

    Custom AutoLabel combines Bayesian Deep Learning, transfer and few-shot learning, and self-supervised learning with a tech stack designed purely for data labeling. This is fully optimized over a set… 

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    Differentiation

    We're on a mission to enable every organization to make smarter decisions about tech. Whether it's finding a new game-changing vendor or understanding a new market, it's easier, faster and smarter with CB Insights. All made possible by the smartest, hardest-working team in tech. Subscribe to see more.

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    Differentiation

    We're on a mission to enable every organization to make smarter decisions about tech. Whether it's finding a new game-changing vendor or understanding a new market, it's easier, faster and smarter with CB Insights. All made possible by the smartest, hardest-working team in tech. Subscribe to see more.

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    Differentiation

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Expert Collections containing Superb AI

Expert Collections are analyst-curated lists that highlight the companies you need to know in the most important technology spaces.

Superb AI is included in 1 Expert Collection, including Artificial Intelligence.

A

Artificial Intelligence

8,717 items

This collection includes startups selling AI SaaS, using AI algorithms to develop their core products, and those developing hardware to support AI workloads.

Superb AI Patents

Superb AI has filed 8 patents.

The 3 most popular patent topics include:

  • Classification algorithms
  • Machine learning
  • Artificial intelligence
patents chart

Application Date

Grant Date

Title

Related Topics

Status

11/23/2020

9/7/2021

Artificial neural networks, Machine learning, Classification algorithms, Artificial intelligence, Google services

Grant

Application Date

11/23/2020

Grant Date

9/7/2021

Title

Related Topics

Artificial neural networks, Machine learning, Classification algorithms, Artificial intelligence, Google services

