Company Updates & Technology Articles
We are proud to introduce PandaSet: a new open-source dataset for training machine learning (ML) models for autonomous driving released in partnership with the LiDAR manufacturer Hesai.
Our latest product offering is capable of supporting a range of inputs from PDFs, Microsoft Word Documents, JPEGs and more. Scale Document builds on the previously released Scale Text product to better support customers with document processing and understanding use cases.
Machine learning is a field of study with tremendous strides being made through active academic research. The machine learning team at Scale AI regularly hosts reading groups to discuss papers they find interesting. In this blog post, we go over the papers the team has read throughout the quarter and provide insights on how a paper influenced our own work here at Scale AI when relevant.
In partnership with researchers from the University of Toronto and University of Waterloo, Scale AI is pleased to announce the release of the Canadian Adverse Driving Conditions (CADC) dataset. The CADC dataset is the first of its kind to advance perception under winter driving conditions.
Scale AI is pleased to be selected as one of America’s Most Promising Artificial Intelligence Companies by Forbes.
Elon Musk recently took a high-profile swipe at LiDAR technology, predicting that “anyone relying on LiDAR is doomed” and that the only hardware Tesla needs is the existing suite of cameras and sensors installed on their vehicles. As leaders in data annotation or data labeling for autonomous driving companies, we compared training data from both camera and LiDAR to figure out how well each system will work.
Scale has officially expanded into Europe with a new office in Munich! I’d like to take this opportunity to have the Scale EMEA team introduce themselves, talk a little about why each of us joined, and provide a way to get in touch.
At Scale, we contract a lot of people from all over the world as labelers. We’ve believed for a while that creating jobs like this can be very impactful — so we asked some of our labelers for times when working for Scale made a positive difference in their lives.
We’ve had an incredible group of people join Scale—and we’re still hiring! We wanted to take a moment to call out some of the recent team members who have joined Scale.
When I first joined Scale, we just offered 2D annotation products. A few of our self-driving customers mentioned off-hand that they would also potentially use a 3D annotation product. After roughly sizing the market and contemplating the trade-offs, we decided to build a minimum viable product to see if this additional business was a good direction for the company.
At Scale, we pride ourselves in developing superior tooling and implementing forward-looking solutions for problems our customers on the cutting edge of computer vision, including self-driving cars, AR/VR, and drones encounter. For example, our 2D image annotation endpoints now support both per-annotation attributes and label hierarchies, allowing you to get richer labeled data out of Scale.
At Scale, we are excited to help you solve your most challenging computer vision problems. One important and well-studied problem within computer vision is that of semantic segmentation, which aims to understand images at the pixel level.
We have exciting news to share today! Scale raised $18M in Series B funding led by Index Ventures with Accel, Y Combinator, Drew Houston, and Justin Kan joining. Mike Volpi of Index Ventures will be joining our board. Scale has come a long way from the dorms at MIT, and we still have a long way to go.
TLDR: Row stores are fast to write but slow to read. Column stores are fast to read but slow to write. Load data from Mongo into Parquet files for fast querying using AWS Athena.
You can now be a part of a team on Scale! Until recently, only the original creator of a task could access its history, visually inspect responses, and provide direct feedback on quality. It was a hurdle to our users getting the fullest possible value out of our platform.
One of the most obvious signs that a site is made with Next.JS is that you load the page, you click a link to another section and it loads instantly. That’s because when a page is loaded, Next.JS will download in the background all the other pages linked with tags.
We are excited to announce that we have raised a $4.5 million Series A round of funding led by Accel. Along with this funding, Accel’s Daniel Levine has joined Scale’s board. The funding will be used to invest in our rapid growth, expand our offerings, and grow our team.
In this blog, we discuss how Scale AI uses machine learning (ML) to supercharge its data annotation pipeline, enumerate some of the technical challenges, and describe our approach to create scalable ML solutions.
We have expanded our data annotation products over the years to support an increasing range of data inputs and annotation types. As our data annotation products grew, it became clear that we needed a better way to group and name our products. We invite you to explore our updated product line up.
As the COVID-19 pandemic continues to spread, it affects all of our communities in different ways. The health and safety of Scale’s employees, as well as our global community of labelers, is a critical matter and a responsibility we do not take lightly. We also want Scale to make a contribution to combating this disease if we can.
How do you scalably maintain the quality of labels, without having labelers check each other’s work? Take a deep dive into how we solved this problem while working with OpenAI on fine tuning their GPT-2 model.
Scale AI is pleased to be selected as one of LinkedIn’s 2019 Top Startups in the U.S. Read on to find out the top three reasons you should join Scale AI.
Our mission at Scale is to accelerate the development of AI applications. We’re proud of what we’ve built over the last three years, and today we’re announcing our Series C funding round to support our continued work against that mission.
Scale AI is pleased to announce the launch of Sensor Fusion Segmentation - Scale’s endpoint for point cloud segmentation.
Deep Learning models have the capacity to get better with more data seemingly without limit. To get a well-functioning model, however, it is not enough to just have large amounts of data, you also need high quality data annotation.
Scale AI is pleased to announce the full release of nuScenes by Aptiv —a large-scale open source dataset for autonomous driving. Scale AI was involved as Aptiv’s data labeling partner on this initiative.
Scale is committed to accelerating the development of AI applications and serving customers with high-speed, accurate, and affordable data annotation. As part of this mission, Scale and Ouster are pleased to announce a data integration partnership that makes it significantly easier and faster for Ouster customers to send their LiDAR data to the Scale platform for annotation.
In the grand scheme of things, most decisions that we make in life are fairly inconsequential. What to eat for lunch, where to get a haircut, which route to take to work… while there’s a small chance each choice has a dramatic impact - maybe you meet your soulmate on the bus - it most likely won’t.
Scale relies on Mode Analytics for internal SQL and Python based reporting and analytics, helping power our operations, insights and business decisions. Many of these reports we set to run on a schedule, so that we can always go to a report and see data that’s no more than fifteen minutes to an hour out of date
Here at Scale, one of the image annotation services we offer is Cuboid Annotation, which annotates your two-dimensional images with projections of cuboids enclosing objects such as cars, trucks, pedestrians, traffic cones, you name it.
We’ve had an incredible group of people join Scale—and we’re still hiring! We wanted to take a moment to call out the amazing engineering team at Scale:
Today we are happy to announce a new version of our customer dashboard! We’ve cleaned up the dashboard so it’s faster and more efficient for you to use.
We recently worked with the team at Cloudinary to help build and evaluate a better image compression and quality measurements. The results of Cloudinary’s work on Scale were very insightful and we wanted to share it broadly to demonstrate how more companies can leverage human judgments to build high quality features.
One of the largest automobile companies in the world uses Scale to help build self-driving cars. Self-driving cars (SDCs) are one of the most exciting technologies being developed today. If they become a reality, the benefits that come will significantly impact our lives in several ways.