Anomaly detection in machine learning is the identification of rarities; whether it be items, events or observations that stand out distinctly from the previously overwhelming majority of data. Anomalies, more popularly known as outliers, are a result of crucial actionable insights that require a systematic approach to be assumed such as system errors, security breaches or change in data patterns. For more info: - https://macgence.com/blog/anomaly-detection-in-machine-learning/

Advancements in Artificial Intelligence are significantly notable, but what if there was a way to optimize the performance of these systems using little training data? This is now possible with retrieval-augmented fine-tuning (RAFT)—a novel strategy transforming the way AI and machine learning (ML) models are developed. Read more: - https://macgence.com/blog/retrieval-augmented-fine-tuning-raft-revolutionizing-machine-learning/