Should you build or buy a data labeling platform?

Building a future-proof labeling platform that can scale efficiently with your growing business is a drawn-out,
multi-step process. It requires continuous planning, designing, engineering, testing, and long-term maintenance efforts.
Here’s everything you need to make an informed decision for your next labeling strategy.
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The Truth Behind Data
Labeling Infrastructures

Using open-source or locally-engineered tools is a feasible option for companies starting their operations or single annotators working on short-term projects. But building sophisticated labeling architectures for a long-term business requires more than what meets the eye.

Building custom annotation tools, engineering effective workflows, developing QC logic and inspection tools, monitoring performance, seamless data flow, integrations, and compliance with data regulations are all tasks that require additional investment and time.

Realistic Time and Cost Investment In Building A Labeling Platform

Often, when teams consider building tools for internal labeling operations, they don’t factor in long-term maintenance costs and the corresponding scaling expenses required to make the tool viable in the future. It takes heavy cost and time investments to build a platform that meets the evolving needs of a business.

Calculate the cost and time investment that go into building a future-proof data labeling platform using the build vs buy calculator.
Annual cost of paying for an alternate service
Number of employees required
Average employee salary
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Other Risks You Must Consider Before Building A Labeling Platform

GT Studio improves productivity per annotator by 5X

Lack of Cross-Functional Collaboration Interface

A labeling platform is more than just annotation tools. The platform must have collaborative interfaces where ML engineers, annotators, reviewers, and project managers can work as a team to execute projects. Teams are often required to work in silos because internally built tools neglect the importance of cross-functional collaboration interfaces.
High-quality data labels for computer vision use cases

Lost Opportunity Cost

While data labeling plays a central role in building successful AI systems, your developers’ time is best utilized when they are building and innovating ML models rather than engineering labeling platforms that are readily available in the market. A building decision may be detrimental to other ML initiatives of your business as it only postpones any technological breakthroughs and increases the time to market.
GT Studio: Easily scalable data labeling platform.

Accumulation of Technical Debt

Teams that build tools for internal use often adopt make-shift principles owing to limited time and resource constraints which later cause problems when the tool must be scaled to meet growing requirements. This also leads to losses in time and cost investments in the long run.
Reliable data labeling partner for ML teams building perception models

Evolving Scope and Quality Compromise

Labeling data platforms are constantly evolving to bring human-based operations closer to automation. As a result, the goalposts are constantly evolving along with the scope of different ML initiatives. While you may choose to stay out of the race to automating tasks internally, you may risk building a platform that reduces your team’s efficiency as a result of the quality compromise.
Reliable data labeling partner for ML teams building perception models

Lack of Scalability

Most internally designed tools aren’t scalable for long-term usability. Teams deal with limited software capabilities and lack expertise in the field to produce a platform that can change and adapt to large-scale labeling requirements alongside growing businesses.

Risk-free Labeling With Playment

Leverage the power of a sophisticated labeling platform to streamline your data pipeline.

Workflow creation, high-quality annotations, and pipeline management with GT Studio

Here’s why our customers trust us

Playment's quality annotations helped us focus our efforts towards building perception system for our robots. Their flexibility to seamlessly incorporate our feedback and evolving requirements have made them a trusted partner.

Jack Guo

Software Engineer, Nuro
Quality annotations by Playment have helped us achieve higher accuracy of our models in a very short time. Flexible solutions, QA process, and dedicated project manager helped us have peace of mind. The team was able to experience a real off-loading of annotation needs.

Abhishek Gupta

Machine Learning Specialist, HELLA India Automotive
We were very impressed with Playment’s ability to grasp complex requirements and quickly build custom tools to support it. Our dedicated engagement manager was very helpful with sharing his domain expertise to formulate the right solution for our team.

Nikola Noxon

Senior Engineer, Daimler
Playment has proven both reliability and dedication to our project goals. All targets were met on time; data flow and QC processes were managed seamlessly at a high level. We appreciate the team’s ability to scale up processes and match our desired quality.

Liat Rosen

Data Processing Team Lead, INNOVIZ
Playment exceeded our expectations with open communication and on-time delivery for all our projects. We think Playment has the best UI and data labeling platform in the industry. Their commitment to delivering high-quality data goes far and beyond contractual requirements.

Johnny Diaz

AI Data Operations Manager, Samsung Semiconductor, Inc.

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