What is the impact of dataset size on ML models? Are large models better, always? or could you have smaller, better models trained on smaller/smart datasets? Introduction In the world of ML, 2019 was the year in which NLP had its imagenet moment. This is the year where applying transfer
When we design many ML applications, particularly classification systems, we come across a common problem wherein the dataset will be skewed in nature, i.e. we have some classes of data that may be seen more than other types. Take the example of the number of indicators of compromise (IoC) compared
Rita Anjana - NLP Engineer - Elemendar The AI Engine we are developing reads and translates a huge volume of unstructured text into structured and insightful reports(STIX), currently I am working on two key aspects: Data & the Entity Extraction model. A machine learning model is only as good as
Elemendar has developed an Artificial Intelligence capability that reads human authored cyber threat intelligence and translates it into machine-readable industry-standard structured information.
How do you make the concepts of risk and threat work together at the operational and tactical levels?
Read our latest blog to find out more 👇
#riskmanagement #nistframework
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