@Marvin what is the best way to deploy flows? Should I use .deploy() python syntax or should I use the prefect.yaml with prefect deploy cli commands?

In Prefect 2.x, you have multiple options for deploying your flows, and the best way to deploy them often depends on your specific use case and preferences. 1. Using the

.deploy()

Method in Python: Prefect 2.x supports the ability to deploy flows directly from Python code using the

.deploy()

method. This method allows you to programmatically create deployment configurations and deploy them to the server. 2. Using

prefect.yaml

with Prefect CLI Commands: Alternatively, Prefect 2.x also supports the use of

prefect.yaml

files in combination with the Prefect CLI. The

prefect.yaml

file contains the deployment configuration, and you can use the

prefect deploy

CLI command to deploy your flows. This approach can be particularly convenient if you are looking to manage deployment configurations as code and if you're using Kubernetes, as the YAML format aligns with Kubernetes object definitions. In choosing between these two methods, consider the following: - If you prefer to manage deployment configurations as code and possibly integrate them into your CI/CD pipelines, using

prefect.yaml

files with the Prefect CLI might be more suitable. - If you like to configure and deploy flows directly from your Python code or Jupyter notebooks, using the

.deploy()

method would be more applicable. It's also important to note that in Prefect 2.x flow pre-registration is no longer required; you can build deployments and send them directly to the server using either the CLI command or Python scripting. This streamlines the deployment process compared to Prefect 1.x. Whichever method you choose, ensure it aligns with your workflows and organizational practices, as both methods are supported and can effectively deploy Prefect 2.x flows.

pretty solid answer imo