The nuances of language can be difficult for a machine to understand, hence the need for human input to accelerate testing and ensure quality control.
Sentiment analysis can turn the abundance of online information into actionable insights, but machines can’t do everything by themselves.
No matter how robust your initial training may be, keeping your machine learning models up-to-date is essential. Here are two retraining approaches.
Your training data operations are like assembly lines: data is your raw material, and you have to get it through production steps to structure it for AI. You need skilled people ...
Anonymous crowdsourcing is a common alternative to an in-house team for AI development. It can be a cheap option for training machine learning algorithms but it’s rarely as ...
Given the challenges of hiring and managing a team to complete the arduous data work behind AI, many companies are turning to outside help.
AI innovators rely on external teams to structure data for ML algorithms. But scaling quality data requires the right people & processes in your tech stack.