Traditional Learning Traditional machine learning model development is resource-intensive, requiring significant domain knowledge and time to produce and compare dozens of models. These models are traditional developed using Python, or R, and require non-trivial programming skill. This is a major barrier to broad adoption
Automated Machine Learning or AutoML, is the process of automating the time consuming, iterative tasks of cleaning data, building and tuning machine learning models. It allows data scientists, analysts, and developers to build ML models with high scale, efficiency, and productivity all while sustaining model quality.
