Machine Learning (ML) is a subset of artificial intelligence that focuses on building systems that learn—or improve their performance—based on the data they consume. It allows computers to learn without being explicitly programmed for a specific task.
ML allows systems to uncover hidden patterns in data and adapt to new scenarios without code changes. This capability drives recommendation engines, fraud detection, and predictive maintenance. However, it introduces the risk of bias and model drift, requiring rigorous MLOps practices.
We advocate for "Data-Centric AI." The quality of the dataset matters more than the complexity of the algorithm. We treat data pipelines as first-class engineering artifacts, ensuring that the "food" we feed our models is clean, structured, and unbiased.