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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

If you were not able to join Transform live, the keynote and breakout sessions are now available on our website for <a href="https://scale.com/events/transform" rel="noopener noreferrer" target="_blank">on-demand viewing</a>.Artificial Intelligence is now a part of our everyday lives. This technology is no longer theoretical, but is making a direct impact on businesses and their bottom lines. Nearly every industry has woken up to the potential of AI to transform their operations, whether it’s leading the charge of an entirely new category, like autonomous transportation, or modernizing legacy industries, like finance, real estate, and retail. AI is poised to be as transformative as the internet, and with that, organizations are learning that data is the new code. If they’re not investing in this critical technology now, they’re at risk of being left behind.Today we hosted our inaugural conference, Scale Transform, to showcase the state of AI today, from the latest research breakthroughs to the real-world impact across industries. As I shared in my opening remarks, “AI continues to surprise us with its new capabilities, primarily enabled through advancements in the core resources of data, compute, and algorithms.”We brought together more than 9,000+ leading researchers, practitioners, and executives to hear from the people and companies leading our industry forward. It was a day full of thoughtful conversations and presentations that examined the strides made in advancing these core resources, the challenges that still lie ahead, and what we can expect in the years to come.<h2>The Latest Advancements in Research</h2>AI research has been crucial in advancing the industry and our understanding of what’s possible. We’ve made significant progress across countless areas that have propelled the industry forward, yet there is still plenty more to be done. Some of the latest research discussed includes:<ul><li>Artificial General Intelligence (AGI) - Sam Altman, Co-Founder and CEO of OpenAI, touched on breakthrough research such as GPT-3, CLIP, and DALL·E, bringing us closer to achieving AGl. He said, “We may not even know when AGI actually emerges - it won’t be a single moment in time. The Turing Test for achieving AGI will be when AI is able to complete a percentage of human labor really well.”</li><li>Machine Learning Frameworks - The ecosystem of ML frameworks is evolving quickly. Google Software Engineer and Keras Creator Francois Chollet discussed how the roadmap for Keras in the next five years includes developing an ecosystem of reusable models such as KerasNLP and Keras Applications, increasing automation, enabling larger-scale cloud-based workflows, and supporting real-world deployment. Separately, Soumith Chintala, the Creator of PyTorch, shared what he considers the three personas in ML Development: The Modeler, Prod, and Compiler and discussed how these personas interacted with each other pre and post-deep learning. He closed his session by highlighting the need for PyTorch and other mainstream frameworks to maintain a high velocity to stay relevant and keep up with the latest innovations to enable Modelers.</li></ul><img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/e15ac9ef-de00-4d59-28e4-1f16f6790900/public" alt="Soumith_Chintala_at_Transform">Soumith Chintala, Creator of PyTorch speaking on the future of ML frameworks.<ul><li>Autonomous Vehicles - Drago Anguelov, Distinguished Scientist and Head of Research at Waymo discussed the current state of perception, highlighting that, “Perception works very well for the vast majority of cases, but there are still some interesting long-tail problems like rare events that still need to be addressed.” Despite the remaining challenges, Anguelov discussed how the self-driving industry is beginning to “move up the stack” to tackle behavior prediction and planning, including the recent release of the updated Waymo Open Dataset to support further research into behavior prediction and motion forecasting. Anguelov also discussed how Waymo has successfully deployed L4 level autonomous vehicles in Chandler, AZ.</li></ul>This research and advancements made by others in the industry, including our conference partners, Stanford Institute for Human-Centered AI (HAI), PyTorch, DeepLearning.AI, OpenAI, Pachama, and ELC, are the catalysts for meaningful use cases of AI.<h2>Challenges Ahead</h2>One of the biggest challenges for our industry is scaling AI systems from proofs of concept to production. A common theme heard from speakers throughout the day is that data is the critical foundation that companies must invest in to unlock AI’s full potential: Andrew Ng of DeepLearning.AI discussed how models can improve with more compute, data and parameters, however, it's not just about how much data you have, but the quality of that data. Andrew brought up a critical point here, because developing AI with bad data means it won’t live up to the potential and quite frankly it’s dangerous. It can lead to:<ul><li>Safety risks (e.g. self-driving cars not accurately identifying pedestrians or obstacles)</li><li>Biased algorithms (e.g. facial recognition bias)</li><li>Inaccurate results / recommendations (e.g. AI-driven healthcare decisions)</li></ul><img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/35e8924f-cf73-4df9-a604-4540068a7400/public" alt="Andrew_Ng_at_Transform">Andrew Ng, speaking on the need for good data to develop AI.Bart Nabbe, VP of Strategic Partnerships and Corporate Development at Aurora, reinforced this point, stating, “You’re not building ladders to get to the moon, you need rockets.” And data is the fuel for those rockets. He emphasized that in the early days of training ML, every piece of data counts, however over time it’s increasingly important to be precise and focus on the long-tail, differentiated data to improve models.The importance of creating ethical and unbiased AI has been a focus in the industry for years, and as the technology has gone mainstream, so has the conversation. Global leaders and technology experts across the world are tackling challenges in developing AI that is ethical, as well as ensuring the benefits of AI can be equally distributed. Our speakers provided passionate advice on the topic:<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/a44a923d-4bb8-4a54-a5d1-e76d926c1300/public" alt="Kevin_Scott_at_Transform">Kevin Scott, CTO of Microsoft at Scale Transform.<ul><li>Kevin Scott, Chief Technology Officer of Microsoft, enumerated some of the challenges to ensuring that the benefits of AI are equally distributed. “We have an enormous amount of work to do. […] We’re learning a lot about biases in datasets, how to manage potential harms, ensuring the development of AI is inclusive, and that AI is being used safely.” Scott then spoke about how his teams at Microsoft are developing the tools (e.g., PowerBI, autoML) to enable developers without machine learning expertise to develop ML applications.</li></ul><img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/836e4864-7ac9-4153-c016-6eb81d57dd00/public" alt="fei-fei_li_at_transform">Dr. Fei-Fei Li, speaking on the need for diversity in the field of AI.<ul><li>Diversity in the field of AI and greater interdisciplinary collaboration is key to minimizing the unintended consequences of AI, and Dr. Fei-Fei Li, Sequoia Professor of Computer Science, Stanford University, Denning Co-Director, Stanford Institute for Human-Centered AI (HAI) believes that as we continue to build AI, the industry needs to take inspiration from and embed human cognition, intention, and emotion in the technology for ML to achieve its full potential to augment human capabilities.</li></ul><h2>Real-World Impact</h2>Perhaps most importantly, we also heard inspiring work that’s happening in the real world – tangible examples of how AI is already improving the way we live and operate:<ul><li>Facebook reaches billions of people across the world and often needs to deploy products quickly, at scale from translations to social recommendations to generating descriptions of images for the visually impaired. On an average day, Facebook is processing 6 billion transactions a day. Head of Applied Research at Facebook AI, Srinivas Narayanan, relayed the importance of “getting the most bang for your buck” for your labeled data by augmenting it with various techniques, and creating AI responsibly by viewing fairness as a process and not a binary notion in order to implement the best AI across a variety of use cases.</li><li>The financial industry has complex workflows filled with massive amounts of paperwork that often includes tedious manual processing. During a panel, Brex’s Co-Founder and Co-CEO Henrique Dubugras discussed how applying AI technology like document understanding has helped the financial world increase efficiencies and lower costs while freeing up humans to focus on more fulfilling work.</li></ul><h2>What’s Next</h2>The continued advancements in AI have created incredible applications in today’s world but there is an exciting future that awaits - from the potential to create safer transportation to agricultural benefits like increasing crop yields and improving healthcare outcomes. At Scale, we’re excited to have a hand in creating trusted, ubiquitous use of AI and we can’t wait to see what’s next.A special thank you to our conference partners: Stanford Institute for Human-Centered AI (HAI), PyTorch, DeepLearning.AI, OpenAI, Pachama, and ELC. We are also grateful for all our incredible speakers representing organizations including Aurora, Blend, Brex, Cruise, DoorDash, Etsy, Facebook AI, Fast.AI, Flexport, Google, Hugging Face, iRobot, Microsoft, Qualtrics, RedFin, University of Toronto, and Waymo.If you were not able to join us live, the keynote and breakout sessions are now available on our <a href="https://scale.com/events/transform" rel="noopener noreferrer" target="_blank">website for on-demand viewing</a>.

