The concept of Automated Machine Learning has gained much traction recently. Automated Machine Learning, also known as AutoML, is the process of automating the end-to-end process of applying machine learning to real-world problems. A typical machine learning process consists of several steps: ingesting and preprocessing data, feature engineering, model training, and deployment. In conventional machine learning, every step in this pipeline is monitored and executed by humans. Tools for automatic machine learning (AutoML) aim to automate one or more stages of these machine learning pipelines making it easier for non-experts to build machine learning models while removing repetitive tasks and enabling seasoned machine learning engineers to build better models faster.
Artificial Intelligence (AI) has the potential to make a big difference in every facet of our lives, especially in areas like healthcare, education, and environmental conservation. However, many enterprises still struggle to deploy machine learning solutions effectively. It is primarily due to the issues of talent, time, and trust, which affect these businesses.