Machine learning is advancing rapidly, driving critical decisions across various business verticals. The number of machine learning engineers is growing at a rate of 70% per year, similar to the growth of application developers in the early 2010s. Today, we are hitting an inflection point in machine learning, with validation and practical applicability improving. However, data quality remains a significant challenge, with companies generating vast amounts of data that is often irrelevant or of poor quality. This leads to model downtimes, regressed model behavior, and bad-quality predictions. To address these issues, it's essential to focus on curating high-quality data, using techniques such as embeddings, clustering, and feature engineering to identify and fix noise in datasets. By doing so, machine learning models can be built that are powerful, reliable, and run continuously in various ecosystems.