Scale AI Hosts Inaugural Scale Transform, Showcasing AI’s Potential to Improve Our Lives

Scale Transform 2021 | The Future of AI

Scale Transform: AI Leaders Showcase Research Breakthroughs and Real-World Impact

Getting Started with Nucleus

How the machine learning team at Scale AI improved Pytorch Transformer architecture on sequence generation. 

Make Pytorch Transformer twice as fast on sequence generation.

Making Pytorch Transformer Twice as Fast on Sequence Generation.

Today we are excited to share several key updates that build on Scale AI’s mission to accelerate the development of AI. First, we are officially announcing Scale’s Series D funding round. Following our Series C fundraise last year, Scale has raised $155M at a post-money valuation north of $3.5B led by Tiger Global. We are in the midst of one of the most impactful technology transformations of human history with AI. The potential applications of AI are going to deeply improve the world: scalable medical diagnosis to everyone in the world, massive reduction in driving accidents, goods which are dramatically cheaper to manufacture and transport, and much more. What’s holding us back from building those dreams is lack of infrastructure. While many organizations are adopting AI, they are forced to build their technology stack from scratch, significantly slowing progress. Four years ago, we recognized this problem and sought to unlock the bottlenecks holding back AI development. We started by building a platform that solves the necessary, foundational layer that fuels all machine learning models: large annotated datasets. Our solution gives developers the ability to send data to our platform through the Scale API and receive high-quality training data that’s ready for building production-grade AI algorithms. We use a combination of machine learning and human intelligence to annotate tens of millions of data points per week for hundreds of customers like OpenAI, DoorDash, Pinterest, General Motors, NVIDIA, Nuro, and Toyota. We’ve chosen to stay focused on the applications of AI with the greatest potential for impact: autonomous driving, government, and cutting-edge consumer technology. As AI has transformed every industry, we’ve continued to see demand for our platform to grow—from e-commerce and AR /VR to relevance &amp; personalization and financial services. We are still very early in the journey of AI, and we remain optimistic about all the future dreams we will support. Our team is tackling the next bottleneck in AI development: managing the full life cycle of AI development across teams. Building and deploying scalable machine learning infrastructure is slow, manual and costly, and in August we launched <a href="https://scale.com/nucleus" rel="noopener noreferrer" target="_blank">Nucleus</a> to enable seamless collaboration across the AI development cycle. Teams can visualize and organize their datasets, test and compare models, and share data with colleagues. With Nucleus, our goal is to become the mission control for your data, and over time we’ll be rolling out support for more data types and adding more features to make developing AI easier and faster than ever.<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/0620751d-5f96-4b5f-0469-e11b65805a00/public" alt="Scale_Nucleus_ML_Development_Cycle"> As part of today’s announcement, we are also excited to announce the acquisition of Helia AI. Helia’s team brings deep expertise in applying AI to real-time video, from core algorithm development to necessary AI infrastructure. Their AI infrastructure experience aligns closely with what we’re building with Scale Nucleus, and the Helia team will help advance the Scale platform as we expand its capabilities. We look forward to continuing our work of building AI infrastructure with Scale’s hundreds of customers, and our new funding will allow us to accelerate hiring, expand to new markets and build more robust products to match the evolving needs of AI as the technology matures. We’re still in the early days of AI, and we believe Scale will enable a future where AI is as easy to build and manage as any other software. If you are interested in joining us on our mission, check out our <a href="https://scale.com/careers" rel="noopener noreferrer" target="_blank">open positions</a>. If you’re interested in partnering with us, please <a href="https://scale.com/sales" rel="noopener noreferrer" target="_blank">reach out</a>.

