What is a/an...
AI Block: Your custom machine learning model based on the training data you have trained it with.
Label: Also known as a tag, category, target, or class. It is what the model will provide to you for unseen data.
Unlabeled data: Any data that hasn't been assigned a category
Error rate: We keep some amount of training data for testing and at the end of the training process, we automatically measure how many errors the model makes per 100 predictions.
Human review: Our platform comes with the ability to give feedback on predictions that the model is unsure about.
Prediction: What the AI Block believes to be the right label for an unseen data point.
Flow: We have a visual workflow builder that lets you connect different applications, AI Blocks, and determine the right actions.
Performance: Simply put, how well the AI Block does based on the information it has. Learn more about how we measure performance.
Confidence: Much of machine learning involves estimating the performance of a machine learning algorithm on unseen data. Calculating the confidence is a way of quantifying the uncertainty of such a prediction.
Classification: Sorting data points into different buckets. Read more about the subject here.
Trigger: Every Flow requires a start signal. This can be a new file, an email attachment, or any other event that you determine to be the starting point of the process.
API: Application programming interface. Learn more about APIs.
Integrations: We have developed direct connections with various 3rd-party applications that allow you to move data and actions between them in a seamless fashion. We are adding new ones on a continuous basis and you can find the complete overview here.