It will need software that recognises objects and visual cues that the vehicle driver normally sees. However, before the machine learns that it has to avoid some visual elements- like people and animals- and respond to elements like traffic signals, the software has to know what these elements are. Throughout the year, we have had many pilot successes, and we managed to bag long-term partnerships with global ML teams from companies like Samsung, Intel, Siemens, Sony, Dell, BMW, Bosch, LG, ZF, and Solera. Earlier in the year, we had an incredible opportunity to partner with Microsoft and showcase our data labeling capabilities along with the Azure ML team in CES 2020, Las Vegas. To make this feasible, Playment technology divided the big chunk of work into small micro-tasks like lego blocks and qualified users on its platform solve the entire puzzle.
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It was in the process of looking out for partners and investors in the business that Playment founders engaged with TELUS leading to the eventual acquisition. All 85 of the startup’s employees, including founders, will join TELUS post acquisition. This dashboard allows us to set up and monitor customized workflows and build an end to end project management with playment workforce. ML engineers can use Python code to integrate their pipelines.
We divert their time to improve the quality of our deliverables with various QC tools and processes. We took forward two designs and discussed the pros and cons of each. CabinetM helps modern marketing and sales teams manage the technology they have and find the tools they need.
Thriving in the new normal came with its own challenges, yet, we were well-prepared to go remote because of our web-based labeling platform and pre-established collaboration and communication protocols. We executed more than 776,475 hours of labeling and shipped ~64M high-quality annotations remotely for our customers worldwide. As a 2D/3D annotation tool and a platform to manage labelling teams of any size with team management software, all in one place, we wanted to communicate our distinguishing features through it. Globally, most companies outsource data labelling jobs to dedicated data annotators. Data labelling takes up the bulk of data scientists’ time, which could otherwise have been devoted to building algorithms. As in case of technology outsourcing opportunities, India has emerged as a leading data labelling destination globally.
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As labeling needs became more sporadic, timelines also started becoming shorter. And gt studio playment we are all aware that human labeling is a time-consuming process. That’s why we introduced ML automation features that reduce the time taken for labeling.
Looking back at Playment’s journey in 2020 and thoughts going into 2021…
Annotators go through the platform and complete pending tasks and get points. The points can be exchanged in the form of vouchers on online e-commerce sites. A batch is a way to organize multiple jobs under one batch_id. You can create new batches from the dashboard or by using the batch creation API.If batch_id is left empty or the key is not present, the job is created in the Default batch in your project. Read all about our automation features, workflow builder, and sensor fusion tools.
- The global launch of GT Studio will further democratize the access to high-performing labeling tools for ML teams worldwide.
- Receiving this recognition is a testament to our belief in the promise of AI in the upcoming years.
- We took charge of the situation, consistently exceeded customer expectations, and used opportunities to stay true to our mission of expediting the AI age.
- Project progress and quality analytics with active feedback are also provided keyboard shortcuts, visualization tools and confusion metrics.
The data annotation tool market is projected to record a CAGR of 27% between 2021 and 2026. The market growth is largely attributed to the increased adoption of software for image data annotation in the automotive, retail, and healthcare sectors. While artificial intelligence drives a lot of solutions globally, little is understood about how it works. Playment particularly deals with AI and machine learning software of the visual kind. Suppose an automobile manufacturer wants to build self-driven cars.
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Data Annotation is not only limited to image and video but now with recent advances in computer vision, it is extended to sensors even. After creating the job, please store the job_id received in the response. None of this would have been possible without the people working behind the scenes. From taking pay cuts during the pandemic to giving more than 100% effort for all our projects, we could not have asked for a better team. From fun sing-off sessions to virtual heists and group competitions, our time working from home was not-so-dull. All credits go to our excellent HR Manager, Sahiba Chandok, who has created an inclusive and fun work culture at Playment.
Project progress and quality analytics with active feedback are also provided keyboard shortcuts, visualization tools and confusion metrics. Since the future of work is remote, there is an increasing demand for web-based annotation tools compared to offline tools. Managing 1000s of annotators every day requires an analytics-led approach in the remote set up. The focused ML efforts of 2020 will fuel the emergence of self-driving cars as early as 2021.
Image Annotation & Image Segmentation
After signing up, the users are taken to the management dashboard which is light in color. To maintain visual uniformity in the journey, we chose to go ahead with the light colored illustration. Unlock special offers and join 10,000+ founders, investors & operators staying ahead in India’s startup economy.
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- Earlier in the year, we had an incredible opportunity to partner with Microsoft and showcase our data labeling capabilities along with the Azure ML team in CES 2020, Las Vegas.
- It will need software that recognises objects and visual cues that the vehicle driver normally sees.
- We’ve experimented with a few of these tools like LabelImg & VGG’s VIA and have our fair share of joy and frustrations, so was curious to understand what your experiences were.
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- That’s why we introduced ML automation features that reduce the time taken for labeling.
This year, we spent more than a few late nights developing a product that makes data labeling more accessible to ML teams. As more and more companies were going remote, we saw the need for a high-performing web-based labeling platform. A low-code platform that anyone anywhere can use to label datasets and manage 100s of labeling pipelines seamlessly. We also gave early access to a few of our customers and teams interested in exploring a data labeling platform that’s perfect for remote setups. Our beta customers have more than 350+ full time labelers working on GT Studio. Playment was launched in November 2015 by former Flipkart employees- IIT-Kharagpur alumni Siddharth Mall and Akshay Lal, along with IIT-Guwahati alumnus Ajinkya Malasane.
In 2015, the team started with providing cataloging and content moderation services to large marketplaces such as Flipkart, Lazada, Paytm, Ola and others. “At Flipkart, we used to address a lot of repetitive cataloguing requirements. Especially because a lot of sellers did not know how to categorise their products properly. This led us to build the Playment solution,” said Malasane speaking to Inc42. The platform is powered by a workforce of 300,000+ users which is managed by the human intelligence experts who build the tasks and deliver results with assured quality. Playment has been proactively working on creating infrastructures that offer maximum flexibility and scalability.
In the initial years, Playment used to work with crowd-sourced data annotators who would do the labelling work. This demand for data annotation solutions also helped the company become profitable in 2020. The global data annotation tools market size was estimated at $321.45 Mn in 2020, according to a report by Expert Market Research.