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I cannot express how excited I am to join the team at Scale. To understand why, one needs to look no farther than my background. Over the past 33 years, I’ve dedicated my career to serving our country, keeping our citizens safe, and improving the technology our warfighters and analysts use to protect us. I’ve seen first-hand how artificial intelligence (AI) already has and will continue to change our world. I swore an oath over three decades ago and I remain committed to it today. While I’m not able to fly my old fighter jet, I can and will contribute by helping our country adopt and operationalize AI. Enter Scale. I was introduced to Scale during my time as General Manager of National Security at Microsoft, when we supported a mutual U.S. Department of Defense customer. I was immediately impressed by the technology and – more importantly – the people. From the CEO to the front line, the entire team was committed to customer success and delivering the right knowledge and the right technology at the right time to achieve operational impact. As I dug deeper, I learned these activities weren’t just demonstrations to excite investors, they were part of the team’s DNA. It was their purpose. And they were really (really) good at it. The U.S. government is at the precipice of AI adoption. There are numerous private companies conducting countless AI experiments. Most, however, treat AI as an out-of-reach, custom commodity. What if it isn’t? What if we could operationalize and democratize AI so warfighters, analysts, and mission owners could have ready-access to AI capabilities? What if we gave them the tools to translate the complex pipeline of AI activities into something no more complicated than building a spreadsheet? This is what Scale does. Every day. Scale is full of phenomenally talented people, creating outstanding technology, who are committed to the success of our country. I can’t wait to see how, together, we accelerate AI readiness across the U.S. government. Join us at <a href="https://scale.com/government" rel="noopener noreferrer" target="_blank">scale.com/government</a>.

Mark Valentine - formerly Microsoft’s GM of National Security - joins Scale AI as our Head of Federal

Scale AI welcomes Mark Valentine as Head of Federal 

Why I Joined Scale AI

When we started Scale nearly five years ago, our mission was to accelerate the development of AI. Today, I’m proud to say that we’re seeing this mission come to life, as customers across industries, from finance to e-commerce to transportation to government, are shipping AI projects that touch our everyday lives.But we’re still just scratching the surface of the potential that AI has to transform every business and industry, which is why we’re happy to share that Scale has raised $325 million in Series E funding, co-led by Dragoneer, Greenoaks Capital, and Tiger Global. Additional new investors in the round include Wellington Management and Durable Capital followed by existing investors Coatue, Index, Founders Fund, and YC. The interest and opportunity for AI is only accelerating, and this new funding will help us continue to expand our team and products to meet this growing demand. Additionally, Jeff Wilke, former CEO of Amazon’s Worldwide Consumer business, will be joining as Advisor to the CEO."The AI industry is at an inflection point where every business is looking to implement an AI strategy and we are starting to see a real-world impact. We believe Alex and his team at Scale have been critical to enabling businesses across industries reap the full benefits of AI and are taking the industry from proof of concept to reality," said Neil Mehta, Founder and Managing Partner at Greenoaks Capital.As we discussed with over 9,000 of the leading minds in AI at our inaugural <a href="https://scale.com/events/transform" rel="noopener noreferrer" target="_blank">Scale Transform conference</a>, we’re moving from a theoretical and research based industry to one that’s rooted in impact and real business results. However, making this shift means companies are facing new challenges, which requires a systematic, data-centric approach to rapidly deploy their most important models.At Scale, we’re building the foundation to enable organizations to manage the entire AI lifecycle. Whether they have an AI team in-house or need a fully managed models-as-a-service approach, we partner with our customers to build their strategy from the ground up and ensure they have the infrastructure in place to systematically deliver highly-performant models.We set out to solve this problem by starting with an essential building block, data annotation. Building on our expertise in delivering high-quality training data to world-leading machine learning teams, we are now applying our technical expertise and understanding to close the loop on the ML development lifecycle.Learn more about how we <a href="https://scale.com/" rel="noopener noreferrer" target="_blank">accelerate ML development with an end-to-end solution</a>.<h2>Driving Real-World Impact</h2>We’ve already seen the impact that the right data infrastructure can bring to our customers. For example, companies like <a href="https://scale.com/customers/toyota" rel="noopener noreferrer" target="_blank">Toyota</a> are paving the way for a future with fully autonomous vehicles, but need massive volumes of high-quality training data to reach this goal. By using our combination approach of machine learning with human insight, Scale was able to improve Toyota’s annotation throughput by 10X in a matter of weeks. For another customer, <a href="https://scale.com/customers/states-title" rel="noopener noreferrer" target="_blank">States Title</a>, Scale improved the accuracy of its document processing to more than 95 percent, freeing up their data science teams to work on actual data science work. And PayPal was able to leverage AI to efficiently validate and categorize millions of products from its partner merchants with our help.Brex, a high-growth startup reimagining financial systems, partnered with Scale to improve how businesses manage their money. Using our document processing solutions, their customers can now process invoices and pay bills faster and more accurately. We’ve also partnered with Flexport, the platform for global trade, to make historically manual logistics paperwork easier to execute and more accurate. Through our document processing solutions, we’ve enabled increased efficiency and compliance across Flexport's network of global trade stakeholders.These are just a few examples of how we’ve been supporting our customers. Over the past six months, we’ve continued to see demand growing across industries as leading organizations expand their AI programs. Today our customers range from the Department of Defense and U.S. Air Force; to leading fintechs like Brex, PayPal, and Square; to transport and logistics pioneers like Flexport, General Motors, Luminar, and Oshkosh; to beloved consumer brands and products like Etsy and Pinterest; to some of the world’s largest technology leaders like Samsung and SAP. We’re proud to be trusted by the world’s most ambitious AI teams to build the data foundation that will set their organizations up for success for decades to come. We can’t wait to see what else will be achieved with this technology as our customers continue to prove that production-ready AI isn’t just a pipe dream, but it’s already here.

