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Merged
eedorenko merged 8 commits into from
Dec 16, 2019
Merged

Deploy to aci fix #126

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bea4657
Take the latest model for the scoring image
Dec 12, 2019
6b81161
revert back
Dec 12, 2019
80764d1
test alpha
Dec 12, 2019
ad10a5c
Correct package version
Dec 13, 2019
a9448e2
revert alpha
Dec 13, 2019
7c2cb0c
Merge branch 'master' into eedorenko/deploy-to-aci-fix
eedorenko Dec 13, 2019
99b8ad9
doc updatye
Dec 13, 2019
216846d
Merge branch 'eedorenko/deploy-to-aci-fix' of https://github.com/micr…
Dec 13, 2019
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2 changes: 1 addition & 1 deletion code/scoring/conda_dependencies.yml
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Expand Up @@ -28,7 +28,7 @@ dependencies:
- azureml-model-management-sdk==1.0.1b6.post1
- azureml-sdk==1.0.74
- scipy==1.3.1
- scikit-learn==0.21.3
- scikit-learn==0.22
- pandas==0.25.3
- numpy==1.17.3
- joblib==0.14.0
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2 changes: 2 additions & 0 deletions docs/getting_started.md
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Expand Up @@ -182,6 +182,8 @@ specified).

* The second stage of the pipeline, **Train model**, triggers the run of the ML Training Pipeline. The training pipeline will train, evaluate, and register a new model. The actual computation is performed in an [Azure Machine Learning Compute cluster](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute). In Azure DevOps, this stage runs an agentless job that waits for the completion of the Azure ML job, so it can wait for training completion for hours or even days without using agent resources.

**Note:** If the model evaluation determines that the new model does not perform better than the previous one then the new model will not be registered and the pipeline will be cancelled.

* The third stage of the pipeline, **Deploy to ACI**, deploys the model to the QA environment in [Azure Container Instances](https://azure.microsoft.com/en-us/services/container-instances/). It then runs a *smoke test* to validate the deployment, i.e. sends a sample query to the scoring web service and verifies that it returns a response in the expected format.

Wait until the pipeline finished and make sure there is a new model in the **ML Workspace**:
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