Which of the following machine learning model deployment paradigms is the most common for machine learning projects?
Which of the following machine learning model deployment paradigms is the most common for machine learning projects?
Batch processing is the most common deployment paradigm for machine learning projects. This approach involves processing data in large groups or batches at periodic intervals, rather than in real-time. It is widely used because it can handle large volumes of data efficiently and is suitable for many typical machine learning tasks, such as training and predictions based on historical data.
D is correct. According to the training provided by Databricks "80-90% of deployments are Batch"
The most widely used deployment paradigm for machine learning projects is typically batch.