The cleanlab library has evolved from its original purpose as a proof-of-concept for utilizing ML models to discover mislabeled data, to become an industry-grade library that handles label errors in various ML tasks such as entity recognition, image/document tagging, and data labeled by multiple annotators. The latest release, cleanlab 2.3, introduces several new features including active learning with ActiveLab, which automatically answers the question of which new data should be labeled or which existing labels should be checked again, KerasWrapper for TensorFlow/Keras models, and improved computational efficiency for detecting label issues. These advancements aim to provide functionalities needed to practice data-centric AI, enabling users to improve their data and train better ML models with minimal labeling effort.