Quality of data is what matters the most if you are planning to roll out AI and ML models. This realization happens when the AI/ML models do not perform adequately. Companies often realize this when it’s late—but it is never too late with data labeling outsourcing at your rescue. This piece highlights the non-negotiable benefits of investing in professional services…
Businesses are investing more and more in new-gen technologies like AI and ML to scale new heights. Before probing deeper, you must know what data labeling is? It is the process of adding tags to raw data such as images, videos, text, and so on. These tags and labels help the AI/ML models to calculate the attributes easily. They can detect and identify the objects and perform other functionalities they are designed for.
And, what makes any AI/ML model smart is the quality of input datasets used. So, if you are planning to roll out a smart model, make sure you prioritize the quality of data used. However, a majority of companies realize this fact when it’s too late and models fail to perform.
Hiring an in-house team is the holy-grail stakeholders find most reliable. Though it is a consistent labeling process that allows best practices for a feedback loop, it has serious limitations. Not only this, you have to either build custom annotation tools yourself to keep control of the quality or invest in licensing from third-party tools. The resource-intensive and time-consuming nature of the process don’t make it a practical solution for major companies.
On the other hand, businesses that resort to data labeling outsourcing find a happy medium. It is because the data labeling companies have the potential required to perform the annotation tasks. They have a pool of data professionals, accredited annotators, and subject matter experts equipped with proprietary tools to tackle immense volumes of data.
The quality of data is vastly superior when compared to other options like crowdsourcing and in-house team. The professionals leverage a time-tested blend of manual workflows to add labels to the input datasets. This helps your AI/ML model to calculate attributes easily. Apart from professional excellence, you enjoy a plethora of numerous other benefits as elucidated here:
- Strict Data Security Protocols
The data labeling companies are consultative. Collaborating with them helps you accelerate your AI/ML model implementation without trading off data integrity. They have strict data security protocols in place and follow set industry standards. All their practices are legally compliant; hence, you need not worry about data security.
- Flexibility and Scalability
One of the significant advantages of collaborating with professionals is they create end-to-end workflow solutions that cater to your unique needs. Their consultative approach is intended to help organizations be relieved from the burden of data labeling tasks. All you gotta do is define your needs and the professionals will execute them for you.
- Stringent Quality Control
As mentioned already, data quality is of immense importance. Acknowledging this fact, the outsourcing vendors leverage tried-and-tested processes that ensure a reliable throughput of annotated datasets to be fed into the machine learning algorithms. This selective approach equates to stringent quality control. They can accurately and rapidly label large volumes of data sets without compromising on quality.
What Works for You?
While in-house data labeling might be your preferred choice, it isn’t always the only solution unless you are a large-scale company that can overcome logistical challenges and afford to perform the tasks in-house. The upside of collaborating with data labeling companies is that the professionals take care of the entire pipeline; and most important of all, quality is guaranteed!
So, if your AI/ML model isn’t working up to the mark or data labeling needs are holding them back, consider consulting professionals like Damco Group. Having 25+ years of experience in catering to the unique needs of the industry, the professionals here offer you a collaborative workflow to help you fuel your smart models.
