Outsourcing Data Annotation: Pros & Cons
Data annotation is one of the most important processes involved in any machine learning or AI project. It involves taking raw training datasets and preparing them by labeling or tagging the needed information that the machines will need to learn. As we will see later on, there are many different types of data annotations that can be performed, depending on the individual needs and requirements of the company creating it. Annotating datasets is a very time-consuming process which is why a lot of businesses choose to outsource such to service providers like Mindy Support. In this article, we will tell you about all of the benefits of outsourced data annotation projects and how they can save you a lot of time and resources. Let’s start by looking at some of the different types of data annotations.
What Types of Data Annotations are Possible?
Data annotation can be broken down into two groups: image annotation and text annotation. Image data annotation includes:
- 2D Box Annotation – This is simply drawing a 2D box around the object the system will need to recognize.
- 3D Bounding Box Annotation – This method implies drawing a 3D box around an object which gives the system additional insights as to the length, width, and height of the objects.
- Semantic Segmentation – This is the most detailed form of annotation and involves labeling each pixel of an image with a corresponding class of what is being represented
- Data Labeling – This is simply labeling all of the needed objects and people in the image
- Landmark Annotation – This form of annotation is most often associated with facial recognition technology and involves marking key points in a specified area, such as peoples’ faces
There are also projects involving text annotation. This includes:
- Optical Character Recognition – This is converting of images of typed, handwritten, or printed text into machine-encoded text
- Transcription – This process involves converting audio files into text
- Classification and Categorization – This os when you need to apply certain tags to the text depending on the context
In-House vs. Outsourced Data Annotation Projects: Which is the Best Way to Go?
There are usually two common approaches in implementing data annotation projects: in-house data annotation or outsourcing it to a service provider. The first method is very difficult to implement because it is very costly. Machine learning projects usually require thousands of images and texts to be annotated and hiring a team in your local market to do such routine work can be costly. It would also not make sense to ask your developers or other staff members to do the annotation because their time would be much better spent on actually doing their core functions.
Outsourcing your data annotation projects is a much better idea because it allows you to get more annotation work done for a lesser price. The cost of labor in countries like Ukraine is a lot less than in your local area and you will not have to pay for renting additional office space, recruitment fees, and other overhead costs. Also, an experienced service provider, like Mindy Support, will guide you through the implementation process by offering you best-practice insights on many years of successfully actualizing data annotation projects. Let’s take a closer look at outsourcing data annotation.
Outsourcing Data Annotation Work
There are many benefits to outsourcing data annotation work such as:
- Cost savings opportunities
- Higher quality of annotation work
- Better scalability
- Timely availability
- Mitigatigate internal bias
- Increased data security
While there are many benefits to outsourcing your data annotation work, there are some drawbacks such as:
- With so many service provider it is hard to choose the right one
- There may be some language barriers that need to be overcome
- You need to give up some of the control over the process
Which Type of Projects Should You Outsource?
One of the most common projects that are outsourced is in the ones in the automotive, healthcare, and agriculture industries. With all of the time and resources invested in self-driving cars, a lot of data annotation work is required to actually put such a vehicle on the road. They have to recognize other vehicles, pedestrians, street signs and, in general, follow the rules of the road. This requires a lot of semantic segmentation and labeling to properly annotate the dataset.
Healthcare projects are also very good to outsource because of the expert knowledge some projects require. For example, a lot of companies are working on AI technology that analyzes medical images and produces a diagnosis. Usually, only somebody with medical education can annotate such medical images, but they would be very costly to hire in your local area. There is a lot of semantic segmentation and contouring required to properly annotate these images.
In the agriculture industry, robotic process automation (RPA) is taking over a lot of the routine and time-consuming tasks that were performed by humans in the past. This includes things like picking fruits and vegetables, spraying herbicides, pulling weeds out of the ground, and many other tasks. AI-powered robots rely on LiDAR technology which produces a 3D point cloud, which is a digital representation of the way the robot sees the surrounding world. Human data annotators must label all of the objects in the 3D point cloud so the robot will rely on this annotated data to recognize the objects in its working environment.
How Much Do Data Annotation Companies Charge?
Since every project is unique, it is difficult to provide the exact pricing companies charge. If you would like to know the costs of your individual project, contact us today. We have more than 2,000 employees in 6 locations all over Ukraine and we can actualize your project faster and with greater quality than the competition.
Benefits of Outsourcing Your Data Annotation Project to Mindy Support
There are many benefits Mindy Support can offer clients. Some of them include:
- Compliance with international standards – Mindy Support is ISO 27001:2013 certified and compliant with GDPR, CCPA
- Scalability – As we mentioned before, we have more than 2,000 employees already on staff which allows us to scale projects within a short time frame.
- QA processes – The QA process we have in place ensures the quality of annotation but also makes sure that the quality remains intact when we scale a project.
- Precise diagnostics of client requirements – We sit down with you to learn about the outcomes you would like to see and create a clear road map on how to get there
- Constant and agile communication with the client – We constantly keep you informed about how the project is progressing and if there are any issues.
Outsourcing Your Data Annotation Project is the Best Way to Go
Finding a company that can perform accurate data annotation can be difficult if you don’t know what to look for. Even projects that only require labeled data need to be performed with a lot of precision since the success of your project depends on it. Mindy Support understands the importance of data annotation and data security which is why companies of all sizes, from startups, to Fortune 500 companies trust us with their data annotation needs. Contact us today to learn more about what we can do for you or browse through our case studies to find out about the results we provided for our clients.
February 5th, 2021 Mindy News Blog
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