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1. Set up your Cloud Environment 2. Create a new custom container artifact for running the pipeline components 3. Create a new custom container artifact for running the training job itself 4. Create a Jupyter notebook outlining the Pipeline flow & components 5. Compile a YAML file from a Vertex AI workbench and store the precompiled YAML file under a GCS bucket path

1. Set up your Cloud Environment 2. Create a cloud function that gets triggered every time the jobID is published on a predefinied topic (from 1st part) 3. Within the cloud function, the python code subscribes to the topic and triggers a Vertex AI pipeline job using the precomiled YAML file (from 2nd part) 4. The pipeline jobs finetunes the stable diffusion model using Dreambooth, uploads the new custom model to Model registy & deploys an endpoint 5. The job also updates Firestore with the status of the pipeline job from start to end