Every day, the world becomes increasingly reliant on AI and Machine Learning to deliver solutions that had once been out of reach. However, as the dependence on these technologies increase, so does the need for high-quality labeled data. Today, data labeling or data annotation, which involves preparing tagged datasets for machine learning models, is still seen as one of the boundaries of artificial intelligence that we must push further if we wish to automate more decision processes.
Any machine learning model is only good as the data used to train it, and that is why the need for high-quality labeled data is ever-growing. While it may seem that several data labeling providers are offering the same thing, this could not be further from the case. There are several factors to consider before choosing a data-labeling provider, but here are some tips that could help make the decision easier:
You must note what you aim to achieve through your machine learning models before choosing a data labeling provider. The world of data labeling is vast, and different providers satisfy different requirements, i.e., some providers may only provide tools that label text-based data while others may offer tools that can annotate text-based data, video, and images.
Different data types bring varying challenges to data labeling, and if you do not fully define your objectives and record all the use-cases for your model, you may find yourself in problematic scenarios. For Example, you may end up with a provider that does not have the necessary tools or expertise to label certain types of data, such as video or image-based data. You may also find that your provider is not able to annotate the data at an efficient rate, resulting in slower workflows or lower-quality data being fed to your model.
However, if you truly describe all your objectives, then you can seek out only providers that can help in achieving all your goals, filtering out the rest. This makes your choice easier and increases the chances of providing high-quality labeled data to your machine learning model.
When working with a data labeling provider, you would likely be sharing your data with the labeling provider, and it is not rare for this data to be considered personal or sensitive. For Example, you may be sharing financial or medical data with the labeling provider. Consequently, before you make your choice, you should consider how a particular provider assures the security of your data once it is in their possession.
You could begin by investigating the provider’s current data or platform security and confirm that they have the necessary protocols in place. You may also want to consider providers that are flexible enough to accommodate the needs of your data security team. As a further step, if you are providing sensitive data to a labeling team, then it is also advisable that you ensure that all members of that team are ready to sign Non-Disclosure Agreements before they commence any labeling.
You may likely have found multiple potential providers that may satisfy all your needs. At this stage, it is recommendable that you ask for a proof of concept before fully committing to any provider. Even if you only have one potential provider, this stage provides insight into the relationship that you would have with your provider, receiving an unfiltered experience into how capable the provider is at meeting your needs and delivering your objectives.
By offering the provider a subsect of your data for labeling, you can also identify any edge cases that you may not have been aware of when noting your use cases and make accommodations for these scenarios before you embark on full production data labeling.
Consider how the provider organizes the different workflows required to deliver a high-quality data labeling experience. You should not only include the quality of the data labeling team, but also the other parts of the organization as well, such as its customer management team.
You also want to choose a provider that has a clear history of proficiently delivering high-quality data labeling experiences to users who are developing machine learning models that may have similar use cases to you, as it would aid in alleviating concerns about their capability of delivering solutions to you.
You may also want to make further inquiries about the different practices and processes which the provider utilizes such as quality control, the support it provides for use case management, or how the provider organizes its workflows during scaling periods.
Before selecting a provider, you should not only consider the present size of your data but also its size as you scale up. As your machine learning model improves, you would require a larger volume of high-quality data. The provider you pick must be able to scale up as you do. Therefore, the labeling team must be flexible enough to react to increases in the volume of data to be labeled and the complexity of the task changing.
What often sets out a high-performing machine learning model from the rest is the quality of the dataset provided. The accuracy of the labeled data should be noted as low-quality data will result in a double-failure effect, whereby the first failure occurs as you train your model and the second occurs as the model uses that low-quality data to make any decisions in the future.
This tip is the resulting consequence of seeking proof of concept as you can investigate the quality of the labeled data before fully committing your entire dataset to a provider. While there are several reasons for low-quality data, it is often due to issues in people and process management. As such, you must pay attention to how a provider operates before making your choice. Your provider’s team should not work as a detached entity, but instead, you should both work in tandem, with the provider understanding how their work relates to the problem you are trying to solve.
Seek out a provider that understands the importance of high-quality labeled data and employs high-standard Quality Assurance (QA). Before choosing a provider, you should make inquiries about their QA method. Many providers often provide automated QA but, for specific datasets, using automation as the sole form of QA is not desirable, such as in text-based labeling (OCR). Instead, desire a provider that offers an additional QA layer performed by skilled annotators.
Choosing a data labeling platform is not an easy decision, and it requires much thought before committing to one but making the right choice is bound to reap high rewards. The provider you choose should understand your visions and goals and should be able to meet your maximum scaling needs. A high-quality provider should also provide you with a myriad of tools that would help in providing labels for all your use cases.
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