Thanks to great harmony between hardware capacity and opensource software, Machine Learning (ML) and especially Deep Learning (DL) is in its golden time. Companies compete on leveraging ML capabilities to make their products more accurate or offer new products that could not exist without ML. The essential components of an ML project are:

I. Data which needs tooling
II. Set of approaches and technologies which needs researchers who design the roadmap,
III. and great team of ML-developers to realize the idea

Best case would be undoubtedly to have all the above teams in your company. However, ML-technologies are expensive and building a Full-stack ML team is not always the best strategy for managers.

Here is where we at AI-Bridge define our role as a consulting agency. We build a bridge between your technical requirements and the world of Artificial Intelligence (AI). From the above-said components for ML projects, the data part (I.) is normally available in the field and could be acquired with some tooling. In the case of data confidentiality, we only need a very small subset of data to design models and perform all trainings on your authorized servers.

The second component (II.) is normally partly available inside the company. That is the company’s field-specific know-how for solving technical problems. On the other side of, let’s assume, the bridge there are ML technologies and solutions. An undeniably important component for building this bridge is how these field-specific problems could be translated to ML-problems and what set of technologies is the optimal set for problem-solving. We make this possible with our great access to researchers in the community of ML.

Finally, to process data at scale and make added-value for your company, it’s essential to have ML-developers with hands-on experience on delivering standard industrial code (based on iso 9126). AI-Bridge generates its problem-solving power by a great set of researchers and developers.