The Scale Team

The co-founder of Schmidt Futures and a former CEO of Google, discusses how AI will shape our global future. Eric was joined by Scale AI CEO Alexandr Wang

Eric Schmidt Discusses the Geopolitics of AI - TransformX 2021 Conference - Scale AI

Eric Schmidt Discusses the Geopolitics of AI

Justin Basilico, Director of Machine Learning and Recommender Systems at Netflix describes how ‘everything at Netflix is a recommendation’.

Netflix on Recommendations and Personalization - TransformX 2021 Conference - Scale AI

Netflix Explains Recommendations and Personalization

Clement Delangue HuggingFace CEO, shares what Transformers can do. He explores how we might ensure they are used ethically and transparently.

Clement Delangue HuggingFace CEO on Transformers - TransformX 2021 Conference - Scale AI

HuggingFace CEO, Clement Delangue: Are Transformers Becoming the Most Impactful Tech of the Decade?

Kevin Scott, CTO of Microsoft shares how we can democratize AI for the benefit of everyone in society.

Highlights From Kevin Scott Microsoft - TransformX 2021 Conference - Scale AI

Microsoft CTO Kevin Scott's Seven Insights on the Future of AI

Editor’s note: This post originally appeared on <a href="https://lambdalabs.com/" rel="noopener noreferrer" target="_blank">lambdalabs.com</a>. You can find a link to it <a href="https://lambdalabs.com/blog/lambda-scale-nucleus/" rel="noopener noreferrer" target="_blank">here</a>. Training accurate models typically requires a deep understanding of your training data. Through visualization, it’s important to both identify potential flaws in your ground truth labels, and to look for insights that will guide model development. Thus, visualizing your training data is the foundation of every successful deep learning project. Nucleus is a tool from Scale that allows you to visualize and debug your training data. It provides an intuitive web interface to visualize and explore a wealth of different types of data and their labels. It also gives you the tools to explore and understand the relationship between your data and the model predictions that you generate. In this post, we'll show you how you can get started with Nucleus by training a pedestrian detection model. To do this, we will use Nucleus to visualize an object detection dataset containing images of common street scenes, and compare the ground truth to the predictions from a trained neural network. This loop of inspecting your training data and looking for failure modes in a network's predictions is a crucial process in refining both your training dataset and your neural network.<h2>Getting Started</h2> The example model in this post was trained on a <a href="https://lambdalabs.com/gpu-workstations/vector" rel="noopener noreferrer" target="_blank">Lambda Vector workstation</a> running <a href="https://lambdalabs.com/lambda-stack-deep-learning-software" rel="noopener noreferrer" target="_blank">Lambda Stack</a>. If you want to follow along, please see the <a href="https://colab.research.google.com/drive/1Cu2Xw0kPwbMMSdqUsSgmMMTzQ5b7usDZ" rel="noopener noreferrer" target="_blank">example notebook here</a>. To start, we’ll download the publicly available <a href="https://www.cis.upenn.edu/~jshi/ped_html/" rel="noopener noreferrer" target="_blank">Penn-Fudan Dataset</a>, which consists of photographs of pedestrian data with labeled bounding boxes. To load these into Nucleus, we need to do two things: first, sign up for a free Scale account and corresponding API key, and second, install the Nucleus client on our Lambda workstation from a command prompt or shell:<pre class="ql-syntax" spellcheck="false">pip install scale-nucleus
</pre> <h2>Training Data</h2> Once we've installed the Nucleus client and downloaded the dataset, we can upload the images and ground truth labels to a new Dataset we created in Nucleus. (For the full details of how to do this, see <a href="https://colab.research.google.com/drive/1Cu2Xw0kPwbMMSdqUsSgmMMTzQ5b7usDZ#scrollTo=jC-728nqvD0L" rel="noopener noreferrer" target="_blank">this section</a> of the notebook.) Now that we’ve uploaded the data, we can visualize the ground truth labels in the Nucleus web dashboard without writing any code.