Best practices for monitoring ML models

Test and monitor Machine Learning models

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Machine learning is a subset of artificial intelligence (AI) that involves the development of algorithms and statistical models that enable computers to learn and make predictions or decisions without being explicitly programmed to do so.

In traditional programming, a programmer writes specific instructions for the computer to follow. However, in machine learning, instead of explicitly programming the computer with rules to follow, the computer is trained on large amounts of data and learns from patterns within that data. This allows the computer to make predictions or decisions based on new data that it hasn’t seen before.

Machine learning algorithms can be broadly categorized into three types:

1. Supervised Learning: In supervised learning, the algorithm is trained on a labeled dataset, where each input is associated with a corresponding output. The algorithm learns a mapping from inputs to outputs, enabling it to make predictions or decisions on new, unseen data.
2. Unsupervised Learning: In unsupervised learning, the algorithm is given unlabeled data and tasked with finding patterns or structures within the data. This type of learning is often used for clustering similar data points together or for dimensionality reduction.