Accelerating the development of AI applications and empowering AI dreamers with a new round of funding led by Tiger Global. 

Scale AI's Series D: Expanding our team to empower AI dreamers

Scale AI’s Series D: Expanding our Team to Empower AI Dreamers

When Covid-19 first hit, many companies had to rapidly reorient their operations – from altering their supply chains to implementing social distancing and work-from-home orders. One sector that was particularly hard hit was the restaurant industry. When in-person dining was suspended, restaurants turned to delivery to survive. But setting up a delivery service from scratch takes time and money that many establishments, with their income drying up, did not have. Suddenly, food delivery platforms became an economic lifeline. To ensure restaurants could onboard quickly and seamlessly in these critical early days, one of the leading delivery platforms turned to Scale.<h3>Scaling labeling</h3> Every time a restaurant joins one of the delivery platforms or changes its menu, the menu’s contents need to be inputted into the platform so users can select their food. Inputting menus manually is slow, expensive, and leads to mistakes. With restaurants relying on delivery to reach customers and support their staff, a leading delivery platform knew it was vital to ensure an efficient process for its partner restaurants. This presented Scale with unique latency and quality control challenges, requiring both an incredibly high level of accuracy with a fast turnaround to provide operational efficiencies at a critical time for our customer. Our aim was to use the combination of partial automation and human quality control in our <a href="https://scale.com/document" rel="noopener noreferrer" target="_blank">Scale Document</a> labeling pipeline to make dramatic efficiency improvements over manual processes. In all, we nearly halved the time it takes our customer to process menus and reduced critical error rates to &lt;1% for all items labeled, allowing them to onboard restaurants smoothly exactly when they needed it.<h3> </h3><h3> Building the right data infrastructure</h3> While processing standardized documents such as food menus, government IDs or loan application forms might be intuitive and simple for humans, it is a surprisingly nontrivial problem for algorithms, involving much more than automatic transcription. In particular, the data often contains many dependencies that need to be captured accurately. For example, ordering a pepperoni pizza doesn’t just mean choosing the type, but also the size, the type of base, choosing to substitute or customize toppings (which can also vary in price depending on the size of the pizza, creating more interdependencies), and whether to add extras like burgers or sandwiches. It’s important to capture these dependencies in the menu correctly, otherwise inputting errors risk causing losses for both the restaurant and the delivery platform.<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/e10f107d-1efe-45c5-45a3-db2291017800/public" alt="DD_decision"> Algorithmically, this has to be represented as complex decision trees made up of categories, items, and lists of options, capturing the relationships between them. Thankfully, both our document processing pipeline and form capture tools were already set up to capture these types of nested data structures automatically without the need for customization. This meant we were able to quickly pivot our tools for our customer’s requirements. <h3>Judicious Automation and Scaling</h3> Once we have the right tooling in place to pull the raw data and label complex data structures, we can then start processing the data. Given how many menus delivery platforms were processing at the start of shelter-in-place, and how important for restaurants’ continued operations it was to process this data quickly with no drop-off in quality, we need to make this process as efficient as possible. At Scale AI, we believe that a mix of judicious labeling automation with continued human oversight is the only way to provide data at the scale, quality, and low cost needed to enable many enterprise applications of AI. In particular, we aim to automate the lower-skilled parts of the data labeling pipeline to focus human review on the hard parts – quality control, edge cases, and the most complex data types. That way, we can help guarantee quality without sacrificing efficiency. We use automation to prefill menu taxonomies for expert human labelers to review and confirm. We trained our form capture tool to automatically understand document structure and predict the next transcription. And when our labelers needed to add inputs manually, we built a “smart suggestion” feature that provides them with ready prompts for common items that they have already encountered in their batch of menus. Both have helped improve both the efficiency and accuracy of labeling simultaneously: allowing us to transcribe more than 3,000 menus in a single day and return menus within short turnaround times to onboard restaurants within 24 hours from start-to-finish. By drawing on our wide network of well-trained labelers and augmenting their work with careful automation, we’re able to handle changes in demand dynamically – essential for providing a smooth service during sudden peaks in demand. <h3> </h3><h3> Custom Benchmarks and Integrations</h3><h3> </h3> Once data is flowing smoothly and efficiently through this labeling pipeline, the next step is to ensure that the data is being labeled to an incredibly high standard, with almost zero errors. We’ve developed a range of techniques to guarantee label quality, including the randomized use of benchmark quality tests, confidence-based consensus, and, for ambiguous labeling requirements, our own <a href="https://scale.com/blog/how-to-label-1m-data-points-week" rel="noopener noreferrer" target="_blank">automated benchmark generation system</a>. Benchmarking is not always an objective science – the most effective benchmarks are often task-specific. The high stakes of onboarding restaurants during a pandemic required particularly high guarantees of labeling quality. To meet them, we worked closely with our customer to build custom benchmarks for this workflow, turning the most complex mislabeled menus into benchmarks that assessed our labelers’ performance. In surveying mislabeled menus, we noticed two key groups of errors. One was associated with the cross-referencing of menu sections between many items and mistaking how they relate to each other, while the other group tended to be mistakes from larger menus where it may be less obvious when certain features of the menu are missing. By identifying these key groups and creating benchmarks out of the associated menus, we can ensure that tasker performance in such cases exceeds the necessary standard to meet menu quality needs. <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/fc0a9a32-08be-4505-422b-a05d016c3f00/public" alt="Menu_dd_photos"> The menu on the left showcases large amounts of cross-referencing and item optionality, while the menu on the right shows a fairly common amount of menu feature density. <h3>What’s Next</h3> AI models are rapidly becoming critical infrastructure for a huge range of businesses – a trend of automation that the COVID pandemic has rapidly accelerated. But gathering effective, accurate, and unbiased data currently remains such a challenging task that it is preventing real-world AI systems from reaching their full potential and presenting high complexity barriers for smaller companies. We’re tackling a whole host of problems to help accelerate that progress. Right now, teams at Scale AI are making better use of machine learning to make labeling orders of magnitude more efficient, building tools to process increasingly complex data types, such as 3D point clouds in computer vision, and developing new infrastructure tools to help streamline the management of data. Our first such management tool, <a href="https://scale.com/blog/introducing-scale-nucleus" rel="noopener noreferrer" target="_blank">Scale Nucleus</a>, is already helping our computer vision customers automate time-consuming manual steps in the ML development process. We’re now working on deploying it for our customers in natural language. Ultimately, we want to make it as easy to deploy AI as any other type of software. If you’re interested in joining us in solving these problems, take a look at our <a href="https://scale.com/careers" rel="noopener noreferrer" target="_blank">careers page</a> for our latest open positions. If you have projects that require high-quality data labeling, <a href="https://scale.com/sales" rel="noopener noreferrer" target="_blank">let our team know</a>!

