In an exciting announcement to close out the year, Scale has been featured in two of Gartner, Inc.’s 2021 Hype Cycles. We are honored to have been featured in Hype Cycles for both Artificial Intelligence and Data Science and Machine Learning. These reports detail emerging technologies in AI, data science and machine learning, and name Scale as a representative vendor for Data Labeling and Annotation Services. The reports describe an urgency for data labeling and annotation services (DLA) to become widely adopted. According to Gartner analyst Anthony Mullen’s analysis, the demand for labeled and annotated data has risen in order to remove bottlenecks in industry-specific use cases. And, due to the fact that most companies do not have in-house skills and processes for DLA, companies like Scale are the best option to provide DLA to yield optimal AI results. Why good data matters Better data leads to more performant models, and performant models lead to faster deployment. With better data, Scale helps customers deliver more value. An increased diversity of use cases in DLA will drive both AI and industry-specific innovation, according to Gartner analyst Anthony Mullen. Vendors in the market have specific offerings across industries including commerce, robotics, autonomous vehicles, retail, GiS/maps, AR/VR, agriculture, finance, transportation and more. Scale is proud to accelerate innovation for customers including PayPal, iRobot, Square, Brex, Pinterest, Etsy, SAP, Airbnb, Instacart, NVIDIA, OpenAI, Samsung, Toyota, GM, the U.S. Air Force, and more. How does Scale solve the bad data problem? Scale is your AI readiness partner. We got our start in data labeling and annotation for autonomous vehicles. Now, Scale provides a data-centric, end-to-end solution to manage the entire ML lifecycle. That means that Scale can meet you wherever you are in your data process. ML-powered, human-in-the loop data Scale’s strength is the combined power of our ML platform and expert human insights to help our customers develop their data strategy with high quality data from the ground up. According to Mullen, there are AI solutions which would be impossible without human-in-the-loop labeling, including in cases of search engine tuning and image recognition. Furthermore, DLA allows data scientists to allocate and prioritize time for higher-order tasks, instead of taking time and effort to annotate data themselves. With Scale Rapid, any machine learning developer can achieve production-quality labels with no data minimums. Receive quality labels in just a matter of hours, with detailed insights and identified edge cases. Scale Rapid can even handle subjective tasks with the utmost precision and quality. But what comes after labeled data? Gartner Inc., has made the case for why data matters to AI development. After data labeling, what’s next? Data management. Whether you have prepared the data, or we label and annotate the data for you, we’re your premiere data management partner. Scale Nucleus is the command and control for your ML dataset. Visualize, curate and debug your dataset to improve your ML model performance with Nucleus. Scale’s advantage: AI development, from data labeling, through the entire ML lifecycle. Scale’s mission is to accelerate the development of AI. We do this by providing a data-centric, end-to-end solution to annotate, manage, automate, evaluate, collect and generate your data. Our products and services meet you wherever you are in the ML lifecycle, whether you need data labeled and annotated, or need an ML engineer to help explore and refine how AI can help your business.

Gartner Hype Cycles for AI and Data Science and Machine Learning