There are 3 ways you can go about implementing MLOps Course:

1. MLOps level 0 (Manual process)
2. MLOps level 1 (ML pipeline automation)
3. MLOps level 2 (CI/CD pipeline automation)

MLOps level 0
This really is typical for companies that are only starting out with ML. A completely manual ML workflow and the data-scientist-driven process might be enough if your models are hardly ever changed or trained.
Characteristics
a. Manual, script-driven, and interactive process
b. The disconnect between ML and operations
c. Infrequent release iterations
d. No Continuous Integration (CI)
e. No Continuous Deployment (CD)

MLOps level 1
The purpose of MLOps level just one is to perform continuous training (CT) of the model by automating the ML pipeline. In this specific way, you achieve continuous delivery of model prediction service.

This scenario may be helpful for solutions that operate in a constantly changing environment and need to proactively address shifts in customer behavior, price rates, and other indicators.
Characteristics
a. Rapid experiment
b. CT of the model in production
c. Experimental-operational symmetry
d. Modularized code for components and pipelines
e. Continuous delivery of models
f. Pipeline deployment

MLOps level 2
For the rapid and reliable update of pipelines in production, you desire a robust computerized CI/CD system. On this automated CI/CD system, your data experts rapidly explore new ideas around feature engineering, model buildings, and hyperparameters.

This specific level fits tech-driven companies who have to retrain their models daily, if not hourly, update them in minutes, and redeploy on 1000s of servers simultaneously. Using no end-to-end MLOps cycle, such organizations just won’t make it through.
This MLOps setup includes the following components:

a. Source control
b. Test and build services
c. Deployment services
d. Model registry
e. Feature store
f. ML metadata store
g. ML pipeline orchestrator.

You can also visit my YouTube Link: https://youtu.be/e5vFMUMOB_0