<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/edf2a44b-d1e4-4dcc-d6f4-70b3587ac100/public" alt="PennFudan pedestrian dataset with bounding boxes in Nucleus"> <h2>Model Predictions</h2> Now that we've "<a href="https://karpathy.github.io/2019/04/25/recipe/#1-become-one-with-the-data" rel="noopener noreferrer" target="_blank">become one with our data</a>," we can start thinking about training a neural network for object detection. For this example we fine-tuned a <a href="https://arxiv.org/abs/1506.01497" rel="noopener noreferrer" target="_blank">Faster R-CNN</a> model pre-trained on the <a href="https://cocodataset.org/#home" rel="noopener noreferrer" target="_blank">COCO dataset</a>. Training it for 10 epochs only takes a few minutes on an <a href="https://lambdalabs.com/blog/nvidia-rtx-a6000-benchmarks/" rel="noopener noreferrer" target="_blank">NVIDIA RTX A6000</a> in a Lambda Vector Workstation. After training is finished we can see that our model has reached an Average Precision (AP@IoU=0.50:0.95) of 0.83. This sounds OK. It's not too far from 1, but what does it mean? Do we have a good pedestrian detector yet? The AP metric of object detection is notoriously hard to get your head around, and there are endless <a href="https://jonathan-hui.medium.com/map-mean-average-precision-for-object-detection-45c121a31173" rel="noopener noreferrer" target="_blank">blog posts</a> that aim to provide an intuition for it, but you’ll ultimately need to look at our results to see how we're doing. Nucleus allows us to upload our model predictions and compare them to the ground truth. Here we can see that blue boxes predicted by our model match pretty well with the green ground truths from the dataset: <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/1dc58fa6-c049-4610-324b-aab8d114a600/public" alt="Image Grid, Ground Truth versus Fast-RCNN Bounding Boxes"> <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/005b8ff2-c8e7-44a0-42bc-615be98ba000/public" alt="Nucleus Annotation, Classification, and IoU Detail"> <h2>What to Try Next?</h2> Nucleus also gives some nice tools to help us figure out what else we should try to improve our accuracy results. Here we visualize the ground truth labels with the model predictions so that we can get a feel for where our model is going off the rails. <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/08ad13c7-2552-45ce-0650-cc8e4115a200/public" alt="Correcting the Ground Truth Bounding Box in Nucleus"> In many cases, it looks like our model is already doing a pretty good job, and it's the ground truth labels that have actually missed some pedestrians! For anyone who has worked on a practical machine learning project, this is probably a familiar story—finding and fixing errors and problems in your dataset is just as much part of deep learning as designing and training models. In this case, we’ll need to go back to our data labels and fix the ground truth errors in order to get a more accurate evaluation of our model. This is also something we can do directly in Nucleus with its convenient annotation tools. <h2>Conclusion</h2> Nucleus is a convenient way to visualize and organize your training data for a machine learning project, as we showed in our object detection example. Most importantly, it makes it easy to inspect and track the predictions from your trained models, and helps you establish the virtuous cycle of training networks, evaluating predictions, identifying failure cases, and improving both data and models: all of these steps prove to be crucial in developing an effective deep learning project. To learn more about Lambda workstations, <a href="https://lambdalabs.com/" rel="noopener noreferrer" target="_blank">check out Lambda’s website</a>, or to get started managing and improving your dataset, <a href="https://dashboard.scale.com/nucleus" rel="noopener noreferrer" target="_blank">sign up for Scale Nucleus here</a>. If you want to try this workflow out on your own <a href="https://lambdalabs.com/gpu-workstations/vector" rel="noopener noreferrer" target="_blank">Lambda workstation</a> or Lambda <a href="https://lambdalabs.com/service/gpu-cloud" rel="noopener noreferrer" target="_blank">Cloud instance</a>, you can explore the <a href="https://colab.research.google.com/drive/1Cu2Xw0kPwbMMSdqUsSgmMMTzQ5b7usDZ" rel="noopener noreferrer" target="_blank">example notebook here</a>.

Scale partners with Lambda to help businesses train better models on better data.