How Scale AI worked with a leading food delivery platform to ensure restaurants could onboard quickly and seamlessly. 

Rapidly scaling menu transcription to support restaurants during the pandemic

Scaling Menu Transcription Tasks with Scale Document

The Scale AI team hosted its first ever Hackathon last month. The hackathon was designed to:<ol><li> </li><li> Encourage innovative thinking to solve some of Scale’s biggest challenges</li><li> </li><li> </li><li> Prototype and potentially launch impactful projects that make Scale better</li><li> in practice</li><li> </li><li>Facilitate long-term cross-functional relationships</li><li>Give all Scaliens a creative break from their daily work.</li><li> </li></ol> Scaliens across functions and teams leapt to the challenge, proposing 63 ideas and ultimately working on 10 separate projects comprising of 37 team members. Projects ranged from new product proposals to better internal search, from refactoring tech debt to streamlining instructions for Taskers.<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/e7572ed8-8b09-48ee-088a-063ab2078f00/public" alt="Nofel_Naoman_Hackathon_Quote"> <h2>Project Highlights:</h2> The following are just some of the projects Scaliens tackled during the Hackathon. As a disclaimer, most of these projects are not live, nor do they indicate immediate plans to build these ideas. <h3>Super Instructions</h3>Winner: Best In ShowTeam Members:<a href="https://www.linkedin.com/in/svanweelden/" rel="noopener noreferrer" target="_blank">Shaun VanWeelden</a>, <a href="https://www.linkedin.com/in/taisiakaraseva/" rel="noopener noreferrer" target="_blank">Taisia Karaseva</a>, <a href="https://www.linkedin.com/in/jeffrey-linj/" rel="noopener noreferrer" target="_blank">Jeff Lin</a>, <a href="https://www.linkedin.com/in/galenbertozzi/" rel="noopener noreferrer" target="_blank">Galen Bertozzi</a>, <a href="https://www.linkedin.com/in/rashidlasker/" rel="noopener noreferrer" target="_blank">Rashid Lasker</a>, <a href="https://www.linkedin.com/in/ivan-roumec-b0450a6b/" rel="noopener noreferrer" target="_blank">Ivan Roumec</a>, <a href="https://www.linkedin.com/in/nofel-naoman-aa28a0142/" rel="noopener noreferrer" target="_blank">Nofel Naoman</a>, <a href="https://www.linkedin.com/in/nba/" rel="noopener noreferrer" target="_blank">Lawder Paul</a>Problem Statement: Annotation instructions are critical for the development of high-quality training data. Today’s annotation instructions are mostly made on static, non-interactive documents, not connected to the API payloads creating the task and requiring 3rd party solutions to host. Solution: The Super Instructions team tackled this problem in 3 ways: <ol><li> </li><li> Extending the Task Schema used in our APIs to accommodate labeling</li><li> instructions.</li><li> </li><li> </li><li> Creating an in-app instruction builder that simultaneously creates the</li><li> instructions as well as the API payloads that would need to be leveraged</li><li> in task creation.</li><li> </li><li> </li><li> Bringing a world-class interactive design to the in-app educational</li><li> experience for those doing the annotating, including features like search,</li><li> navigation, and zoomable images.</li><li> </li></ol> The goal of this prototype was to re-imagine the experience both our customers and Taskers have when creating and completing work on Scale’s platform. We firmly believe better instruction design and more consistency leads to higher-quality annotations for all involved. <h3>Corp Search</h3>Winner: Audience ChoiceTeam Members:<a href="https://www.linkedin.com/in/nba/" rel="noopener noreferrer" target="_blank">Lawder Paul</a> and <a href="https://www.linkedin.com/in/james-lennon-30421078/" rel="noopener noreferrer" target="_blank">James Lennon</a>Problem Statement: “Corp” is Scale AI’s admin portal for employees to gather information on on-going projects and to influence the quality and outcome of data annotation. The Scale AI team has launched countless tools and introduced new functionalities to Corp to improve our operations but the Corp portal itself was not very user friendly. There are now so many tools in Corp that it can be hard to find the exact tool, and for new Scaliens it’s not always clear what different tools do. <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/6a652d50-f749-461b-3c7a-dc221ec55600/public" alt="Scale Corp Search"> Solution: Corp Search allows employees to quickly search across all areas and tools within Corp, packaged in an elegant "command-line" interface that can be surfaced anywhere in Corp. <h3>Corp Migration</h3>Winner: Most Immediately ImpactfulTeam Members:<a href="https://www.linkedin.com/in/diegomura/" rel="noopener noreferrer" target="_blank">Diego Muracciole</a>, <a href="https://www.linkedin.com/in/federicomarcos/" rel="noopener noreferrer" target="_blank">Federico Marcos</a>, <a href="https://www.linkedin.com/in/martin-cabral-b0897615/" rel="noopener noreferrer" target="_blank">Martin Cabral</a>Problem Statement: As stated in the Corp Search project, Corp had become a complex and sophisticated tool used by many to provide the best service to our customers. Many of the Tasker and customer-facing features and tools were tied together or interlinked with Corp, making additional engineering work more challenging. Solution: The team spent the week separating these Tasker and customer-facing features were separated from Corp. The team also developed a framework to encourage internal modularity and code reuse to enable faster deployment in the future as Scale continues to grow. The team also ensured backwards compatibility with previous code to ensure daily operations could continue. This project has already enabled several refactors and performance improvements. <h3>The Great Refactor</h3>Team Members:<a href="https://www.linkedin.com/in/liuandrewk/" rel="noopener noreferrer" target="_blank">Andrew Liu</a>, <a href="https://www.linkedin.com/in/nick-induni-44357292/" rel="noopener noreferrer" target="_blank">Nick Induni</a>, <a href="https://www.linkedin.com/in/steven-hao-160b9638/" rel="noopener noreferrer" target="_blank">Steven Hao</a>Problem Statement: Like many fast-growing startups, Scale AI has accumulated some level of tech debt in order to quickly deliver new features and products to our customers. Solution: The Great Refactor team looked to tackle this issue by investing a week of their time to improve code quality to optimize for long-term maintainability and development velocity. <h3>Tasker Scoresheets</h3>Team Members:<a href="https://www.linkedin.com/in/akshat-bubna-188885103/" rel="noopener noreferrer" target="_blank">Akshat Bubna</a>, <a href="https://www.linkedin.com/in/rilka/" rel="noopener noreferrer" target="_blank">Rilka Li</a>, <a href="https://www.linkedin.com/in/alan-yu-954926110/" rel="noopener noreferrer" target="_blank">Alan Yu</a>Problem Statement: Although Taskers (our global data annotation workforce) regularly receive feedback on the quality of their submissions, it is difficult to provide granular feedback with specific areas of strength and weakness. Solution: The team developed Scoresheets that could be accessed on the Tasker dashboard. The dashboard allows Taskers to track their individual performance on specific skills over time and provides learning resources to improve at weak skills. <h3>Scale Grep</h3>Team Members:<a href="https://www.linkedin.com/in/jonfung1/" rel="noopener noreferrer" target="_blank">Jonathan Fung</a>Problem Statement: Instead of searching through command-line grep, IDE search, or Github search, which can be slow and unwieldy, engineers need quick, interactive, on-the-fly code search that can generate quickly sharable links. Solution: We deployed two open-source projects, Livegrep and Webgrep internally, and added automated index-generation so that the code in our git repositories gets indexed every 2 hours. Now, engineers can search through our large codebase in milliseconds and click code fragments to generate sharable links to exact lines of code. <h2> </h2><h2> Facilitating Interactions to Foster Cross-Functional Relationships</h2><h2> </h2> In addition to working on impactful projects, another goal of the Hackathon was to facilitate cross-functional relationships. Fostering these connections when everyone is remote can be particularly tricky. How do you recruit teammates to work on your project when you can’t organically run into folks in hallways? How do you whiteboard ideas, or learn a new skill from teammates when you can’t physically sit together? <h3>Pitch Session</h3> To help idea originators find teammates, we held a company-wide pitch session a week before the Hackathon. Scaliens then spent the following week signing up for the pitched projects and forming cross-functional teams. <h3>Events and Activities</h3> <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/9e1ad977-b0ea-4bc4-1903-e9cc346ab700/public" alt="Scale_Hackathon_Zoom"> To help Scaliens develop new skills, we organized workshops throughout the week of the Hackathon. Workshops included: <ul><li>A session on Storybook for planning engineering deployments</li><li> </li><li> A session on Figma to understand Scale’s design resources and to enable</li><li> better designing and prototyping</li><li> </li><li>A session on Tableau to enhance data visualization.</li></ul> We also organized daily social activities and special events throughout the week to facilitate social bonding. These activities included a Daily “Brain Game” and a virtual Game Night. To help everyone get into the spirit of the Hackathon (whether they were on a team or not), we fully branded the hackathon experience. Our Brand Director, <a href="https://www.linkedin.com/in/tywilkins/" rel="noopener noreferrer" target="_blank">Ty Wilkins</a>, created custom Zoom backgrounds, and branded swag, while Product Designer, <a href="https://www.linkedin.com/in/chun-jiang-0428/" rel="noopener noreferrer" target="_blank">Chun Jiang</a>, designed our Paranormal Hacktivity mascot. <img src="https://cdn.builder.io/api/v1/image/assets%2Fe0438815ba51486bbb6a202747122d4b%2F2c8bc6da94334d25a8882cfda83bdcfe" alt="scale_blog_hackathon_ghost"> <h3> </h3><h3> Presentations and Hackathon Wrap-up</h3><h3> </h3> Once projects were wrapped up, the teams presented their projects to the entire company. A team of internal judges gave out awards for “Best-In-Show” and “Most Immediately Impactful” and the entire company voted on a “Audience Choice” award. After the final presentations, Scaliens gathered in a virtual stretch session to unwind and relax. We hope this blog inspires you to host your own virtual hackathon or spark ideas for a hackathon that is in the works. If you’re interested in tackling new challenges and joining a collaborative and exciting team, take a look at all of our <a href="https://scale.com/careers#jobs" rel="noopener noreferrer" target="_blank">open positions</a>!

Encouraging innovative thinking and making Scale AI better in practice with our first virtual Hackathon. 

Encouraging innovative thinking and tackling Scale AI's biggest challenges.

