In this blog post we discuss a tool we build to ensure data used for training is balanced and has minimal labelling bias.
Training a machine learning model from scratch is hard. It is a common misconception that building the model itself is the hardest part of training a model. In reality, the data collection and annotation process is far harder than people assume.
In this blog post, we talk about how Active Learning, which sits at the heart of the Re:infer platform, reduces the amount of data required to train a high quality model.
When building and training a machine learning model, understanding performance is essential. Even the most advanced model will produce incorrect predictions.
We just published an accessible blog post where we discuss some of the problems that arise from different ways of measuring performance, and some of the solutions Re:infer uses to simplify the process.
Check out a new post on our developer blog on why you should care about model validation