Comments (2)
Two versions of the pipeline present
from mlops-on-gcp.
Related Issues (20)
- Bad argument name in "chicago_taxi_dag.py"
- TFX on Cloud AI Platform Pipelines: "Activate Cloud Shell" unnecessary HOT 1
- TFX on Cloud AI Platform Pipelines: GKE cluster not provisioned on lab startup HOT 1
- TFX on Cloud AI Platform Pipelines: kfp not installed by default
- TFX on Cloud AI Platform Pipelines: Bucket not created by default HOT 2
- TFX on Cloud AI Platform Pipelines: Skaffold not deployed on Notebooks instance
- TFX on Cloud AI Platform Pipelines: TFX pipeline run cannot write into defined bucket (403 Insufficient Permission)
- Missing `covertype_training_pipeline.py`
- MLFLOW_TRACKING_EXTERNAL_URI is empty. It seems MLFlow Tracking Server is not properly deployed HOT 8
- ml-ops examples do not work
- mlops-composer-mlflow/install.sh old composer version issue
- Missing CS dataset
- build HOT 1
- Something
- `dag` parameter not required because the operator is in the DAG context already
- [TFX Standard Components Walkthrough] Outdated library dependencies HOT 4
- Typos in workshops/tfx-caip-tf23/lab-01-tfx-walkthrough
- workshops/tfx-caip-tf23/lab-02-tfx-pipeline lab's last two checkpoints cannot be satisfied HOT 1
- `Pusher.png` mistakenly labeled as "InfraValidator"
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from mlops-on-gcp.