The Scale Team

We founded Scale to create the infrastructure needed to build AI in any industry, by anyone. We started tackling this complex problem at the root — turning raw data into high quality training data for models. In this pursuit, we spent the last four years building ML-augmented annotation products for all data types, expanding our solutions for major industries, and making significant technological strides in <a href="https://scale.com/blog/how-we-scale-machine-learning" rel="noopener noreferrer" target="_blank">scaling our use of ML</a> in doing so. But the problem of building effective, accurate, unbiased ML models still remains. To do this, aggregate metrics in ML are not good enough. Better ML starts with understanding your data in depth. To improve production ML, you need to understand their qualitative failure modes, fix them by gathering the right data, and curate diverse scenarios.<ol><li>Before training a model, ML engineers must curate and sample their</li><li> data, ensuring that they have the right data to solve a specific problem. This</li><li> process is too often very manual. For example, to teach a self-driving car</li><li> how to handle left turns, an ML team has to manually crawl through their</li><li> driving sequences to isolate examples of left turns to create a training</li><li> dataset. The data also needs to be representative of the ground</li><li> truth of the problem you’re trying to solve. If you’re building a model to</li><li> assign a gender to faces, you have to ensure representative data for all</li><li> genders to have unbiased outputs. Again, that’s too often a manual and</li><li> highly inefficient process.</li><li> </li><li>After training, ML teams test and benchmark model performance, ensuring that the test dataset is also sufficiently representative of the</li><li> problem they are trying to assess its performance against. For example, a</li><li> model learning how to tell cars apart from pedestrians needs enough of both</li><li> examples for accurate benchmarking. This requires ML engineers to spend</li><li> significant amounts of time building one-off UIs to chart and share</li><li> performance data.</li><li> </li><li>After deployment, debugging the model, identifying failure</li><li> modes and fixing them. Too often issues in data only arise after a model has</li><li> entered deployment – requiring time-consuming debugging. One Scale customer,</li><li> for example, found that their vehicle recognition algorithm didn’t perform</li><li> well in certain environments – it turned out that the model was trained on a</li><li> dataset where vehicles were mostly in the bottom of the image, so the model</li><li> associated “bottom of the image” with “likelihood of being a car.”</li><li> </li><li> </li></ol> The Scale team has been working to productize the concept that Andrej Karpathy calls "<a href="https://www.youtube.com/watch?v=oBklltKXtDE&amp;feature=youtu.be&amp;t=475s" rel="noopener noreferrer" target="_blank">Operation Vacation</a>." Nucleus is a new way—the right way—to develop ML models, helping us move away from the concept of one dataset and towards a paradigm of collections of scenarios and giving ML engineers the ability to automate time-consuming manual steps in the ML development process. <img src="https://cdn.builder.io/api/v1/image/assets%2Fe0438815ba51486bbb6a202747122d4b%2F004680ecf1424e3193faf21d626662b8" alt="ML Development Cycle"><a href="https://scale.com/nucleus" rel="noopener noreferrer" target="_blank">Scale Nucleus</a> provides advanced tooling for understanding, visualizing, curating, and collaborating on your data – allowing teams to build better ML models via a powerful interface and APIs. With Scale Nucleus, you can: <ul><li>Visualize your dataset, ground truth, and model</li><li> predictions to improve model performance</li><li> </li><li>Curate interesting slices within your dataset for active</li><li> learning and identifying key edge cases</li><li> </li><li>Upload and choose data to be annotated for rare event</li><li> mining and dataset balancing</li><li> </li><li>Search your data based on metadata or ML-produced</li><li> attributes</li><li> </li><li>Identify edge cases through visual search</li><li>Measure key metrics like dataset balance, class</li><li> correlation, and confusion, via a powerful insights tab that shows the</li><li> overall health of the data</li><li> </li><li>Debug model performance</li><li>Share your dataset seamlessly to provide a single source</li><li> of truth for data within your team.</li><li> </li></ul> <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/a41b6e07-5da7-41e2-5d94-542a8a933900/public" alt="Scale Nucleus Query"> Scale Nucleus’s query function, allowing users to return images matching automatically-generated meta-tags. Scale Nucleus is directly integrated with Scale AI’s data labeling pipeline – allowing teams to fix any issues in their data at the source. Nucleus currently supports Image data, with support for 3D Sensor Fusion, Video, Text and Document data coming soon. We built Scale Nucleus to allow data scientists and ML engineers to manage data more efficiently and increase the marginal value of their data. Deeper insight into a dataset’s features can have several transformational effects on the development of AI: <ul><li> </li><li> For engineers, the easier visualization and curation of datasets lowers</li><li> the barriers to entry to building ML systems.</li><li> </li><li> </li><li> Spotting hidden modes of failure in datasets before deployment (such as a</li><li> set of driving sequences that doesn’t include any sequences at night)</li><li> makes it much easier to train high-quality models, and provides a robust</li><li> way of eliminating issues like bias at source.</li><li> </li><li> </li><li> The ease of debugging could significantly improve iteration speed on model</li><li> fine-tuning post-deployment.</li><li> </li></ul> We are excited to take the next step in building the infrastructure to enable efficient, accurate, and unbiased ML development. If you’d like to join us in this journey and try Scale Nucleus, contact us at nucleus@scale.com or sign up on our <a href="https://scale.com/nucleus#sales" rel="noopener noreferrer" target="_blank">website</a>.

