Data Annotation Challenges and Overcoming them with AI Data Labeling Companies

 

Ever since the dawn of the digital era, data has become crucial. The advent of Artificial Intelligence and Machine Learning has further increased its importance to a great extent.

Ever since the dawn of the digital era, data has become crucial. The advent of Artificial Intelligence and Machine Learning has further increased its importance to a great extent. Machine Learning algorithms coupled with AI learn and perform the desired actions using input datasets that are prepared with the help of humans.

As more and more businesses, whether big or small, rely on AI/ML-based applications to expand their paradigms—data collection and processing with minimal human error, therefore, becomes important. So, data annotation is the process by which Machine Learning models are trained.

Understanding Data Labeling

For a Machine Learning algorithm to perform like humans, it must be fed with the respective data that instructs it. The process of encoding this data in a language that machines can interpret is known as data labeling. With this process, different Machine Learning algorithms like Computer Visions and Natural Language Processing models perform the desired actions. Any errors or inaccuracies in the data labeling process can impact a business’s bottom line negatively, rather than streamlining or enhancing it.

Challenges in Data Labeling

Known that any AI model is as smart as the data it is fed with. Besides, adding accurate labels to the different parts of input datasets is a significant undertaking. It requires a dedicated amount of time and effort to be executed efficiently. Otherwise, AI/ML implementation can prove to be a failure, resulting in impeded efforts. First, let’s take a look at the challenges in labeling datasets that a majority of organizations face:

• Inappropriate Infrastructure

Getting the infrastructure that can efficiently store bulk volumes and support the labeling tasks needs a budget. Broadly speaking, any technical infrastructure includes maintenance, development, and up-gradation cost. Businesses that are not into core technical tasks often find this a taxing financial liability, opting for outsourced data annotation.

• Scalability & Flexibility

Training smart models requires constant volumes of high-quality data. A shortage of input datasets might interrupt the algorithms’ learning, resulting in inefficient outcomes. Collecting and labeling such large volumes of data poses a logistical challenge for many companies.

• Scarcity of Skilled Resources

Accurate data annotation is the key to getting precise AI outputs. If data labeling misses the mark, it might result in similar errors in AI like humans. Hence, it is vital to annotate data accurately—and to supervise these input datasets, organizations need skilled professionals. Hunting for such competent annotators is a challenge, especially when it impacts your model’s productivity directly.

• Monetary Issues

Setting up a data labeling department in-house needs monetary investments in terms of infrastructure, technology implementation, hiring, and training resources. Whereas, collaborating with an accomplished AI data labeling company is another way to consider. You only have to pay for the services involved and optimize operational expenditures.

In-House Data Annotation

It is important to know that the success of the data labeling process is rooted in the fact that effective use of this new-gen technology significantly contributes to increasing productivity. But, introducing AI/ML & data annotation is like initiating a whole department that involves infrastructure, skilled human resources, and a lot of finance. Let’s look at the pros and cons of in-house setup.

• Pros

One of the biggest reasons businesses prefer in-house setup is that your data remains hygiene and its integrity untouched. It is completely secured since it is not shared with any third party. Undoubtedly, stakeholders have control over the entire process. In addition to this, they can also keep a check on the overall cost of the project.

• Cons

On the negative side, talent hunting and skill training are difficult tasks. Hiring an in-house team adds to operational expenditures in terms of high-tech infrastructure development and maintenance of upgraded technology. The cost of hiring skilled resources is also exceptionally high due to the scarcity of trained personnel. Besides, the annotation process might get stuck in internal bias.

As evident, the cons of hiring an in-house data annotation team outweigh the pros. Collaborating with experienced data labeling companies not only helps businesses to get quality training datasets within the desired time, but also optimizes the operational costs. They address the data security concerns by following strict protocols and safety measures. So, isn’t this a win-win for companies looking to harness AI/ML?

Ending Note

Data labeling is a vital job. Different applications have different labeling requirements, for example- an autonomous vehicle operates on a different algorithm than a drone. The entire industry is highly dependent on human-in-loop wherein annotators currently depend on tools to prepare input datasets.

So, whether to perform it in-house or consult with professionals and engage in outsourced services depends on the complexity of the task. You might consider in-house an option if you have a limited labeling requirement or are very much concerned about the privacy of your data.

But large projects have to be labeled using techniques like semantic segmentation, bounding boxes, 3D cuboids, etc. In such a case, it is better to collaborate with professional service providers that are experienced and possess the required skills to align outcomes according to your goals.

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