Data Curation with Nucleus and Better Training on Lambda 

Lambda and Scale Nucleus: Empowering Your Model Training with Better Data

It’s been an exciting journey since we shipped Scale Nucleus for the first time <a href="https://scale.com/blog/introducing-scale-nucleus" rel="noopener noreferrer" target="_blank">just over a year ago</a>. We’ve helped over 100 different organizations explore, curate and improve their datasets and debug their machine learning (ML) models, across semantic segmentation, object detection, and even 3D point clouds. Today, we’re excited to share an all-new Nucleus, with powerful new features and a redesigned user interface that make it easier than ever to create better models with better datasets. To improve ML models requires first understanding where they fail. Nucleus helps you go beyond aggregate error metrics, and instead quickly discover specific patterns of failure across your model predictions and dataset labels. Once you’ve identified problems, Nucleus makes them easy to fix. Address bad labels through efficient, model-assisted data quality assurance (QA) and one-click labeling integration, or improve model predictions through targeted data curation of what to label next.<a href="http://scale.com/nucleus" rel="noopener noreferrer" target="_blank">Scale Nucleus</a> is one of the most intuitive and efficient ways to select the right data for labeling: random sampling shouldn’t be the way you select data to label. Training better-performing models requires a focus on edge cases, false positives, and the portion of minority classes or edge cases that are relevant to the modeling problem you’re trying to solve. And your curation process shouldn’t be overly time-intensive or manual. As we launched Nucleus, we <a href="https://scale.com/blog/introducing-scale-nucleus" rel="noopener noreferrer" target="_blank">explained</a> that better ML starts with understanding your data in depth. To improve production ML, you need to understand your models’ qualitative failure modes, fix them by gathering the right data, and curate diverse scenarios.<h2>From Similarity Search to Autotag:</h2> When Scale customers look to improve ML accuracy, they find that their models often struggle with minority classes and edge cases, what we refer to as “the long tail.” It might be a QA team that highlights challenges with a self-driving vehicle, like the relatively rare combination of entering a tunnel at nighttime behind a truck. Although this cross-section accounts for only a small fraction of driving scenarios, their computer vision models must be capable of handling them just as well. In the below example, we’ve identified uncertain samples, and using a similarity search based on internal models and feature vectors, surfaced similar portions of the dataset for labeling or QA. This process culminates in our Autotag feature:<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/907a4a44-3760-4607-8981-4fd69c669700/public" alt="Similarity search and Autotag"> Autotag is an incredibly efficient way to tag similar objects or scenes with a new class. Simply select a few similar images of the class you are trying to classify or create a tag for, click ‘Autotag,’ and then further refine your set of images with positive and negative examples. Scale Nucleus will then create an Autotag, an internal classification attached to the subset of your dataset that Scale identified as similar. You can then retrieve objects in your dataset based on this classification, even if labels for it don’t exist in your ground truth. <h2> </h2><h2> The same useful core, with refined usability:</h2><h2> </h2> Nucleus focuses on several main use cases that we’ve developed in conjunction with our customers: Understand the strengths and weaknesses in your dataset as you identify ways to improve quality: <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/3b36ea98-b2c3-42e9-c063-a4a2a400b200/public" alt="Understand the strengths and weaknesses in your dataset as you identify ways to improve quality."> Using the query bar at top or powerful search options at left , you can discover edge cases, debug model failures, and efficiently QA any part or your dataset. Analyze the long tail of your dataset, with collaborative label edits, and granular insights like Intersection over Union (IoU): <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/94c22661-e595-47af-3446-858a7ae4e700/public" alt="Analyze the long tail of your dataset, with collaborative label edits, and granular insights like Intersection over Union (IoU)."> Object view helps you assess all classes, labels, and bounding boxes in your dataset and make quick tweaks to a label, approve/reject the object as well-labeled, or send the object to be labeled. Examine metrics, failure cases, and confusion matrices, all linked to your underlying training data. <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/fd250417-e7f1-42bc-8df6-f1e141092d00/public" alt="Examine metrics, failure cases, and confusion matrices, all linked to your underlying training data."> The Insights tab provides you with interactive class distributions, correlations, and confusion matrices, from which you can access the underlying data for each category in one click. Scale Nucleus was built with the explicit goal of helping ML teams improve their datasets to extract better and better performance out of models they’re training on their data. Whether you’re starting out with an industry-standard dataset, and then execute transfer learning with the addition of proprietary data, or training a model entirely on your own data, Nucleus helps you train your model on the data that matters. <h2> </h2><h2> Privacy Mode: using Nucleus without sharing sensitive data</h2><h2> </h2> Several prospective customers asked if they could use Scale Nucleus without uploading their training datasets to our cloud. Accordingly, we created Privacy Mode in conjunction with our existing API, letting you use Nucleus to curate your dataset without sensitive raw data ever leaving your servers With Privacy Mode, you can submit URLs to Nucleus that link to raw data assets like images or point clouds, instead of transferring that data to Scale. These URLs may optionally be protected behind your corporate VPN or an IP whitelist. When you load a Nucleus web page, your browser will directly fetch the raw data from your servers without it ever being accessible to Scale. Privacy Mode even works well with similarity search and Autotag, as users can <a href="https://dashboard.scale.com/nucleus/docs/api#create-a-custom-index" rel="noopener noreferrer" target="_blank">create custom model embedding indexes</a> for their datasets. <h2> </h2><h2> What customers are saying about Nucleus:</h2><h2> </h2><blockquote> </blockquote><blockquote> “KeepTruckin encounters all manners of surprising edge cases in real world</blockquote><blockquote> data collection, so when it comes to knowing we’re labeling the most</blockquote><blockquote> valuable subset of our collected data, we turn to Scale Nucleus. Its</blockquote><blockquote> intuitive visualizations, query engine and Autotag help our teams improve</blockquote><blockquote> both data quality and models, all in the same motion.”</blockquote><blockquote> </blockquote>—Ali Rehan, Engineering Manager AI/Vision Products, KeepTruckin Nucleus makes it easy to query data samples based on their metadata, or simply bucket images based on “similarity,” specifically along feature vectors of a Scale-trained model. For example, if you know your model should be detecting police vehicles and it’s not doing so already, you can quickly query for a handful of examples, find similar images, and through simple positive and negative feedback to Nucleus, identify a subset of your dataset to send out for labeling. One year in, Scale Nucleus has already come a long way towards building a new generation of ML tooling, but we’re just getting started. If you’d like to join us in this journey and try Scale Nucleus, sign up <a href="http://scale.com/nucleus" rel="noopener noreferrer" target="_blank">on our website</a> or <a href="https://calendly.com/russell-kaplan/30min" rel="noopener noreferrer" target="_blank">schedule a demo</a>.

