Top Four Myths About Outsource Data Annotation Services
With rising of Artificial Intelligence (AI) and Machine Learning (ML) supported machines, systems, and applications, other fields are also getting the advantage of these advanced stage technologies. To develop AI or ML-enabled applications, a huge quantity of data set is required to help machines recognize objects through computer vision.
Generating such datasets is not possible for companies to involve in AI or ML model development. Outsourcing is the best option to get the right data as per the needs within the budget. Perhaps, few fellowships bother to rely on others and avoid outsourcing owing to myths that conserve their minds making it for them difficult to acquire training data.
Getting Annotated data is not possible, only experts can do this job better. Clear the myth from your mind and read here the top four myths about outsourcing data annotation with few suggestions to know how to outsource the data annotation.
#1 Is My Data is Safe and Remains Confidential with Annotators?
This is one of the top reasons, companies try to annotate the data at their in-house resources. Actually, losing the data could be dangerous for businesses, but it’s not true, outsourcing companies also operate professionally and keep the client’s data in safe hands.
Even, you can sign legal agreements with terms and conditions to not share any details about your data. In data labeling or annotation, not only the annotated data but raw data is also kept confidential till the project is handed over to the client.
Conversely, in-house annotation would be costly and also required time and more effort in such a task which a professional company can do with better results. Cogito is one of the well-known data annotation companies providing high-quality training data for machine learning and AI development at affordable costing.
#2 In-house Annotators Can Do This Job Or Don’t Need Expertize
This is also hearsay, that annotators are not skilled or not specialized enough to annotate with that quality, instead of in-house annotators can do this job easily. Actually, nobody knows better than you what is your model, but it doesn’t mean you can cater to other aspects, rather specialists can also understand your requirements.
If you outsource data annotation to companies working with experts you will get better results. Actually, such companies have specialists for each type of project as per the requirements of the field, so they better understand the technical specification of the annotations.
These specialists have worked on multiple projects, hence they know what kind of issue might come while performing the annotation task. The best part of working with professionals is that they will also save your time and cost with better results.
#3 You Think Your Use Case is Too Complicated than others
Many AI or ML companies assume that their use case is too much complicated, that data annotation companies will unable to meet the requirements. But it is not true, having a great discussion with both team members can easily clear the exact requirements, and annotators can deal with any type of industry for providing the training data.
Actually, data outsourcing companies already worked with different organizations, hence they understand the complexities of models of different industries allowing developers to work on complex models and produce quality results.
Cogito is one of them delivered the training data for all leading sectors use cases including healthcare, agriculture, retail, robotics and drone training. It can help to solve the computer vision problem with accurately labeled data for your project.
#4 Hiring third-party Annotators are Expensive
You have already spend a handsome amount of money on a machine learning engineer or data scientist to develop a fully functional model for you. Compromising with other inputs will give you poor results. And training an AI-enabled model through computer vision is a difficult job when you assign such tasks to internal resources.
You hire a team of annotators consists of top-to-bottom experts who need regular monitoring with a handsome amount of money to work on such projects incurring an extraordinary cost. While hiring the third-party service provider will do this job at low cost, who have a dedicated team and other resources to perform this task more economically.
Hope your story towards outsourcing data annotation has changed up to some level. Similarly, there are numerous other myths that confound developers delay their projects causing notional loss. Outsourcing data annotation is a cost-effective decision, specialized, and more focused result-oriented data available for unique developments in AI fields.