The most advanced ML Observability platform
We’re super excited to share that Aporia is now the first ML observability offering integration to the Databricks Lakehouse Platform. This partnership means that you can now effortlessly automate your data pipelines, monitor, visualize, and explain your ML models in production. Aporia and Databricks: A Match Made in Data Heaven One key benefit of this […]
Start integrating our products and tools.
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.