Machine learning
In the machine learning lifecycle, two distinct phases exist: development and training, and inference or operational.
During the development and training phase, the data scientist builds the machine learning model using training data. Numerous tests and algorithm refinements are performed to ensure its accuracy. In the subsequent inference phase, machine learning engineers convert the model into a pipeline for specific tasks.
Various cloud products and open source tools are used to develop and deploy the pipeline:
Use case
Use case
Use case