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Scale AI has closed its Series E funding to accelerate the deployment of AI across every industry.

 Scale AI’s Series E: Deploying AI Across Every Industry

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

The most impactful technology trends often start with infrastructure buildout. Starting my career in 1998 at Cisco at the height of the dot-com bubble, Cisco was laying down the internet infrastructure. While the dot-com bubble burst, it did lay the foundation for the next generation of infrastructure: cloud computing. This in turn paved the way to the SaaS revolution. When I joined Twilio in 2011, it was in the early stages of the now macro trend of highly programmable infrastructure through APIs. Twilio gave developers the flexibility to build applications efficiently leveraging these new interfaces. Great companies like Stripe, Twilio, AWS all provide a developer-first approach with a consumption-based model focusing on developer productivity. The days of the “snazzy SaaS” sales rep, snapping their fingers attempting to discount a per seat license are over. As applications, infrastructure and our world become increasingly more complex, consumption based go-to-market models, leading with a technical enterprise sale will be key. Now in 2021, as I contemplated joining Scale AI, it was clear that again we are in the beginning stages of a new macro trend of AI infrastructure. Similarly to before, there will be an infrastructure build out, followed by end-user applications. Scale is on a mission of making AI developers more productive. Starting with data annotation and highly accurate training data, their first product, Scale Image is market leading. Subsequently, Scale continued to innovate and released several products including Scale Text, Video, and Document all focused on solving problems for AI/machine learning engineers. With a consumption based model and a developer-led focus,I believe there are some great parallels between the AI macro-trends and the trends of the past. Not to mention, after meeting and spending time with the team, it was clear to me that the company culture and leadership are aligned to me personally. The focus on diversity &amp; inclusion, intelligence, transparency, while making decisions based on data gave me great comfort to know that I wasn’t just joining a hyper growth company, but also a once in a lifetime generational company that I could be proud to say I contributed to. As an early employee of Twilio, I had a similar opportunity to shape their history, and now as the Chief Revenue Officer for Scale, I get the opportunity once again to shape, mold and build Scale. It wasn’t a hard decision to join Scale, the opportunity to build the next generation of infrastructure focused on machine-learning and AI, was a no brainer. I couldn’t be more excited about Scale, the opportunity, and bringing my collective experiences to lead their go-to-market organization. If you’d like to join my team, you can find all our open roles on our <a href="https://scale.com/careers" rel="noopener noreferrer" target="_blank">careers page</a>.

Chetan Chaudhary—formerly Twilio's VP of Vertical Sales—joins Scale AI as our CRO

Scale AI welcomes Chetan Chaudhary as CRO

Why I Joined Scale

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

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