Understanding ML operations and CI/CD
In the ML lifecycle, there are many steps that require a skilled data scientist's hands-on interaction throughout, such as wrangling the dataset, training, and evaluating a model. These manual steps could affect an ML team's operations and speed to deploy models in production. Imagine your model training job takes a long time and finishes in the middle of the night. You either have to wait for your first data scientist to come in during the day to evaluate the model and deploy the model into production or have to employ an on-call rotation to have someone on standby at all times to monitor the model training and deployment. But neither option is ideal if you want an effective and efficient ML lifecycle.
Machine Learning Operations (MLOps) is critical to a team that wants to stay lean and scale well. MLOps helps you streamline and reduce manual human intervention as much as possible. It helps transform your ML lifecycle to enterprise...
