Even with low cost widely available cloud computing, it can take significant time and compute power to train machine learning models on large data sets. This is expensive and is often at odds with the net-zero carbon goals of many organisations today. Throwing more data at a problem isn’t always the best answer, and by using AI that is responsible by design, we can reduce these problems while maintaining performance. Active learning is one of the methods we use at Mind Foundry to achieve this.
3 min read
How active learning can train machine learning models with less data
Topics: machine learning data science Continuous Meta-Learning
5 min read
Hyper-Parameter Tuning: What it is and why it’s natural
Hyper-parameter tuning can be observed in many everyday processes, but few people are aware of its existence. This blog aims to change that, and demonstrate its true value in data science.