Scale Hackathon

Back on September 16th, we introduced you to our <a href="https://scale.com/blog/introducing-our-new-api-docs" rel="noopener noreferrer" target="_blank">new API Docs</a>. Today, we’re pleased to follow up the original announcement with further details on how we implemented this with Next.JS, Tailwind CSS, and ReadMe. Furthermore, rather than simply describing how we used each platform and framework, we have packaged together an example for you to implement and build off of <a href="https://github.com/scaleapi/custom-documentation-example" rel="noopener noreferrer" target="_blank">in this repository</a>.<h2>Some Context:</h2> At Scale AI, we are building the critical tools that make it easier to build and scale real-world AI systems. To provide our customers with the best experience possible on the Scale AI platform, we believe that a clear, simple, and memorable front-end, user interface, and user experience is critical. Scale’s design team has been hard at work building out a design system and improving customer-facing interfaces to bring front-end consistency across our platform. Most important to this effort was to implement the design system across our different applications. We decided to use Tailwind CSS as a CSS framework and CSS Modules as a way to add custom elements when some customization was needed. To ensure our website and applications were highly performant and loaded quickly, we chose to use Next.js as our react framework.<h2>Why Tailwind?</h2> Scale’s design team have used Next.js and styled-jsx as a way of providing CSS styling since the initial launch of our website. While this was fine when there were fewer pages on the website, it proved to be hard to scale and maintain a design system without proper documentation. At this point the team faced two options: we could either spend a lot of time creating and improving our own CSS and writing documentation for it, or we could use a CSS framework that could adapt to our needs. Since the Scale team prefers not to re-invent the wheel when it isn’t needed, we decided that using a CSS framework would yield the best results in the shortest period of time. Tailwind has allowed us to expedite the development of new layouts and pages while maintaining consistency across our website. Tailwind also has the advantage of having strong documentation behind it to support future development. Using Tailwind has also freed the team up to spend more time focusing on special details for each page like custom animations and interactions.<h2>Why Next.js?</h2> Next.js is one of the best React Frameworks out today. Since the launch of <a href="https://nextjs.org/blog/next-9-3#next-gen-static-site-generation-ssg-support" rel="noopener noreferrer" target="_blank">SGG support</a> in March, it’s a no brainer for a fast and performant website and API docs. Quoting them from their own <a href="https://nextjs.org/blog/next-9-3#next-gen-static-site-generation-ssg-support" rel="noopener noreferrer" target="_blank">blog post</a>:<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/4a743efa-0436-43a8-18b6-9368be8e8a00/public" alt="Next.js Quote"> Next.js’s API routes also proved to be helpful as we used this to retrieve the search values made by the user. More on their API Routes <a href="https://nextjs.org/docs/api-routes/introduction" rel="noopener noreferrer" target="_blank">here</a>. <h2>Why Readme?</h2> As we highlighted in our previous <a href="https://scale.com/blog/introducing-our-new-api-docs" rel="noopener noreferrer" target="_blank">blog post</a>, we chose ReadMe as the platform for our API documentation because ReadMe allowed us to: <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><h2> </h2><h2> Sharing our example code with the developer community</h2><h2> </h2> We developed the new API documentation during Scale’s Hackathon. We are incredibly proud of what we accomplished in just one week and we wanted to share our solution with anyone who is looking to solve a similar problem. Rather than simply providing a step-by-step instruction for this solution, we have packaged together an example for you in our <a href="https://github.com/scaleapi/custom-documentation-example" rel="noopener noreferrer" target="_blank">Github repo</a>. We hope this sample code allows you to build your own beautiful and customized documentation.

How to customize your API Documentation’s front-end leveraging cutting-edge platforms and frameworks.

Customizing API Documentation with Next.JS, Tailwind CSS, and ReadMe.

