A 378-page book teaching practical approaches to building ML models by prioritizing data quality over algorithm complexity, covering data collection, cleaning, labeling, and synthetic data generation with Python. Published February 2024, it emphasizes responsible AI and the role of subject-matter experts in model development.

• Shifts focus from algorithm optimization to data quality as the foundation for robust, fair, and interpretable ML models
• Covers concrete techniques: data imputation, cleaning, labeling, augmentation, and synthetic data generation with scikit-learn code examples
• Introduces "small data" concept and strategies for handling missing data, addressing bias, and building ethical AI systems

A review of "Data-Centric Machine Learning with Python" arguing that ML success depends more on data quality than model sophistication, and explaining why this approach has become standard practice. The reviewer extends this to show how LLMs fit into data-centric pipelines as both label generators and sources of weak supervision.

• The shift to data-centric ML happened years ago through landmark projects like Netflix Prize, ImageNet, and Word2Vec—not a recent invention—driven by the internet's planet-scale data generation.
• Data preparation is the real bottleneck in modern ML: cleaning, imputing missing values, extracting features, removing bias, and iteratively refining labels through human-in-the-loop active learning takes far more effort than training.
• LLMs can be treated as replacements for human subject matter experts in labeling and weak supervision, which means the techniques for managing SME bias and inconsistency now apply directly to LLM outputs.