In addition to algorithms, data is fundamental to AI systems. Without consistent data, AI projects can frustrate expectations and underperform or fail. For models to learn refined, meaningful, and unbiased patterns, they must be trained on masses of high-quality data instances that can be representative of real-world scenarios.
Therefore, in this module-structured training program, we approach crowdsourcing as a comprehensive data collection method that can be used to interpret, evaluate, and debug machine learning models and pipelines.