In 2020, 1.7 MB of data was created every second by people. And in the same year, we produced close to 2.5 quintillion data bytes every day in 2020. Data scientists predict that by 2025, people will generate close to 463 exabytes of data daily. However, not all the data can be used by businesses to draw useful insights or develop machine learning tools.
Yet, when companies are contemplating developing AI models, there will come a time when they have to take a hard decision – one that could impact the outcome of the ML model – in-house or outsourced data labeling. Your decision could affect the development process, budget, performance, and success of the project. So let’s compare both and recognize the advantages and disadvantages of both.
In-House Data labeling Vs Outsourcing Data Labeling
| In-House Data Labeling | Outsourced Data Labeling |
| Flexibility | |
| If the project is simple and doesn’t have specific requirements, then an in-house data labeling team can serve the purpose. | If the project you are undertaking is quite specific and complex and has specific labeling needs, it is recommended to outsource your data labeling needs. |
| Pricing | |
| In-house data labeling and annotation can be quite expensive to build the infrastructure and train employees. | Outsourcing data labeling comes with the freedom to choose a reasonable pricing plan for your needs without compromising quality and accuracy. |
| Management | |
| Managing a data annotation or labeling team can be a challenge, especially since it requires investment in time, money, and resources. | Outsourcing data labeling and annotation can help you focus on developing the ML model. Additionally, the availability of experienced annotators can also help in troubleshooting issues. |
| Training | |
| Accurate data labeling requires immense training of staff on using annotation tools. So you have to spend a great deal of time and money on in-house training teams. | Outsourcing doesn’t involve training costs, as the data labeling service providers hire trained and experienced staff who can adapt to the tools, project requirements, and methods. |
| Security | |
| In-house data labeling increases data security, as the project details are not shared with third parties. | Outsourced data annotation work is not as secure as in-house. Choosing certified service providers with stringent security protocols is the solution. |
| Time | |
| In-house data labeling is much more time-consuming than outsourced work, as the time taken to train the team on the methods, tools, and process is high. | It is better to outsource data labeling to service providers for a shorter deployment time as they have a well-established facility for accurate data labeling. |
When Does In-House Data Annotation Make More Sense?
While there are several benefits to data labeling outsourcing, there are times when in-house data labeling makes more sense than outsourcing. You can choose in-house data annotation when:
- The in-house teams can’t handle the large data volumes
- An exclusive product is known only to company employees
- The project has specific requirements available to internal sources
- Time-consuming to train external service providers
4 Reasons You Need To Outsource Your Data Annotation Projects
Expert Data annotators
Let’s start with the obvious. Data annotators are trained professionals who have the right domain expertise required to do the job. While data annotation could be one of the tasks for your internal talent pool, this is the only specialized job for data annotators. This makes a huge difference as annotators would know what annotation method works best for specific data types, best ways to annotate bulk data, clean unstructured data, prepare new sources for diverse dataset types, and more.
With so many sensitive factors involved, data annotators or your data vendors would ensure that the final data you receive is impeccable and that it can be directly fed into your AI model for training purposes.
Scalability
When you’re developing an AI model, you’re always in a state of uncertainty. You never know when you might need more volumes of data or when you need to pause training data preparation for a while. Scalability is key in ensuring your AI development process happens smoothly and this seamlessness cannot be achieved just with your in-house professionals.
It’s only the professional data annotators who can keep up with dynamic demands and consistently deliver required volumes of datasets. At this point, you should also remember that delivering datasets is not the key but delivering machine-feedable datasets is.
Eliminate Internal Bias
An organization is caught up in a tunnel vision if you think about it. Bound by protocols, processes, workflows, methodologies, ideologies, work culture, and more, every single employee or a team member could have more or less an overlapping belief. And when such unanimous forces work on annotating data, there is definitely a chance of bias creeping in.
And no bias has ever brought in good news to any AI developer anywhere. The introduction of bias means your machine learning models are inclined towards specific beliefs and not delivering objectively analyzed results like it’s supposed to. Bias could fetch you a bad reputation for your business. That’s why you need a pair of fresh eyes to have a constant lookout for sensitive subjects like these and keep identifying and eliminating bias from systems.
Since training datasets are one of the earliest sources bias could creep into, it’s ideal to let data annotators work on mitigating bias and delivering objective and diverse data.
Superior quality datasets
Like you know, AI doesn’t have the ability to assess training datasets and tell us they’re of poor quality. They just learn from whatever they are fed. That’s why when you feed poor quality data, they churn out irrelevant or bad results.
When you have internal sources to generate datasets, chances are highly likely that you might be compiling datasets that are irrelevant, incorrect, or incomplete. Your internal data touchpoints are evolving aspects and basing training data preparation on such entities could only make your AI model weak.
Also, when it comes to annotated data, your team members might not be precisely annotating what they’re supposed to. Wrong color codes, extended bounding boxes, and more could lead to machines assuming and learning new things that were completely unintentional.
That’s where data annotators excel at. They are great at doing this challenging and time-consuming task. They can spot incorrect annotations and know how to get SMEs involved in annotating crucial data. This is why you always get the best quality datasets from data vendors.
[Also Read: A Beginner’s Guide to Data Annotation: Tips and Best Practices]