Scale Nucleus helps you curate and automatically tag your training data.

Nucleus relaunched: making data and model quality even easier.

Improving Datasets and Debugging Machine Learning Models with Scale Nucleus, Rebuilt from the Ground Up 

Artificial Intelligence (AI) and machine learning (ML) are increasingly being applied to high-stakes domains. One emergent domain involves applying ML to healthcare to assist doctors in providing better care for patients. In the United States, however, people of color face disparities in access to healthcare, the quality of care received, and overall health outcomes. In dermatology, for example, darker skin tones are underrepresented in dermatology residency programs, textbooks, research, and diagnoses. We aren’t the first researchers to investigate this problem. Academics such as <a href="https://www.mdedge.com/dermatology/article/216697/pigmentation-disorders/racial-limitations-fitzpatrick-skin-type" rel="noopener noreferrer" target="_blank">Dr. Susan Taylor</a>, <a href="https://news.mit.edu/2018/study-finds-gender-skin-type-bias-artificial-intelligence-systems-0212" rel="noopener noreferrer" target="_blank">Joy Boulamwini</a>, and <a href="https://proceedings.mlr.press/v81/buolamwini18a/buolamwini18a.pdf" rel="noopener noreferrer" target="_blank">Timnit Gebru</a> have made great strides in identifying racial limitations around critical issues such as facial recognition and healthcare. In reviewing these various papers and studies, we recognized a need for a better understanding of the underlying data fueling this research. Specifically, we sought to understand how this underrepresentation impacts an ML model’s ability to accurately diagnose various skin conditions. In collaboration with our research partner, MIT Media Lab, we analyzed and evaluated deep neural networks trained on clinical images in dermatology.<h1>Methodology:</h1> The team at Scale annotated 16,577 clinical images sourced from two dermatology atlases — DermaAmin and Atlas Dermatologico — with Fitzpatrick skin type labels. The Fitzpatrick labeling system, while not perfect, is a six-point scale originally developed for classifying sun reactivity of skin phenotype. The Fitzpatrick scale served as the basis for skin color in emojis and, more recently, the Fitzpatrick scale has been used in computer vision applications to evaluate algorithmic fairness and model accuracy. The annotated images represent 114 skin conditions with at least 53 images and a maximum of 653 images per skin condition. The annotations, which we are calling the Fitzpatrick 17k Dataset, have been open-sourced <a href="https://github.com/mattgroh/fitzpatrick17k" rel="noopener noreferrer" target="_blank">here</a> if you would like to explore the dataset in greater detail.<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/8176b7a8-94e7-494b-746f-17563912e200/public" alt="The Fitzpatrick Skin Type Scale"> After the dataset had been labeled, researchers at MIT trained a transfer learning model based on a VGG-16 deep neural network architecture pre-trained on the seminal ImageNet dataset to classify various skin conditions. For more details on how the model was trained, we encourage you to read the full paper <a href="https://arxiv.org/abs/2104.09957" rel="noopener noreferrer" target="_blank">here</a>. <h2>Research Findings:</h2> The research found that the data used to train a model does matter. By tagging the images with Fitzpatrick labels, researchers found that the dataset contains 3.6 times more images of the two lightest Fitzpatrick skin types than the two darkest Fitzpatrick skin types. The underrepresentation of dark skin images in the dataset — and in dermatology atlases more broadly — led to larger disparities in the model’s ability to correctly diagnose skin conditions involving darker skin tones. While more research is required to identify where accuracy disparities are greatest across skin types, this research empirically shows that the data a model is trained on matters. More importantly, it matters how data is collected and what type of data is collected. Before developing and deploying large-scale ML models in the healthcare sector, we encourage researchers and practitioners to consider and examine biases in training datasets to ensure healthcare disparities are not unintentionally amplified by these models.

