Data labeling is a critical part of AI.
This essential process takes not only more than 25% of time and effort going into an ML project, but also requires an ever-growing amount of specialization and technology expertise.
Good data labeling leads to better results, whether it’s in autonomous cars, medical imaging, or any other industry where AI thrives.
Done poorly, the entire system suffers.
Inefficiencies and inaccuracies become inevitable, while major safety risks caused by poor labeling can derail an entire project.
Today, it’s simply not enough to outsource this essential label to the cheapest vendor.
In 2020, the right approach to fulfill data labeling needs is to look for a technology partner.
The benefits are substantial compared to a more hands-off approach.
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Adaptability & Innovation A data labeling partner should be as innovative as the industry it’s serving.
The technology and pace of change are developing rapidly in industries betting on Machine-Learning based innovations.
So too, then, should data labeling technologies and processes.
Disruptive industries require partnerships that can handle rapid change.
This isn’t just a buzz phrase labeling agencies can use to acquire new clients.
It needs to be proven in the results.
Before beginning a partnership, companies should ask which innovations their potential agency has implemented recently, and how they intend to adapt to new shifts on the horizon.
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Expertise Taking advantage of business process outsourcing (BPO) or relying on a crowdsourced work with a so-called ‘throw it over the wall’ approach is a poor strategy.
There’s a certain appeal to this kind of data labeling practice, as it seems to offer the greatest level of speed and the best “bang for the buck.
” Yet the advantages of BPOs and crowdsourcing are often illusory.
Because data labeling requires iterations on both processes and tools, any result that doesn’t immediately cross the high-quality bar will require revisions.
This leads to more back-and-forth between the AI company and the labeling vendor, as well as the potential for hidden errors that don’t get cleared up.
Companies can avoid frustration and save time and money by focusing on laying down the foundations of great results from the start.
A good annotation technology partner will guide them in the initial phases of taxonomy design and labeling spec definition to ensure a seamless experience down the line.
Even more importantly, labeling requires more and more specific knowledge, tech and expertise.
With the addition of a good annotation partner who has deep technical expertise, any AI business can shape the success of an annotation task in the long run.
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Software, Software, Software.
This aspect is perhaps the most essential: Many industries developing cutting edge AI, while still being in the middle of an intensive R&D process, have already passed the “fast prototyping” phase.
In this landscape, finding robust solutions outside the boundaries of the firm becomes more and more crucial.
An annotation partner should be, at its core, a builder of great software.
Cutting-edge tools specific to solving the most challenging annotation tasks differentiate a good data labeling agency from a great one.
The tools are the core of labeling; it’s vital to find a partner which can look at each annotation task as a new challenge, ready to build custom features to optimize for the specific tasks.
This indicates a deep understanding of not just annotation, but also how the AI team will use annotated data today, and into the future.
Great software also means having a stronger and more efficient human labeling process.
The right tech partner will have great software, with a UX and capabilities that naturally guide the human workforce towards the optimal workflow for that task.
The partner should also understand that delivering the critical dimensions of high-quality annotation — accuracy and speed — is dependent on great software in the hands of well-trained and experienced annotators.
Evaluating this expertise can start with a software demo.
Asking the agency to show exactly how their tech works is a good place to start, and specific follow-up questions ensure the right fit.
With due diligence, companies will be able to make sure data labeling agencies really know their responsibilities and understand the technical challenges.
Effective annotation requires a highly capable, collaborative technology partner — it’s too important to treat it as a commodity service.
If data labeling partners can provide proven results and expertise, as well as the flexibility to adapt to constant changes in technology, the future of ML projects will be well set up on the path to success.
About the Author Mohammad Musa is Founder & CEO of Deepen AI.
Mohamad is a startup veteran with 3 successful exits, as a Lead Product Manager at Google he worked on and launched apps and features used by billions of people worldwide.
Now he’s on a mission to make the world safer and more productive through AI-powered software that is driving the next generation of autonomous systems development at Deepen AI.
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