We use Machine Learning techniques to add intelligence to the applications we build. These AI services are seamlessly integrated into the software platform with top performance, state-of-the-art architecture and methodologies, with improved security and privacy. We have leveraged applied AI in several use cases such as:
Real-world ML systems are composed by several components; Hydrogen platform will be responsible, among others, for serving the infrastructure, extracting features, monitoring and analysis. The ML code will be a component or service integrated in the platform.
These are the most common steps of ML projects:
We provide statistical analysis on large-scale databases to extract data patterns and knowledge.
We teach computers to perform tasks without rules or domain specific knowledge. For this purpose we have used supervised and unsupervised techniques, for classification and regression problems for the former, and clustering (similarity) for the latter.
Using reinforcement learning our software learns from trial-and-error for well defined problems.
We use this approach to solve intuitive problems like segmenting an image or understanding text. By gathering experience from data and using state-of-the-art deep neural networks architectures trained on GPUs, our software is able to do tasks like predictions.