Cleanlab's open-source library is a popular software framework for practicing Data-Centric AI, which automatically detects various common issues in datasets such as label errors, outliers, near duplicates, and drift. The library can be used with almost any type of data, including images, text, tables, and audio. It provides flexible functionality that allows users to decide how to improve their dataset and model based on the outputs from its algorithms. Cleanlab Studio, a no-code platform, builds upon this foundation by automating most of the hard parts of turning raw data into reliable ML or Analytics, including labeling, training baseline models, diagnosing and correcting data issues, identifying the best ML model for your data, and deploying it to serve predictions in business applications. The platform offers an intuitive interface that allows users to employ just the right amount of automation to produce high-quality data quickly. Cleanlab software aims to automate much of the 80% of time spent discovering and correcting data issues in ML projects, enabling companies to achieve faster model deployment and business impact via ML on auto-corrected data. The future of AI is Data-Centric, with Cleanlab software playing a crucial role in improving reliability and efficiency in machine learning applications.