We work a lot with text processing, vectorization, and other NLP tasks. Sometimes, we need to process websites, presentations, or optics quickly because they're used in user engines and other applications. We use PyTorch to test our implementations as well.

We use the solution for reliability engineering. We apply ML techniques to predict product failures and identify the reasons behind those failures.

Our primary use case for this solution is training your mathematical models. We are a data science team that trains mathematical models with this solution. It can spin up VMs, and you can use it up, or in your local machines.

There have been a couple of use cases. Firstly, we use it for generative AI, specifically a style transfer kind of use case. 

The other use involves building classifiers over video streams. So, it's mostly classification as well as severity-related use cases.

I work within the field of NLP and data logistics, but most of the information is stored in the description. I mostly use NLP algorithms to extract the sentiment or the similarity if they have a similarity score calculation. For example, something like the windshield cracking, or something extremely severe like a garage burning, or multiple car damage. 

The NLP tasks don't deal with the financial data directly but use extra information to get some insights. It's not really a good practice in India to use deep learning models directly on financial data because of audit reasons.

It is mostly used for everything that is connected to machine learning, data science, training, and other similar things.