Status

Grant

Latest Superb AI News

State-of-the-Art Data Labeling With a True AI-Powered Data Management Platform

Apr 1, 2021

Diving into genuine state-of-the-art automation of the data labeling workflow on large unstructured datasets Data labeling is an essential part of the machine learning workflow, particularly data preprocessing, where both input and output data are labeled for classification to present a learning base for planned data processing. We use data labeling to identify raw data, such as objects in images, videos, text, and so on. It works by affixing one or more significant and informative labels to produce context so that a model can learn from it [ 2 ]. Sometimes due to the inefficient data labeling, it ends up eating a whole chunk of time, and it represents a bottleneck for machine learning workflows. Superb AI , a novel B2B SaaS startup, has a highly developed data platform that tackles the challenges businesses face throughout their machine learning workflows, specifically cold-start and data labeling throughout large unstructured datasets. Their platform does not only tackle data gathering and transformation, but it truly brings a novel way toward true automation during the fundamental and possibly most challenging stages of the machine learning lifecycle, including data preparation, data labeling, and the data management workflow. Unlike their competitors, Superb AI’s machine learning data platform does not require AI-assistance during its workflows, saving countless amounts of human hours and heading toward a data management workflow with a significantly smaller amount of human-in-the-loop (HITL) efforts. Back in 2018, Superb AI started its journey with an outstanding data labeling service called “Human-in-loop (HITL).” HITL is a branch of artificial intelligence that strengthens human and machine intelligence to build machine learning models. The traditional human-in-the loop strategy involves several cumbersome circles like training, tuning, and testing of algorithms with a given dataset. Superb AI’s strategy is more innovative and more straightforward than the traditional human-in-the-loop strategies, helping enterprises and startups build machine learning systems 10x faster [ 3 ]. Source: Superb AI , image used with permission. Collecting, labeling, and delivering high-quality datasets is probably taking you weeks, if not months. With Superb AI , train our transfer learning auto-label model within an hour with a small ground-truth dataset, bulk label massive datasets in minutes, audit for quality, and deliver — all in a matter of days! What are you waiting for? Sign up for free . Undoubtedly, Superb AI helps automate the data labeling process, and due to their revolutionizing platform, machine learning models can be built way faster than with traditional approaches. Human-in-the-Loop (HITL) process | Image by the author. What is cold-start in machine learning? Cold start is a challenging problem in data science that involves the construction of automated data modeling. Specifically, when an ML model cannot label data that has not gathered sufficient information yet. The availability and utilization of labeled data to label unseen historical data are fundamental to supervised machine learning. It is not easy to label future data without labeling past data. This problem is prevalent in machine learning workflows [ 1 ]. There are three cases for cold start problems: New community. New user. Surprisingly, the name derives from the issue that cars tend to experience during winter. When the weather is cold, a car’s engine will have challenges with starting up, but once it reaches its optimal driving temperature, it will run smoothly. In data science, the “cold start problem” commonly means that the conditions are not optimal for the model to provide the best possible results. Why is labeling a massive volume of unstructured data a challenge? Also known as the curse of big data labeling. Unstructured data does not accommodate neatly into databases designed by fixed schemas like name, phone, email, and others. Unstructured data is schema-free data, and it is not bound to the specific structure of schema and its relationships. Unstructured data can be a great and valuable source of related information, but it does not merely accommodate itself to older forms of data storage and analysis. There are five significant challenges in labeling massive volumes of unstructured data: Dataset nature. Smart tools. Nowadays, large unstructured data can have massive volumes. It is a tiresome task to manage all employees who can process the massive volume of unstructured data and still aim for high-quality throughout the entire workflow model. Unstructured data face several issues mentioned below: It is challenging to assure continuous communication and collaboration between labelers and data scientists to maintain quality, validation, and model training. There can be geographical and cultural gaps between labelers who might fail to annotate data accurately. It is tough to produce consistent high-quality data by the labeler. It is a costly process concerning labeling and quality assurance. It is a time-consuming process, and it is even more so in machine learning, which demands iterations and evolves data features as we train and tune our models to improve data quality and model performance. Derivative from a screenshot of Superb AI’s website highlighting their machine learning suite. How can Superb AI’s Platform help in Data Labeling Superb AI has a unique approach to data labeling. For instance, we can label a ground truth dataset containing hundreds to thousands of objects. Depending on the object detection task’s complexity, we can utilize their transfer learning model and bulk label the large datasets. Once completed, human reviewers use auditing tools to fine-tune and deliver the labeled dataset. Besides providing terrific support during the labeling process of a massive volume of data. Superb AI’s data labeling platform also lets us work with human labelers to produce highly accurate ground truth labels — and as you may know, producing accurate labels is the initial and one of the most critical steps in a machine learning workflow. Steps to Labeling Data in Superb AI’s Platform Superb AI’s platform is straightforward and user-friendly to use and perform data labeling. Please follow the following steps to get started: Signing up for An Account with Superb AI Go to the signup page to create an account. Fill the form shown thereafter to create an account: Screenshot from Superb AI’s platform. Login Screenshot from Superb AI’s platform. This dashboard provides three important sections: Project List Note: Categories are only created for image-classification-related tasks. Screenshot from Superb AI’s platform. Create category group Give the name of the Category Group. Screenshot from Superb AI’s platform. Create an Object Detection class Object Detection class allows creating a type of object class. Each annotation allows selecting the type of class of annotation like a vehicle, cloth, person, and others, which will enable us to select the annotation type for each class. Screenshot from Superb AI’s platform. Create Class Group Note: Creating class groups are only needed when we may have many