The most advanced ML Observability product in the market
Building an ML platform is nothing like putting together Ikea furniture; obviously, Ikea is way more difficult. However, they both, similarly, include many different parts that help create value when put together. As every organization sets out on a unique path to building its own machine learning platform, taking on the project of building a […]
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We’re excited 😁 to share that Forbes has named Aporia a Next Billion-Dollar Company. This recognition comes on the heels of our recent $25 million Series A funding and is a huge testament that Aporia’s mission and the need for trust in AI are more relevant than ever. We are very proud to be listed […]
A natural language processing model can be an extremely effective tool for extracting information from unstructured text data.
NLP models are a statistical tool for predicting words by analyzing patterns of language.
As a result of NLP, a model can combine many capabilities, including audio-to-text conversion, speech recognition, sentiment analysis, summary, and spelling checking.
When an NLP model is in production, a number of issues can arise, including: data drift, missing value rates, unexpected new values, etc. To make sure your model is working as intended, it’s important to monitor your NLP models in production, detect issues early, and intervene when necessary.