Improving clinical dermatology diagnostics with better data

Scale AI & MIT Media Lab: Improving Clinical Dermatology Diagnostics

Scale AI and Research Partner MIT Media Lab Analyze Bias in Dermatological Datasets to Improve Diagnoses

Have you ever missed the on-ramp for a freeway because you were in the wrong
lane? Or had to circle a block in search of a package drop box or specific
entrance? These moments may just be minor, albeit annoying, inconveniences for
us as individuals. But for autonomous vehicles, ride sharing, or logistics
companies that make thousands of daily trips and deliveries, the costs of such
navigation errors add up quickly.
&nbsp;
Companies that depend on high-quality maps for precise navigation have two
off-the-shelf solutions:
<ol>
<li>Regular map applications like those used by individual</li>
<li>consumers do not provide enough detail and users can&rsquo;t layer their own data</li>
<li>on top of these maps in a scalable way.</li>
<li>HD map companies offer more turnkey solutions but customers</li>
<li>still can&rsquo;t edit or update the data themselves because the maps are owned by</li>
<li>the mapping companies. Furthermore, it&rsquo;s a time-consuming process to get</li>
<li>maps updated and customers must wait for vendors to cover new regions.</li>
</ol>
&nbsp;
Lacking a viable off-the-shelf option, many companies opt to build their own
maps. Scaling the process to generate large volumes of extremely precise
annotated map data poses the same challenges as scaling data pipelines for any
other AI application.
<h2>Introducing: Scale Mapping</h2>
&nbsp;
Scale Mapping gives customers the most
flexible, scalable, and transparent mapping solution. With Scale Mapping,
customers can generate high-precision maps down to the centimeter level for
simulations and real-world testing, enhance prediction and motion planning by
effectively predicting another agent&rsquo;s intent, train perception models to
live-detect map features, and improve navigation and route planning to
maximize efficiency.
<h3>How It Works:</h3>
&nbsp;
&nbsp;
High-precision annotations of large-scale maps are extremely challenging. To
minimize cognitive load for human annotators (Taskers) and to maximize
labeling efficiency and accuracy, Scale Mapping first breaks map sections down
into tiles. From there Taskers start with vector annotations, or annotations
of physical map features such as lane boundaries and crosswalks. Once the
vector annotations are complete, semantic annotations &mdash; or the annotation of
concepts &mdash; such as the location of package drop boxes or a bike lane becoming
a turn lane is layered on. Lastly, relationships between map features and map
semantics are linked, such as a traffic light to a specific traffic lane.
After all three layers of annotations are complete, the individual map tiles
are stitched back together to create a comprehensive map.
<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/b4e2e6c5-8eb3-4305-9a65-afb211dbc100/public" alt="How Scale Mapping Works Diagram">
&nbsp;
<h2>Real-World Impact</h2>
&nbsp;
Because it can support data inputs from LiDAR sensors, cameras, and even
existing maps, this workflow allows customers to develop, update, and own
their own proprietary HD maps at speed. Scale Mapping is already trusted by
leaders in the autonomous driving industry.
&nbsp;
&nbsp;
Nuro, one of the leaders in the self-driving delivery space, and Domino&rsquo;s,
the largest pizza company in the world based on global retail sales,
launched autonomous pizza delivery in Houston. Nuro&rsquo;s R2 robot is the first
completely autonomous, occupantless on-road delivery vehicle with regulatory
approval from the U.S. Department of Transportation.
&nbsp;
&nbsp;
<iframe class="ql-video" src="https://nuro.sfo3.digitaloceanspaces.com/Nuro-Delivery-B-Roll-2021.mp4?mtime=20210411191557&amp;focal=none" frameborder="0" allowfullscreen="allowfullscreen"></iframe>
&nbsp;
&nbsp;
Scale partnered with Nuro to improve perception and mapping models for the
R2, starting with providing high-quality, rapidly labeled data. With Scale
Mapping, Scale is moving up the autonomous driving stack by enhancing
localization and mapping capabilities. Put simply, the R2 needs to know
where it is, what&rsquo;s around it, where it&rsquo;s going, and how to get there. The
data Scale provides lets the R2 do exactly that while ensuring the safe,
accurate, and efficient delivery of pizzas.
&nbsp;
&nbsp;
<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/d156b7f7-dc7e-4f28-0442-9c4317206b00/public" alt="Nuro Quote">
&nbsp;
&nbsp;
In terms of the impact across our Nuro projects, we doubled the amount of
data annotated from 2020 to 2021.
&nbsp;
&nbsp;
Talk to our team today for a demo and more information about our
industry-leading quality with transparency, flexibility, and rapid
turnaround.
&nbsp;

