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Choosing a Platform Checklist


Third-party data labeling platforms combine the best of annotation techniques, technology, and expertise to accelerate machine learning (ML) initiatives. An enterprise-grade platform needs to be able to scale your data labeling and remove all the obvious and not-so-obvious labeling tasks off of your team’s plate without compromising on accuracy. Data science teams who need to improve labeling quality at scale or those who need to offset the high cost of in-house data labeling will benefit from a third party labeling platform. 

While the labeling quality and throughput vary with respect to each platform, most platforms offer a combination of annotation tools, task design frameworks, and a team of humans who will annotate your data. In selecting the right platform for your project, it is important to know what you’re trying to do with the data and the level of complexity involved in labeling your data ahead of time. Some platforms are great for straightforward tasks, while others are made to handle a range of complex use cases and subjective judgments. Below are things to consider as you look for a project-to-labeling platform fit.

Data Labeling Techniques



Is the platform able to build and distribute high-volume labeling tasks to a qualified workforce? Can the platform be configured to support your ML needs at the organizational level? Can it be integrated with your internal ML development technologies and support API data exchange? Look for providers who deliver a high level of visibility, feedback, and flexibility to allow for continuous feedback and optimizations.



What tools are available to optimize data labeling, whether it be text, images, audio, or video? Are these tools customizable for a range of use cases, or do they put constraints on your data labeling capabilities? Does the platform technology support high accuracy labeling for subjective use cases? And is the platform provider investing in R&D to continually improve their tooling capabilities?


Who comprises the workforce that labels the data? Are the annotators screened, vetted, and trained appropriately to make domain-specific judgments? What is the level of engagement, expertise, and accountability of the customer success team who will be managing your project? Are project management and support included? In the case of managed platforms, how well does your customer success manager understand your domain and data objectives? 



Does the provider deliver a targeted quality control strategy that is optimized for your organization’s use case and budget, or is it one size fits all? What QC methodologies can they support? Does the provider have a robust screening and training curriculum to qualify the annotation workforce? Are they able to evaluate the labeling accuracy against gold or ground truth data?


What are the best practices they’re implementing in order to scale and improve labeling quality? What is their track record on delivering large-scale enterprise data labeling? Does the platform support processes and workflows to support complex use cases?



What data and platform security protocols are in place? Are the integration points and APIs safe for data transfer? Are their annotators NDA-ready? Do they have data encryption and portal access controls in place, andAre they able to accommodate your organization’s compliance requirements?
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