Outsource Data Annotation for ML Model Validation

Generally Machine Learning is known as a tool for making predictions. But it becomes possible only after several steps of data preparation, building and ML model training, and then its validation. And here is Model Validation is last but not least part of the whole process. 

The Importance of Model Validation in Machine Learning

Model Validation is a process that we use after Machine Learning model training to evaluate its abilities to do tasks we expect. There are different testing datasets made for each model separately to assess exactly those points we need. The result of Model Validation is understanding if the model achieves set goals or not and improving it if not. Generally speaking, it is the evaluation of the model’s effectiveness. 

Validation model is probably the most important part of all this process as it gives us an opportunity to evaluate this ability of our model. Moreover, different models can make different predictions using the same data because they may have various working algorithms. In this case Machine Learning Model Validation is also a very important part that can check the accuracy of the results. Thus, Model Validation can ensure accuracy and stability of the models. 

Model Validation Methods

To check and evaluate models we can use various Model Validation methods according to the goals we pursue and expected results. Thus, there are such methods:

  • Machine Learning Cross Validation Method for Models
  • Teach and Test Method 
  • Leave-One-Out Cross-Validation Machine Learning 
  • Overriding Mechanism Method
  • Random Subsampling Validation
  • ML Model Validation made by Humans
  • Bootstrapping ML Validation Method
  • Holdout Set Validation Method
  • Running AI Model Simulations
manual ml model validation

Pros and Cons of Human vs Automated Model Validation

Human Model Validation


  • reliable and precise
  • correct at once finding a problem
  • high quality


  • may take a lot of time
  • probable mistakes or missings because of “human factor”
Pros and cons of human ML model validation

Automated Model Validation


  • works fast
  • unbiased checking
  • no “human factor” and missings connected with it


  • demand to correct mistakes afterwards
  • need to build a checking algorithm very precise
Pros and cons of automated model validation

Use Cases of Model Validation

There are not only multiple methods of realization Machine Learning validation but also several use cases of it.

Most often Model Validation is used for Machine Learning tagging or AI video tagging. It is a process of making labels on necessary objects. But machines cannot do it accurately all the time so we need to check their work and improve it. Moreover, the results depend on how well trained the model is. So that we should check the results and teach the model once more if necessary. Or, maybe, we need to extend tasks the model manages with. In that case improving the learning system is also an important part. 

So, here are some use cases to clarify the abilities Model Validation gives us.

#1 Validation of Pre-trained ML Models

When annotating images taken by CCTV, for example, Machine Learning models may make mistakes in object recognition. Model Validation can help find and correct such mistakes. What is more, it can help to teach the model to work better in future using already found problematic cases. In such a way Model Validation helps develop memory that can improve further predictions made by this model. This variant can be used in autonomous vehicle annotation as there is a need to recognize objects precisely. 

#2 Analyzing Overlooked Objects

It means checking and analyzing probably missed or ignored by the model objects and further correcting them. It also can help to build a better learning system for the model. In this variant already validated data can be used for the model learning once more. But if the dataset is not enough for this model mistakes may occur. 

#3 Learning by new datasets

This variant can cover the drawbacks of previous. The best way to do it is using Cross Validation in Machine Learning. This method evaluates the efficiency of the model using new data. The main point is that if the model deals well with concrete data it does not mean that it will deal as well with another one. Cross Validation Machine Learning is the best variant of validation used to train your model work perfectly. 

#4 Authenticating Facial Annotations

Also Model Validation can be used for facial annotations. In this case it may be super important the occurrence of the results so it must be checked. It can be image facial annotation or video annotation.

According to all mentioned above you can see that validation Machine Learning is widely used in labelling data for Machine Learning. It is an important part of making all that process working well.

validation services

Benefits of Outsourcing Annotation for Model Validation to Professionals

If now you are passionate about all the advantages Model Validation in Machine Learning can give you but are upset because you cannot imagine who would do that for you, we have a good variant for you! Outsourcing may be the solution. 

And while outsourcing has its advantages, a common question is the geographical location of these services. This video sheds light on the evolving landscape of the Data Annotation industry, addressing whether it’s gravitating towards specific offshore or nearshore destinations.


Now let us dive into specific benefits you may receive if address outsourcing:

  • It is cost-effective. Outsourcing is probably the best variant to save some money. And it can be especially good for Model Validation. As it is a relatively small task it will be at least strange to search for a constant employee for it. Instead, outsourcing gives you an opportunity to hire a programmer that will help you with that task and you will pay only for work done. Surely it is the best solution in this situation!
  • You can start working immediately. Outsourcing some tasks on bpo data annotation services you should not do documentation or waste your time on some other dumb things. A specialist can start your task the same or next day so that you will get the results faster.
  • Highly accurate work. When giving your task or project to the outsourcing company in order to outsource data annotation services you will get nearly perfect results with high guarantees from the company’s side. It is really important for them to make the client satisfied so you can rely on them.
  • Less worries, less hassle. As it has been already said before, it is important for companies to make their work perfect and leave you satisfied, so you should not worry when outsourcing the project. Besides, you can hire a specialist in data labelling outsourcing in just two clicks, that is surely the easiest thing you can do.
  • Specific solutions. When appealing to a company specialising in data annotation outsourcing you can receive some important pieces of advice about what will be better for your concrete project. It can also help with some more unusual or difficult tasks, for example, speech annotation. So you must not learn more about it by yourself but still get high results suitable for your business. 
All Types of Data Annotations Covered

Mobilunity-BPO.com Is a Company With a Deep Experience in Data Annotation

Mobilunity-BPO is a 10+ years experienced company working with companies all over the world. It works in such industries as helpdesk support, telemarketing, online research, database management, data entry and many more!

You also can find here Data Annotation Services, Back Office Support Services, Marketing and Sales Services, Customer Support Services. 

Among the Data Annotation Services you can get Managed annotation service, Part-time dedicated annotator and Full-time dedicated annotator. Managed annotation means that you can just send us your data and we will do the annotation per set deadline. 

Besides that you can hire a part-time and full-time specialist with us. The difference is in your demands. For short, unstable projects or projects with tight deadlines it is better to hire a part-time expert that will adapt for the task fastly and do it perfectly. For long-lasting projects, vice versa, it would be better to hire a full-time specialist that will learn your project and become a part of the team.

Data Annotation Services Offered by Mobilunity-BPO

So if you are looking for the better validation services most likely you have already found them!

Need good ML model validation services for your business? Do not hesitate, contact us right now!