The flexible solution to develop and scale your own custom maps.

Introducing: Scale Mapping

Own Your Own Maps. Introducing: Scale Mapping

We are honored to share that Scale AI has been named to <a href="https://www.cbinsights.com/research/report/artificial-intelligence-top-startups/" rel="noopener noreferrer" target="_blank">CB Insights’ AI 100</a> 2021 ranking of the most innovative private AI startups – highlighting our leading data annotation services. Five years ago, we built the foundational infrastructure for AI applications, labeling data to generate high-quality, large-scale training datasets. This past year, we expanded beyond data annotation launching Scale Nucleus to provide a single platform for dataset management, data selection and data annotation, to model development. Today, Scale AI serves as a trusted partner for organizations across industries like finance, transportation, e-commerce and more. All companies recognized on CB Insights’ list are private companies providing data infrastructure and hardware for AI applications, as well as those optimizing machine learning workflows and applying AI across a variety of industries including finance, healthcare, agriculture, insurance and more. The CB Insights research team selected the list based on patent activity, investor quality, news sentiment analysis, proprietary <a href="https://www.cbinsights.com/company-mosaic" rel="noopener noreferrer" target="_blank">Mosaic scores</a>, market potential, partnerships, competitive landscape, team strength and tech novelty. Looking ahead, Scale AI will continue to develop new products and services that address every phase of the AI development lifecycle to ensure Scale is the unified platform for AI development. If you’re interested in joining us on our mission of accelerating the development of AI, check out our <a href="https://scale.com/careers?location=All&amp;team=All" rel="noopener noreferrer" target="_blank">open positions</a>.

Scale AI has been named to CB Insights’ AI 100 2021 ranking of the most innovative private AI startups – highlighting our leading data annotation services.