Quick Start Guide to Customized API Documentation

<h2>Introduction</h2> The ML Team at Scale hosts a weekly reading groups where members choose papers from the broad AI/ML community and discuss them ranging from topics in Computer Vision to NLP to Active Learning. Here, we describe a brief summary of some of the insights we gained from various papers and how we aim to use some of that knowledge in future research projects and applications to Scale AI’s business.<h3><a href="https://arxiv.org/abs/2008.09049" rel="noopener noreferrer" target="_blank">Discovering Useful Sentence Representations from Large Pretrained Language</a></h3><h3><a href="https://arxiv.org/abs/2008.09049" rel="noopener noreferrer" target="_blank"> Models</a></h3>Presenter:<a href="https://www.linkedin.com/in/nishantsubramani/" rel="noopener noreferrer" target="_blank">Nishant Subramani</a> This is Scale’s first research paper and we wrote a <a href="https://scale.com/blog/discovering-useful-sentence-representations" rel="noopener noreferrer" target="_blank">blog post summarizing the paper</a>. This paper focuses on whether we can adapt pre-trained language models off-the-shelf as universal decoders. To be considered “universal,” a decoder must have an implicit representation for any target sentence such that it can recover that sentence exactly when conditioned on its representation. We investigated whether such representations exist and whether they can be easily discovered. Experiments show that not only do these representations exist for sentences from a variety of different genres, but also that our methods can recover these sentences almost perfectly without fine-tuning the underlying language model at all.<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/733c8e61-0b1b-4a08-9092-43c494a9fc00/public" alt="Scale blog ml papers B_01"> <h3><a href="https://arxiv.org/abs/1910.04126" rel="noopener noreferrer" target="_blank">Scalable Nearest Neighbor Search for Optimal Transport</a></h3>Presenter:<a href="https://www.linkedin.com/in/anastasiia-alokhina-534b1014a/" rel="noopener noreferrer" target="_blank">Anastasiia Alokhina</a> The authors of this paper look at the problem of nearest neighbor search with respect to Wasserstein distance. As Wasserstein distance has become popular in high-resource data environments and domains (images, text, etc.), search becomes prohibitively slow. As a result, approximation methods are important. The authors introduce a Quadtree variant called a Flowtree that formally shows better asymptotic accuracy and empirically show that it beats existing methods on a variety of different real-world datasets on either running time or accuracy. Flowtree is a fast nearest neighbor search algorithm for Optimal Transport (Wasserstein-1 distance), which has a linear running time and speeds up SOTA performance by up to 7x. They do this by evaluating the optimal tree flow in the original metric space. <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/2f1c10db-58ad-4966-b9aa-a69c64080500/public" alt="scale blog ml papers B_02"> At Scale AI, we work with lots of high dimensional data such as documents and images for a variety of tasks. For these, we often need to find good representation spaces for these data points such that we can do fast and highly accurate similarity search in order to find outliers in datasets in order to find errors in labeling and find examples to label to improve our models using active learning. We think the flowtree method here could be invaluable in giving us the ability to use a different distance for this end and is something we will explore going forward. <h3><a href="https://demuc.de/papers/schoenberger2016sfm.pdf" rel="noopener noreferrer" target="_blank">Structure-from-Motion Revisited</a></h3>Presenter:<a href="https://www.linkedin.com/in/ppyht2/" rel="noopener noreferrer" target="_blank">Hongbo Tian</a> Structure-from-Motion(SfM) is a field of computer vision techniques which aim to reconstruct full 3-dimensional scenes from only a series of images. Accurate SfM algorithm can reproduce colored point clouds similar to LiDAR. The authors of this paper along with <a href="https://colmap.github.io/bibliography.html" rel="noopener noreferrer" target="_blank">Schönberger’s thesis</a> introduced a robust computational pipeline for accurate dense 3D reconstruction. The authors also introduced <a href="https://colmap.github.io/index.html" rel="noopener noreferrer" target="_blank">COLMAP</a> - an open-sourced CUDA accelerated software to reproduce the results. <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/62e7cdd5-e0b4-4fc0-7dee-619984c25800/public" alt="Scale blog ml papers B_03"> At Scale AI, our labelling pipeline is not subjected to the same online computational constraints compared to our customers. This allows us to pursue batch methods to enrich our 2D dataset, enabling our human labelers to capture 3D contexts from only images. <h3><a href="https://storage.googleapis.com/pub-tools-public-publication-data/pdf/59f3bb33216eae711b36f3d8b3ee3cc67058803f.pdf" rel="noopener noreferrer" target="_blank">Representation Learning for Information Extraction from Form-like</a></h3><h3><a href="https://storage.googleapis.com/pub-tools-public-publication-data/pdf/59f3bb33216eae711b36f3d8b3ee3cc67058803f.pdf" rel="noopener noreferrer" target="_blank"> Documents</a></h3><h3><a href="https://storage.googleapis.com/pub-tools-public-publication-data/pdf/59f3bb33216eae711b36f3d8b3ee3cc67058803f.pdf" rel="noopener noreferrer" target="_blank"> </a></h3>Presenter: <a href="https://www.linkedin.com/in/nishantsubramani/" rel="noopener noreferrer" target="_blank">Nishant Subramani</a> The authors aim to extract structured information from form-like documents. They observe that forms have fields that often correspond to well-understood types and are often associated with a specific key phrase that bears a visual relationship with it. They also observe that key phrases in documents are drawn from a small vocabulary. Due to these observations, the authors build a system with two parts: candidate generation and scoring &amp; assignment. In the candidate generation phase each field type is associated with a candidate generator based on a cloud-based entity extraction service. Spans are then detected. In an invoice there are multiple dates. Every date in an invoice becomes a candidate for every date field in the target schema. This process is repeated for every target field. In the scoring and assignment phase, the goal is to find the correct extraction candidate for each field. To do this, a score is computed for each candidate independently using a neural model. Next, the most likely scored candidate is assigned for each field. This process yields an independent learned representation based only on the candidates’ neighborhood (this is entirely independent of other candidates of fields). The neural scoring model is below. <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/18c49c25-c170-4dcd-e6da-51144e415600/public" alt="Scale blog ml papers B_04"> Our major takeaways are that structured extraction is not super well-studied by the academic community, but is relatively simple. The extraction system presented has promising accuracy and generalizes well to different domains. The representations have some interpretability as well. Ultimately some of the learnings from this paper could influence some of our work on <a href="https://scale.com/document" rel="noopener noreferrer" target="_blank">Scale Document</a>. <h3><a href="https://arxiv.org/abs/1912.11370" rel="noopener noreferrer" target="_blank">Big Transfer (BiT): General Visual Representation Learning</a></h3>Presenter: <a href="https://www.linkedin.com/in/felix-lau-0386641a/" rel="noopener noreferrer" target="_blank">Felix Lau</a> This paper focuses on how to transfer pre-trained representations to other tasks in the visual space. They propose a recipe called Big Transfer (BiT) and achieve very strong performance on ILSVRC-2012 (87.5% top-1 accuracy), CIFAR-10 (99.4%), and the Visual Task Adaptation Benchmark (VTAB; 76.3%). <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/e2a0b8c1-282d-4511-835f-a183b368ad00/public" alt="Scale blog ml papers B_05"> BiT has two phases: upstream and downstream. In upstream pre-training, the authors first investigate the scale of computational budget and how that affects performance. Secondly, they look at ground normalization and weight standardization, which helps significantly with small-batch and large-batch training alike. In downstream transfer, the authors propose a cheap fine-tuning methodology by using the BiT-HyperRule to select the most important hyperparameters for tuning as a function of the task’s intrinsic image resolution and number of datapoints. The hyperparameters they thought were important were training schedule length, resolution, and whether to use MixUp regularization. There are a few takeaways from this method we found interesting. There’s a lot of value in balancing the computational budget and simplifying the experimental pipeline when needed. The BiT-HyperRule focusing on only a few hyperparameters was illuminating. We were interested in the dynamics of how large batches, group normalization, and weight standardization interplayed and were surprised at how poorly batch normalization performed relative to group normalization and weight standardization for large batches. The empirical strength of group normalization and weight standardization surprised us as well and we will start exploring these techniques in some of our computer vision models with lots of data available. However, the most impressive part was the empirical few-shot learning performance relative to the baseline. We are trying to build models that quickly adapt to new customers and domains and often struggle with fine-tuning, so these ideas are very top of mind. <h3><a href="https://arxiv.org/abs/1905.03677" rel="noopener noreferrer" target="_blank">Learning Loss for Active Learning</a></h3>Presenter: <a href="https://www.linkedin.com/in/rishgoyell/" rel="noopener noreferrer" target="_blank">Rishab Goyal</a> The authors present an active learning method that is a task-agnostic by attaching a loss prediction module to a target network and train it to be able to predict target losses of unlabeled inputs. The module can then provide insight into data that the model could predict incorrectly and thus improve the overall model by choosing just those examples to go and label. <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/105e736f-2bce-4ad5-6e99-7279ab964900/public" alt="Scale blog ml papers B_06"> Their general process is that for every labeled example you predict the target and predicted loss for your prediction. Next, you take the unlabeled pool and pass them through the model to get predicted losses. Lastly you annotate the top-k data points and add them to the labeled training set. The loss prediction module uses a ranking loss. As a data labeling company, we are very interested in active learning and how we can leverage our own strengths to better build ML models. We really liked the approach because it is widely applicable, but were concerned that there could be many degenerate cases. We weren’t convinced that the loss prediction module would be strong enough to offer significant improvements over uncertainty or coverage based methods such as entropy, query-by-committee, and coreset based methods. <h3><a href="https://arxiv.org/pdf/1912.13318v5.pdf" rel="noopener noreferrer" target="_blank">LayoutLM: Pre-training of Text and Layout for Document Image</a></h3><h3><a href="https://arxiv.org/pdf/1912.13318v5.pdf" rel="noopener noreferrer" target="_blank"> Understanding</a></h3>Presenter: <a href="https://www.linkedin.com/in/malcolm-greaves/" rel="noopener noreferrer" target="_blank">Malcolm Greaves</a> The authors try and solve the problem of automated understanding from scanned business documents. They use both computer vision and natural language processing techniques via textual and visual layout information for pre-training their system. Their system achieves SOTA on a set of layout analysis, receipt understanding, and document image classification tasks. They claim that their empirical performance is from their novel loss functions: a masked visual-language model (MVLM) loss and a multi-label document classification (MDC) loss. <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/756680db-57a2-4448-2f6f-3f0976fa5500/public" alt="Scale blog ml papers B_07"> MVLM extends the masked language model loss from BERT by randomly choosing some words to mask. They then keep the masked token’s 2D positional embedding and forces the model to predict the word’s text. MVLM forces the model to understand language context and 2D relationships between words in the document. MDC assumes that documents have possibly several labels and is a standard cross-entropy loss. This is optional and seems to help on the document image classification tasks. We found this paper interesting, but computationally demanding: alongside their loss functions, a key contribution of this paper is their pre-training routine. Without significant pre-training, these models do not seem to work well. This work requires off-the-shelf OCR and treats that as ground truth, which simplifies the work, but makes it less practical for document analysis practitioners. For the business document tasks that our ML team takes on, we often like to have greater control on the OCR task. It's sometimes advantageous to be able to adjust OCR to provide better downstream task-dependent results. We do a lot of document analysis at Scale, but can’t obfuscate or explain away the OCR component, which do produce some errors (e.g. with handwriting). Although this approach needs word-level annotations, Scale is in a unique position to get large amounts of these labor-intensive labels using our labeling platform. <h3><a href="https://arxiv.org/abs/2006.16205v1" rel="noopener noreferrer" target="_blank">Simplifying Models with Unlabeled Output Data</a></h3>Presenter: <a href="https://www.linkedin.com/in/alexandre-matton-73042b170/" rel="noopener noreferrer" target="_blank">Alexandre Matton</a> This paper focuses on tasks whose outputs have to obey some constraints. This happens in pseudo-code to code transcription for example. What we ultimately want is a code that compiles and runs without error. The usual way to address these tasks is to build a single end-to-end model and hope that it will learn the input-output mapping and understand the output constraints. The authors provide a new way to tackle these tasks by replacing the original model by two sub-models: a base predictor and a denoiser. The goal of the base predictor is to learn the mapping between the input and the output. The goal of the denoiser is to learn the constraints of the output space, in order to fix the base predictor’s output. <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/9578c3e1-2f67-408f-2bd1-a2972c25ee00/public" alt="Scale blog ml papers B_08"> There is a reason why this decomposition works well. As the denoiser simplifies the job of the base predictor, it is now possible to increase the regularization of the base predictor without affecting the overall model performance. This in turn leads to solutions that generalize better on unseen data. The big win of this architecture is that the denoiser can be trained on unlabelled output data, which is often abundant (e.g. there is a huge amount of compiling code pieces freely available on github). A big number of tasks actually impose constraints on the output (such as molecule generation, or translation from one language to another). At Scale, our ML team also tackles such problems. This paper provides a very general way to increase our downstream scores on them. <h3>Join our Team</h3> If you’re interested in joining our growing engineering team, take a look at our <a href="https://scale.com/careers" rel="noopener noreferrer" target="_blank">careers page</a> for open positions.

