The technology commonly used to implement machine learning workflows includes programming frameworks such as TensorFlow, PyTorch, and Scikit-learn. These tools provide libraries, algorithms, and pre-built components that facilitate the development, training, and deployment of machine learning models. They streamline the end-to-end process, from data preprocessing to model evaluation, making it easier for developers and data scientists to build and optimize their solutions. Additionally, they support various applications, ranging from predictive analytics to artificial intelligence, expanding the scope of machine learning in different industries.