Scale AI Named to CB Insights’ AI 100 2021 List

At Scale AI, we envision a future where AI is as easy to build as any software. To help AI achieve its full potential, we are building the tools that make it easier to build and scale real-world AI systems. We began by focusing on the data - the foundation of all AI applications. With the Scale AI platform, customers can understand, visualize, and curate raw datasets, send raw data to be annotated, then debug model performance - all in one place. From the beginning, we knew the Scale AI platform had to be API-based to enable teams working on AI and ML to automate their data pipelines and seamlessly integrate Scale into ML development cycles. Since our start in 2016, our engineering team has built incredible products to deliver large volumes of training data at high-quality, whether the data input is 3D sensor data, video, image, document, or text. Despite the continued evolution of our products and the robustness of our APIs, until recently, the API documentation itself was static, with limited interactions and not a lot of details to fully understand our products, set up effective workflows, and optimize for quality.<h2>The Evolution of our Documentation:</h2> If you are a long-time Scale customer, you are probably familiar with this as our API documentation:<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/e4b49b7e-e94c-4e83-558b-ed0436a2c600/public" alt="Scale AI's Original API Documentation"> The early documentation simply served as an API reference guide. The parameters were well spaced out, included relevant examples, and showed some example code using our SDKs. As we expanded the documentation to include content on key concepts, the customer dashboard, and other elements of the Scale AI platform, the documentation became increasingly clunky and awkward to navigate. The early documentation was also very static, with limited interactions, no search capability, and overall proved to be a less than ideal user experience. <h3>Transition to ReadMe</h3> Earlier this year, our Customer Experience and Field Engineering team identified <a href="https://readme.com/" rel="noopener noreferrer" target="_blank">ReadMe</a> as an industry-leading documentation platform that could scale with our evolving needs. With ReadMe, we were able to realize three tangible benefits:<ol><li> </li><li> Unify the API reference and the Customer Dashboard documentation into a</li><li> unified website.</li><li> </li><li> </li><li> Improve the developer experience. Instead of only showing static code</li><li> examples, ReadMe enables developers to submit API requests directly with</li><li> their Scale API key. ReadMe also has built in search functionality to make</li><li> it easier for developers to find specific topics.</li><li> </li><li> </li><li> Democratize the content creation process. Instead of editing raw markdown,</li><li> making a pull request, and then deploying to a server, we just had to</li><li> update the text we needed in the ReadMe UI. Ultimately, the ReadMe</li><li> platform provided a richer and more dynamic user experience.</li><li> </li></ol> The only limitation of the ReadMe platform out-of-the-box was the lack of front-end customization to ensure visual consistency between the documentation and the rest of our website. <h3>Latest State:</h3> The latest update to our documentation marries the content and functionality of ReadMe with our front-end to provide a better and more consistent experience between our API documentation and the rest of our platform. <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/e120309c-17d5-479b-4e47-fce17cdea400/public" alt="Latest State of API Documentation"> We accomplished this by effectively leveraging ReadMe as a content management system (CMS) on the back end. We then used <a href="https://tailwindcss.com/" rel="noopener noreferrer" target="_blank">Tailwind CSS</a>, a utility-first framework to build the UI for the front end, and <a href="https://nextjs.org/" rel="noopener noreferrer" target="_blank">Next.JS</a>’s static generation feature to pre-render the page into HTML on the server ahead of each request. The HTML can be globally cached by a CDN and served instantly. Technical details on how we implemented this will follow in another blog post. <h2>New Features:</h2><h3> </h3><h3> In-depth Customer Dashboard Documentation</h3><h3> </h3> As a Scale AI customer, the Dashboard is the central location to manage your account and your projects. The new in-depth Customer Dashboard documentation walks you through how to manage your account, your projects, get an overview of submitted and completed tasks, and audit a random sample of tasks for quality. <h3>Scale 101 Workflow Overviews</h3> Every institution, company, and even team has their own ways of describing the data labeling workflow. To ensure every customer is successful on the Scale AI platform, we introduce the terminology we’ve developed in this section. <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/f3a4368d-6d0d-438c-0590-d59ddd39ad00/public" alt="Scale 101: Key Concepts"> <h3> </h3><h3> Make API calls using your Scale API key directly from the Documentation</h3><h3> </h3> Instead of static code examples, this feature enables developers to submit live API requests directly using their Scale API key. <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/8c6eac81-5b29-4e0a-0d0b-1f9feebee400/public" alt="API Key"> <h3>Search</h3> As we continue to expand the documentation, we realize it can also become more challenging to navigate to and find details on a specific topic. To offer a faster and easier way to find keywords or key phrases, we have added a new search feature in our documentation powered by Algolia. <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/f5cef952-a43c-429e-62a6-0eafe33f6e00/public" alt="Search Functionality"> <h3>Dark Mode</h3> Dark mode reduces the light emitted by device screens while maintaining the minimum color contrast ratios required for optimal readability. To view the API documentation in dark mode, select “Dark” in your computer’s display settings or “Auto” to switch from light mode to dark mode at sunset or at a specified time. <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/3c6ca586-71f7-4928-de8e-d4ee154fff00/public" alt="Light Mode/Dark Mode"> <h2>What this Means:</h2> If you are an existing Scale customer, rest assured that the updates to the documentation do not impact any existing projects, batches, tasks, or workflows. If anything, we hope these updates will clarify the expected behavior, showcase our API’s flexibility, and enable easier task creation with more effective instructions to improve the overall quality of returned tasks. If you are just getting to know our products, we hope the updated documentation inspires you to come up with new project ideas.

Introducing a new look and new features for our API Documentation