The article discusses the growing importance of a data-centric approach in machine learning operations (MLOps) tools for optimizing datasets. It highlights that while models have become standardized, datasets have not due to cultural inertia and lack of tooling. Data-centric AI focuses on improving the quality and diversity of training data rather than solely relying on advanced model architectures and algorithms. The article presents a list of MLOps tools that help teams get more out of their data in a systematic way, including Snorkel AI, CVAT, Clean Lab, SuperAnnotate, WhyLabs, Tecton, AWS SageMaker, YData, Synthetic Data Vault, Arize AI, Fiddler AI, Arthur, Algorithmia, Deepchecks, Galileo, Seldon, Pachyderm, DVC, Superb AI, Dolt Hub, Neptune AI, and Activeloop. The author also explains the concept of data-centric AI, its differences from model-centric AI, and why data quality is vital in this approach.