AutoLabel

Simplify, automate, and accelerate your data labeling projects

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Driving Innovation

AI is a fiercely competitive field evolving at great speed. Reducing the time to start training AI models helps your business get to market faster than the competition and can often be the deciding factor between commercial success and failure. It is well known that about 80% of the total workload of a data science or AI project lies in producing a clean, labeled data set. In addition, enterprise data is expected to grow at a rate of 30% for each of the next 7 years, and dataset sizes are expected to grow, as well.

Now, you can fully streamline your AI workflow by labeling only the data that provide valuable information and by using an arbitrarily large hardware infrastructure to execute it – all of this comes in an easy-to-use environment powered by Samsung SDSA’s autoLabel and RedBrickAI.

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Major Services

  • What can Autolabel do for you and your business?

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Use Cases

    Price Plan

    Customers pay for the number of images labeled in addition to cloud training and inference instance costs.
    autoLabel software License
    Tier 1 Price per Image (<50K images): $0.08
    Tier 2 Price per Image (50K < x < 1M): $0.04
    Tier 3 Price per Image (>1M): $0.02

    Total autoLabel SaaS
    Tier 1 Price per Image (<50K images): $0.135
    Tier 2 Price per Image (50K < x < 1M): $0.095
    Tier 3 Price per Image (>1M): $0.075

    Example Pricing: For applying object detection labels to a dataset of 100,000 images, we estimate 11 autoLabel iterations, each consisting of 1% of the dataset, which would require manual labeling of 11,000 images. The autoLabel system will apply labels to the entire dataset of 100,000 images, automatically. The autoLabel SaaS price estimate would be 50,000 * $0.135 + 50,000 * $0.095 = $11,500, and the end customer would also pay 11,000 images * 7 labels/image * $0.036/label * 3 labelers = $8,316 in estimated manual labeling costs. The customer TCO would be $19,816 = $11,500 for autoLabel SaaS + $8,316 for 3rd party manual labeling assuming that each manually labeled image is separately labeled by 3 human labelers to disambiguate labeling quality. Note: Manual labeling is not part of this offering, and the price for a human-generated label is stated here only for comparative purposes.

    Testimonials

    • "We experienced an automation rate of over 90% due to autoLabel active learning as opposed to a fully-manual labeling. This process has the potential to substantially reduce labeling duration and cost"

      Ben Bongalon, Advanced Analytics & Development Manager, Intuitive Surgical

    • "There is no question about the value that deep learning brings to auto-labeling pathology images. We are really excited to explore the idea of a system that can learn little-by-little from pathologists"

      Nasim Eftekhari, Applied AI and Data Science Director, City of Hope

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    • "The biggest obstacle to both is having an expertly annotated large dataset of images. We want to create such datasets and models in a partnership with Samsung SDS"

      Zakia Rahman, Clinical Professor of Dermatology, Stanford University School of Medicine

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    • "Samsung SDS America has developed a software platform that will equip AI scientists to streamline and automate many mundane tasks required to build accurate models"

      Pranav Kumar, Senior Technical Lead, Innovecture

    • "With the new AI Accelerator offerings, it is now possible to scale productivity seamlessly and efficiently through a single user interface while eliminating significant barriers to AI progress through a push-button experience"

      Pranav Kumar, Senior Technical Lead of Innovecture

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    Whether you’re looking for a specific business solution or just need some questions answered, we’re here to help.

    "All supervised models that we use today have been trained on labeled data. It is always the most expensive part of any AI solution to label the data for training in every industry. It is even more difficult in healthcare because … these are very specialized images … that need expert labeling and annotation. It is very time-consuming and expensive because doctors and healthcare professionals have to annotate these images and each of them takes hours and hours of time. It is very difficult to feed a whole slide image into a deep learning method because each takes up multiple Gigabytes and so we have to break them into tiles. Usually, each slide is broken into thousands of tiles. Not every tile is cancerous. Thus, each tile needs to be examined and labeled. We wanted to explore the advantage of Samsung’s active learning in auto-labeling these images. There is no question about the value that deep learning brings to auto-labeling pathology images. We are really excited to explore the idea of a system that can learn little-by-little from pathologists."

    "Dermatology represents two great frontiers for new business models powered by artificial intelligence. Patients can take mobile phone pictures of their skin and obtain instant reliable diagnoses of any condition, and people may use pictures of their healthy faces and bodies to compare them against society’s beauty standards for precision cosmetics to look better while not looking abnormal. The biggest obstacle to both is having an expertly annotated large dataset of images. We want to create such datasets and models in a partnership with Samsung SDS. The skin, in addition to being the largest organ, is also the most visible. This accessibility has resulted in an exponential increase in the number of images. The skin is, and will likely continue to be, the most imaged organ. While there is potential for democratizing diagnosis for the general public, the impact to mental health through image distortion cannot be overstated."