Usually, an ML algorithm needs clean data to detect some patterns in the data and make predictions over a new dataset. However, in real-world applications, the data is often not ready to be directly fed into an ML algorithm. Similarly, the output from an ML model is just numbers or characters that need to be processed for performing some actions in the real world. To accomplish that, the ML model has to be deployed in a production environment. This entire framework of converting raw data to usable information is performed using a ML pipeline.
The following is a high-level illustration of an ML pipeline:
We will break down the blocks illustrated in the preceding figure as follows:
- Data Ingestion: It is the process of obtaining data and importing data for use. Data can be sourced from multiple systems, such as Enterprise Resource Planning...