subclasses within an object class in the dataset. For instance, traffic signs, stop signs, speed limit signs, and so on. Screenshot from Superb AI’s platform. After creating the class groups, Superb AI gives us a complete project overview as shown below: Screenshot from Superb AI’s platform. Adding the Dataset Superb AI offers three options to upload a dataset, whether it is by uploading a file, uploading a CSV, or by using Cloud Upload. Superb AI’s cloud upload makes it easy to integrate with your cloud storage provider, for instance, AWS S3, GCP, or Google Drive: By clicking on the Data List tab, we get the following options to upload a dataset: Screenshot from Superb AI’s platform. For instance, to integrate Amazon S3 with Superb AI, click on AWS S3, and then Add S3 Integration. Next, we are asked to connect our S3 bucket as a data source for the suite. To do so, click on Add, and follow the prompts to connect your S3 bucket. Screenshot from Superb AI’s platform. A neat option when uploading a dataset from the cloud allows us to select whether we would like our images stored directly on Superb AI’s servers or be handled as read-only. Read-only allows for full platform functionality without physically storing images on the platform. This is especially useful when working with sensitive data and for security purposes. Assign Uploaded Data to a Project Click on the Data List tab → Click on Assign to Project button. Screenshot from Superb AI’s platform. Start Labeling After assigning uploaded data to the project, we can start labeling the data. To start the data labeling process, please follow the following steps: Open the project → Click on Start Labeling. Screenshot from Superb AI’s platform. It opens the labeling dashboard → Click on Start Label List. It opens the labeling dashboard → Click on Open Label List → Assign me a new label → Select Image. Screenshot from Superb AI’s platform. Select Class → “Vehicle”→ Select Category → bus Click on Submit Screenshot from Superb AI’s platform. Click on next, and the entire dialogue box appears where we can select the members and assign tasks to other members. Superb AI gives you various options for distribution, whether it be something as simple as the newest images or label uncertainty which is calculated by Superb AI’s Uncertainty Estimation. The latter allows for the identification of hard examples so that teams can quickly assign for manual review, creating smooth, active learning workflows. Screenshot from Superb AI’s platform. After selecting the members, now we move to the distribution tab by clicking on Next. We can see the allocation tab appearing. Then click on the apply tab — the assignment is verified. Screenshot from Superb AI’s platform. After we complete the process above, we can notice that the label's status is In Progress. Screenshot from Superb AI’s platform. Final step: Screenshot from Superb AI’s platform. Training the Custom Auto Label (CAL) A big part of the active learning loop is understanding where the AI is highly uncertain, helping teams address those anomalies and complex examples before assigning uncertain items for human review. To take advantage of Superb AI’s state-of-the-art AI-powered auto label, we first need to export our labels. To do so, click on the Export tab, and then if we click on Export Guide, they give us a quick way to export our labels, whether by exporting all labels or by exporting all submitted labels. First, we must select the images (label tag “train set”) we want to use to train our Custom Auto-label model. Once the images are selected, click on Export Selected to export these images to train the Custom Auto-label model. Screenshot from Superb AI’s platform. Next, we are directed to the Export page, where we can see our exported images. To train our Custom Auto-label model, click on Create Custom Auto-label AI. One thing that truly caught our eye during the CAL process was the training’s speediness, as it completes in less than an hour. Screenshot from Superb AI’s platform. Once the Custom Auto-label model is trained, we can find it by clicking on Custom Auto Label in the left navigation bar. Note: if we select less than 10 images from the label list, the suite will show us an error message Note how, after training is complete, the platform shows how much more efficient the custom auto-label will be in comparison to human labelers. Screenshot from Superb AI’s platform. Next, go back to the Label List page, filter for the dataset we would like to label using our Auto Label and select Auto Label as shown. As a reference, the auto-label can run through 100,000 images in about 30 minutes. Screenshot from Superb AI’s platform. Analytics and Insights By clicking on the Analytics tab, we can get an overview of our project analytics, the total amount of our labels, how many have been submitted, the percentage of the completion process, and others. Superb AI offers data distribution, workforce and labeler activity, annotation statistics all within one platform. Screenshot from Superb AI’s platform. Screenshot from Superb AI’s platform. Conclusion Superb AI’s suite helps manage, collaborate and strengthen up our ML development cycle immensely. Superb AI creates customized data sets to meet any project’s requirements, using AI to speed up the tagging process. This platform also helps in reducing the human-in-the-loop hours to speed up our machine learning workflows significantly. It uses a proprietary AI, specifically few-shot, transfer learning, bayesian classical machine learning, and deep learning techniques, to help data practitioners achieve faster labeling of images, videos, and others by splitting training data into smaller components. By tackling these challenges thanks to Superb AI, we firmly believe that anyone can easily build their own AI systems. By continuing to innovate, we are confident that Superb AI will become the best global SaaS platform in the space of machine learning data management. DISCLAIMER: The views expressed in this article are those of the author(s) and do not represent the views of any company (directly or indirectly) associated with the author(s). This work does not intend to be a final product, yet rather a reflection of current thinking, along with being a catalyst for discussion and improvement. All images are from the author(s) unless stated otherwise. Resources:

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  • When was Superb AI founded?

    Superb AI was founded in 2018.

  • Where is Superb AI's headquarters?

    Superb AI's headquarters is located at 400 Concar Drive, San Mateo.

  • What is Superb AI's latest funding round?

    Superb AI's latest funding round is Series A.

  • How much did Superb AI raise?

    Superb AI raised a total of $10.98M.

  • Who are the investors of Superb AI?

    Investors of Superb AI include Murex Partners, kt investment, Duke University, Stonebridge Ventures, Atinum Investment and 6 more.

  • Who are Superb AI's competitors?

    Competitors of Superb AI include Labelbox and 1 more.

  • What products does Superb AI offer?

    Superb AI's products include The Suite: Automate and 3 more.

  • Who are Superb AI's customers?

    Customers of Superb AI include Fox Robotics and Autonet.

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