The machine learning research papers the Scale AI team read and discussed in Q3 2020.

Research Papers the Scale AI Team has Read in Q3 2020

Scale AI Machine Learning Digest - Q3 2020

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

Introducing Scale AI's updated API Documentation

Introducing: Our New API Docs

URL redirections for Website. Disclaimer: takes 5~ min for each added or edited.

url-redirects

Use relative path. use `/blog/foo` not `https://scale.com/blog/foo`

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event

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guides

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blog-model

Option to list or not list this post in the Blog index. Enabled = not list in the index, Disabled = list in the index

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leaderboards

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Blog

Scale AI Named to CB Insights’ AI 100 2021 List

Scale Transform: AI Leaders Showcase Research Breakthroughs and Real-World Impact

Getting Started with Nucleus

Making Pytorch Transformer Twice as Fast on Sequence Generation.

Scale AI’s Series D: Expanding our Team to Empower AI Dreamers

Scaling Menu Transcription Tasks with Scale Document

Scale Hackathon

Quick Start Guide to Customized API Documentation

Scale AI Machine Learning Digest - Q3 2020

Introducing: Our New API Docs

The future of your industry starts here

Blog

Scale AI Named to CB Insights’ AI 100 2021 List

Scale Transform: AI Leaders Showcase Research Breakthroughs and Real-World Impact

Getting Started with Nucleus

Making Pytorch Transformer Twice as Fast on Sequence Generation.

Scale AI’s Series D: Expanding our Team to Empower AI Dreamers

Scaling Menu Transcription Tasks with Scale Document

Scale Hackathon

Quick Start Guide to Customized API Documentation

Scale AI Machine Learning Digest - Q3 2020

Introducing: Our New API Docs

The future of your industry starts here