What if you could push your AI models to be 10 times faster?
Modern Machine Learning (ML) and especially Deep Learning (DL) have become deceptively easy. It is almost trivial to have something up and running with presentable results as the the tools hide most of the complexity and hard decisions. Whereas that might be good enough for a Proof of Concept (POC) or a Minimum Viable Product (MVP), ensuring stability, high performance and scalability is a whole different ball game.
Many CXO’s and seniors managers find themselves trapped into subpar solutions that cannot be used effectively because they lack the technical know-how to productionalize them. At ELECTI we have that knowledge and we can help you go that extra mile from POC / MVP / Demo to full scale product.
Deep neural networks have proved to be a very effective way to perform Machine Learning tasks. They excel when the input data is high-dimensional, the relationship between input and output is complicated, and the number of labeled training examples is large.Nevertheless, top-performing ML systems can be expensive to store, slow to evaluate and hard to integrate into larger systems. We replace such cumbersome models with simpler ones that perform equally well, making them more efficient thus saving you time and resources.
Most companies employ off-the-shelf open source R&D models that are highly inefficient and very power hungry when deployed at scale. We work with GPU high performance environments such as TensorRT than can cut the inference time and resources needed by a huge margin thus saving infrastructure costs.