Scale Nucleus: The mission control for your data

Introducing Scale AI's latest product: Scale Nucleus

Introducing: Scale Nucleus

In these unprecedented times, COVID-19 has brought out a renewed and inspiring sense of collaboration in AI and research communities as we work toward solving pressing issues. But the pandemic has also exacerbated some of the difficulties of developing new technologies at scale. For example, as we shelter in place around the world, the promise of autonomous vehicles (AVs) to improve access to critical goods and services has never felt more <a href="https://mashable.com/article/autonomous-vehicle-perception-coronavirus/" rel="noopener noreferrer" target="_blank">relevant</a>. But as we realize more ways these technologies could improve our lives, the essential data collection and testing that power them have rightly been suspended to ensure the safety of those involved. That’s why today we’re launching <a href="https://pandaset.org/" rel="noopener noreferrer" target="_blank">PandaSet</a>: a new open-source dataset for training machine learning (ML) models for autonomous driving released in partnership with the LiDAR manufacturer Hesai. While many AV companies are turning to complementary techniques and simulated data to continue their work, there is often no substitute for high-quality data that captures the complex and often messy reality of driving in the real world.<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/a9340c61-2c49-46f1-b0c2-3b904bf7e500/public" alt="LiDAR Comparison"> High-quality data is crucial to building safe and effective AV systems. PandaSet is the world’s first publicly available dataset to include both mechanical spinning and forward-facing LiDARs (Hesai’s Pandar64 and PandarGT)—allowing ML teams to take advantage of the latest technologies. It is also the first to be released without any major restrictions on its commercial use. There are three reasons why we hope AV teams will find PandaSet to be a valuable resource: its content, its quality, and its no-cost commercial license. <h3>Content</h3> Covering some of the most challenging driving conditions for full level 5 autonomy, PandaSet includes complex urban environments, their dense traffic and pedestrians, steep hills, construction, and a variety of lighting conditions in the day, dusk and evening. There are more than 48,000 camera images and over 16,000 LiDAR sweeps—more than 100 scenes of 8s each. Capturing sequences in busy urban areas also means there is a high density of useful information, with many more objects in each frame than in other datasets. <h3>Quality</h3> By combining the strengths of both mechanical spinning and forward-facing LiDARs, PandaSet captures the complex variables of urban driving in rich detail. It also includes 28 different annotation classes for each scene as well as 37 semantic segmentation labels for the majority of scenes. With LiDAR data far beyond the capabilities of traditional cuboid labeling, it features Scale’s Point Cloud Segmentation that enables the highest precision and quality annotation of complex objects, such as smoke or rain. PandaSet also features Scale’s market-leading <a href="https://scale.com/3d-sensor-fusion" rel="noopener noreferrer" target="_blank">Sensor Fusion</a> technology, allowing ML teams to blend multiple LiDAR, RADAR and camera inputs into a single point cloud that allows for the semantic segmentation of different objects in LiDAR data. By allowing ML teams to exploit their LiDAR data much more systematically, this makes PandaSet ideal for building highly-performant autonomous systems. <img src="https://cdn.builder.io/o/assets%2Fe0438815ba51486bbb6a202747122d4b%2F00e35e3fc46c40309cf37c6a0b93d8ae%2Fcompressed?apiKey=e0438815ba51486bbb6a202747122d4b&amp;token=00e35e3fc46c40309cf37c6a0b93d8ae&amp;alt=media&amp;optimized=true"> <h3>No-cost commercial license</h3> Many existing open-source datasets have restrictive licensing terms that allow only research or limited commercial uses. While that can be important for helping ensure data is used appropriately, we wanted to make PandaSet available to the entire community, democratizing access to the latest LiDAR technologies for ML teams around the world at a time when the barriers to data collection are higher. We all want to accelerate the safe deployment of AVs—and the need for the right data has never been more pressing. By filling the gap for AI and ML developers who might otherwise be unable to build and test new technologies, we hope PandaSet will provide a useful resource for teams building a future for mobility that is safer and more accessible for everyone. You can find more information about PandaSet <a href="https://scale.com/open-datasets/pandaset" rel="noopener noreferrer" target="_blank">here</a>, with dataset support tools available on <a href="https://github.com/scaleapi/pandaset-devkit" rel="noopener noreferrer" target="_blank">GitHub</a>.

