Building the future of ML

Machine learning (ML) is at a pivotal point as its potential is widely recognized, yet the development processes often lag behind, resembling the early, error-prone days of software development. Engineers typically manage datasets manually, using tools like Jupyter Notebooks, which hampers collaboration and leads to errors in ML models that can result in significant negative consequences, such as biased or unethical outputs. Error analysis is crucial for identifying and addressing these issues proactively rather than reactively, involving a systematic approach to understanding model failures. Organizations are encouraged to advance from basic error analysis practices (L0) to more sophisticated ones (L4) by integrating activities such as global and local explanations, adversarial analysis, and data unit testing. Openlayer aims to facilitate this transition by offering tools that centralize error analysis within the ML development pipeline, thereby enhancing model quality and performance through systematic error assessment and informed data collection strategies.