getstartednotebook_quickstart#3812
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Gnanasundaramsampath-sys wants to merge 7 commits intomainfrom
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…r 502 error - pip<=21.3.1 (2021) is incompatible with mlflow==2.17.0 (2024); updated to pip>=23.0 - pandas==1.5.3 is too old relative to scikit-learn==1.5.2 and mlflow==2.17.0; updated to pandas>=2.0.0 - These mismatched versions caused the serving container liveness probe to fail with HTTP 502
Use MLflow default inferred environment for registered sklearn model. This avoids deployment-time container startup failures (liveness 502) caused by custom environment resolution/loading issues.
Three root-cause fixes: 1. Restore correct mlflow.sklearn.log_model() call - previous edit had introduced a literal backslash-n (\\n) making main.py unparseable Python, causing every training job to fail silently. 2. Add minimal conda_env to log_model so the MLflow model artifact carries only the required serving packages (mlflow>=2.0, scikit-learn==1.5.2, cloudpickle, numpy, pandas, psutil) with pip as the installer - avoids the large Azure ML training env being baked into the serving container. 3. Make create_job cell wait for the training job to complete via ml_client.jobs.stream() before proceeding to deployment. Without this, deployment picked up models from previous runs that carried the broken original conda_env (pip<=21.3.1), causing persistent 502 liveness failures.
…racking server The sklearn-1.5 curated environment ships MLflow 2.17 which introduced a new 'LoggedModel' entity requiring POST /api/2.0/mlflow/logged-models/create. Azure ML's MLflow tracking server does not implement this endpoint, causing: 'UserError: User process python exited with status code 1' 'rest_store.create_logged_model -> HTTP 404/not-implemented' Two fixes: 1. job environment_variables: MLFLOW_ENABLE_LOGGED_MODEL_CREATION=false Instructs MLflow 2.17 to skip the new LoggedModel creation path and use the legacy artifact-only log path compatible with Azure ML tracking. 2. mlflow.sklearn.autolog(log_models=False) Prevents autolog from also attempting to call _create_logged_model() during clf.fit(), which would fail the same way independently.
Training was still failing in cell 6 with: /api/2.0/mlflow/logged-models -> 404 from mlflow.sklearn.log_model in curated sklearn-1.5 env. Switch notebook training script to the same working repo pattern used by Responsible AI samples: - save sklearn model to a temp local directory - register using MlflowClient.create_registered_model/create_model_version with file:// source URI - keep explicit conda_env in the saved model artifact for deployment runtime This bypasses logged-models endpoint entirely while preserving model registry and downstream endpoint deployment flow.
… using unique model names Deployment was still failing with liveness 502 after training succeeded. Two hardening changes: 1) Stop passing custom conda_env when saving the model artifact used for registry (let mlflow infer default env from training environment to avoid dependency drift). 2) Use a unique registered model name per run (credit_defaults_model_<uuid>) to guarantee deployment uses the model created in the current CI run and cannot accidentally select stale/broken versions from prior runs. Also keeps prior logged-models workaround and job wait behavior.
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