Introducing PandaSet, the world's most advanced LiDAR dataset for commercial use.

Launching the world’s most advanced LiDAR dataset for commercial use.

Introducing: PandaSet. The world's most advanced LiDAR dataset for commercial use

<h2>Notice any changes to our website?</h2>
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The Scale AI team is excited to introduce a new look and new structure for our
product line-up. Scale AI was founded almost four years ago because we
believed that high quality training data is the key bottleneck to the
development of AI applications. Thanks to feedback from incredible customers,
we have significantly expanded our data annotation products over the years to
support an increasing range of data inputs and annotation types for computer
vision and natural language (NLP) applications.
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As we expanded our data annotation capabilities, it became clear that we
needed a better way to group and name our products to reflect both the current
state and future vision of our product offering.
<h2>What exactly are the changes?</h2>
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Sensor Fusion Cuboids and Sensor Fusion Segmentation is now one product called
<a href="https://scale.com/3d-sensor-fusion" target="_blank" rel="noopener noreferrer">Scale 3D Sensor Fusion</a>.
<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/8ef57c88-a258-41a3-a359-5c9fed59b400/public">
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3D Cuboids, Bounding Boxes, Image Categorization, Lines &amp; Splines,
Polygons and Semantic Segmentation are also now one product called
Scale Image with an
addition of Point Annotation.
<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/132eb95b-d69d-47a9-f657-bb3ec16e2e00/public">
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Video Annotation is now
Scale Video, and has been expanded to include classification, cuboid and polygon
annotation in addition to the existing bounding box annotation.
<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/28098202-ca0e-436e-606d-f39ff82eea00/public">
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Our Natural Language products have been updated to
<a href="https://scale.com/text" target="_blank" rel="noopener noreferrer">Scale Text</a> with
Scale Audio currently in private beta.
<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/41c67cce-2a87-4057-2353-667527172500/public">
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<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/8b1ab848-ccda-4691-3f23-77daa83f0100/public">
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You may also wonder, &ldquo;how does this product restructure affect me?&rdquo; If you are
one of our amazing customers, from a practical standpoint, impact to you is
minimal. While the product names are different, the products themselves are
still the same, and we will continue to deliver data at the quality you
expect. If you&rsquo;re learning about Scale AI for the first time, we hope this
rebrand allows you to better understand our full product suite and its
capabilities.
<h2>What&rsquo;s Next?</h2>
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The Scale AI team remains committed to delivering products customers love and
rely on. We are grateful for all of our incredible and innovative customers
for their support. In the coming weeks and months, we&rsquo;ll be announcing and
rolling out new products and additional annotation types to existing products.
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In the meantime, if you have any questions about todays's announcement, reach
out to us at support@scale.com. Otherwise, we invite you to explore our
updated product line up.

