AI model is only as good as the training dataset

How to build your first AI model effectively?

When building first AI models, especially for popular tasks like people or car detection, ML developers quite often rely on available free datasets developed by universities and companies. However being a good choice for a start, in many real-life use cases the data turn out to be insufficient for developing a reliable and trustworthy AI solution, especially if they are supposed to work in specific environments or conditions.

Proper preparation of training datasets should ensure they are representative and unbiased and include types of data that the model will operate on. In developing models, researchers should consider what data they should use and determine the necessary data volume, data attributes and annotation strategy, including specified edge cases.

Such a data-centric approach may improve the performance of your outcome model far more than fine-tuning model parameters or augmentation.


How to achieve best results with your AI/ML projects?

To achieve your goals you need to spend hundreds if not thousands of hours preparing and analyzing data, one of the most important stages of this type of project is labeling the data necessary to train your models. Despite appearances it is not an easy task and most companies or sturtups are not well prepared for it. Often the resources for data annotation are underestimated and the assumed time for this stage in the context of the whole project plan is too short.

As an example image or video annotation typically involves human-powered work and is defined as the task of annotating an image or video with labels. What and how images or videos need to be annotated, depends on main model training purposes like classification, object detection, or semantic segmentation.

The good news is that you don’t have to allocate your own, mostly expensive resources, learn from your mistakes and discover how to handle the data annotation process correctly. With companies like, which provides the best annotation services for enterprise companies and startups, you can focus on developing your AI applications and leave the annotation task to the professionals. As in many other areas of IT, outsourcing is a great way to help your company move through the various stages of development, especially when it comes to projects that involve large data sets, there is always manual work to be done, such as data cleansing, annotation, or augmentation.