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Choosing the Right Data Annotation Platform for Your Machine Learning Project
Choosing the Right Data Annotation Platform for Your Machine Learning Project

May 24, 2023

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Artificial intelligence, or AI, has become an important facet of our lives, which we follow even without realizing it. The technology has progressively become mainstream, with several business enterprises implementing it, along with machine learning models, into their processes. Business enterprises new to implementing AI are often unaware of the work that goes into sourcing, collecting, and testing data. So, when the data is sourced, it is received as raw and unprocessed. It is only when the data is properly labeled that it becomes useful. And it is only by using a data annotation platform such as Zastra that high-quality data can be derived for specific use cases. Also, by choosing the right data labeling platform, the launch of AI algorithms and machine learning models can be a success. 


With the demand for machine learning (ML) applications increasing, data annotation or labeling has become the key to building successful ML models. Here, data annotation is about labeling data points to provide context and meaning to raw data, which is then used to train ML algorithms. The process is manual and often time-consuming. This is why selecting the right data annotation platform is crucial. However, before that, it is important to know what data annotation is all about. 
 

What Is Data Annotation?

Data annotation means labeling data to make it readable by the machine learning model. Low-quality or poorly labeled data can lead to losses to the tune of $15 million per year (source: Gartner). On the surface, data labeling activity seems trivial, but that could not be further from the truth. A data annotation platform such as Zastra can help businesses minimize annotation efforts while delivering top-quality classification, detection, and segmentation of image and video datasets. 

 

Factors to Consider When Choosing a Data Annotation Platform

The key to driving success in implementing AI-ML models is the presence of a large quantum of well-labeled and high-quality data. The factors or criteria to choose the right data annotation tool are as follows:

1. Annotation Types: The first step to selecting a data annotation and labeling platform is identifying the types of annotations the machine learning model require. Some of the common annotation types would include object detection, image classification, and natural language processing.

2. Annotation Quality: The quality of data annotations or labeling is critical to the accuracy of the ML model. Businesses should choose a platform that not only offers high-quality annotations but also has quality control mechanisms in place. It is only the platforms that employ multiple annotators for the same data point and use statistical measures to identify discrepancies that can help deliver top-quality annotations.

3. Customization Options: Machine learning projects have different requirements and need customization to meet them. Select an annotation platform that offers a host of customization options. These may include the ability to create custom annotation types, adjust annotation settings, and provide domain-specific knowledge.

4. Data Security and Privacy: Data security and privacy have become crucial for any project, let alone an ML project. So, choose a platform that provides robust data security and privacy features, such as data encryption, access control, and stringent compliance with data protection regulations.

5. Integration: An ML project often involves the use of multiple tools and platforms. Therefore, it is crucial to choose an annotation platform that can easily integrate with other tools and platforms. For instance, the platform should offer APIs and SDKs for easy integration with other software.

6. Pricing: Data annotation platforms can have different pricing models. These may include pay-as-you-go, subscription-based, and per-annotation pricing. Hence, businesses should choose a platform that fits their budget and preferences for a pricing model.

7. Support: The data annotation platform should provide excellent customer support, including technical support and training. Also, it should have a manual for the users to know about the ways to use it. Thus, choose a platform that provides user guides, tutorials, documentation, custom onboarding, and responsive customer support.

8. Time: Any computer vision model can take an enormous amount of data labeling time to get it right. The criteria to consider while building such a model would include calculating the time to build the model, and the time to test and update it. 

 

Conclusion

With data annotation becoming the key to the success of any ML project, it is important for data labeling services to leverage the right platform. However, while choosing the platform, consider the above-mentioned criteria, such as annotation types, annotation quality, customization options, data security and privacy, integration, pricing, support, and time. It the end, the data annotation platform should help minimize efforts, maximize collaboration, and enable active learning of datasets. 


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