Explore the new product line-up: Scale 3D Sensor Fusion, Scale Image, Scale Video and Scale Text

Introducing a new look & new structure 4 our product line-up

Introducing a New Look for Scale AI Products

As the COVID-19 pandemic continues to spread, it affects all of our communities in different ways. The health and safety of Scale AI’s employees, as well as our global community of labelers, is a critical matter and a responsibility we do not take lightly. That’s why we’ve taken action over the past several weeks to protect both our full-time and contract employees, and ensure we can continue to offer the high-quality services our customers expect. We also want Scale AI to make a contribution to combating this disease if we can. Research to track and contain the virus is in its early days, but we know that better data could make a difference. To help these efforts, Scale AI will provide free data annotation services for any researcher working on diagnosing, containing, tracking or finding a vaccine or long-term cure for the virus. If you’re working on a project, you can reach out to us <a href="https://events.scale.com/accelerating-covid-19-research" rel="noopener noreferrer" target="_blank">here</a>. <h2>Scale AI's Employees, Labeling Community and Customers</h2> The safety of our team, both full-time and contractors, is a top priority. As a technology company, we are fortunate to be able to leverage existing communications tools to maintain collaboration. Scale AI’s ML-enabled platform also gives a global, work-from-home workforce the ability to deliver data annotation at the level of quality and volume our customers expect. Anyone working with a basic laptop and internet connection can utilize our platform. We are proud to provide a safe option for workers to continue to earn wages to support themselves and their families throughout this period of uncertainty. We have also reached out to our customers to let them know how we're working to deliver products and services. We do not expect any disruption to our work at the moment, however, because the situation remains fluid, we will continue to closely monitor our operations. In the event of a disruption, we will make sure to coordinate with your teams to minimize any impact. <h2>Accelerating COVID-19 Research</h2> Adjusting to the reality of a new disease has been difficult and unsettling, but it has been heartening to see a range of companies and organizations come together to help combat this pandemic. We want to make our own contribution to these efforts where we can. Given the scale of the pandemic, researchers should have every tool at their disposal as they try to track and counter this virus. Researchers have <a href="https://deepmind.com/research/open-source/computational-predictions-of-protein-structures-associated-with-COVID-19" rel="noopener noreferrer" target="_blank">already shown</a> how new machine learning techniques can help shed new light on this virus. But as with all new diseases, this work is much harder when there is <a href="https://www.wsj.com/articles/coronavirus-reveals-limits-of-ai-health-tools-11582981201" rel="noopener noreferrer" target="_blank">so little existing data to go on</a>. In those situations, the role of well-annotated data to train models or diagnostic tools is even more critical. Our mission at Scale AI is to accelerate the development of AI applications. To help in those efforts, Scale AI will provide free data annotation services for any researcher working on diagnosing, containing, tracking or finding a vaccine or long-term cure for the virus. Advancing and accelerating health research and science is critical to reducing the spread of viral infections. If you have data labeling needs related to this research, please let us know <a href="https://events.scale.com/accelerating-covid-19-research" rel="noopener noreferrer" target="_blank">here</a>.

Scale AI will offer free labeling to researchers working to contain the spread of the virus.

Scale AI is committed to containing the spread and being a resource to researchers working on COVID-19.

Supporting researchers and our community during COVID-19

We believe that creating jobs can be very impactful. We asked some labelers for times when working for Scale made a positive difference in their lives.

The Scale Team

Introducing: Scale Nucleus

Introducing: PandaSet. The world's most advanced LiDAR dataset for commercial use

Introducing a New Look for Scale AI Products

Supporting researchers and our community during COVID-19

Positive Externalities: Life of a Labeler