Continuous Machine Learning: Continuous Machine Learning refers to an iterative and ongoing process of training and updating machine learning models using newly available data without requiring a complete retraining from scratch. It involves incorporating new data into existing models to continuously improve their accuracy and performance over time.

Continuous Learning Machine Learning: Continuous Learning Machine Learning is a similar concept to Continuous Machine Learning, emphasizing the ability of machine learning models to learn continuously from new data and adapt to changing environments or patterns. It highlights the dynamic nature of machine learning models, which can evolve and improve with each new learning iteration.

Model Delivery: Model Delivery refers to the process of deploying or delivering machine learning models into production or operational systems. It involves making trained models accessible and available for real-time predictions or decision-making, enabling them to provide value in live environments.

Continuous Machine Learning Models: Continuous Machine Learning Models are machine learning models that are designed to be continuously updated or refined based on new data. These models are capable of incorporating incoming data seamlessly into their existing knowledge to adapt and provide more accurate predictions or insights over time.

Continuous machine learning enables organizations to leverage the latest data to keep their models up to date and responsive to changing patterns or trends. It supports the idea of learning from ongoing experiences and adapting models in real-time, allowing businesses to make informed decisions and predictions that align with the most recent data available.