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MLflow MLOps Architecture - Technical Perspective

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MLflow MLOps Architecture

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Andre Mesarovic 27 December 2023 MLflow MLOps Architecture A Technical Perspective
Peeling Back the MLflow MLOps Tech Stack Onion
The Many Meanings of a Model
Model is an overloaded term with several meanings in MLflow: ● Native model - simply the native flavor’s serialized format. For sklearn it’s a pickle file, for TensorFlow it’s a directory with SaveModel format files. ● MLflow model - wrapper around the native model with metadata in the MLmodel file and environment information in conda.yaml and requirements.txt files. Lives in an MLflow run. ● Model version ○ Wrapper around an MLflow model of which there are two: ■ The source run's MLflow model used for UI and lineage purposes - lives in workspace. ■ A copy of the run's MLflow model that lives in model registry storage. For UC it lives in UC storage, for non-UC it lives in the workspace locked-down DBFS. ● Registered model - a bucket of model versions. See MLflow documentation page Components of a Model in MLflow Meanings of a Model
MLflow Object Relationships - Overview ● Model Version - points to a Run ● Run points to: ○ MLflow Model - contains: ■ Native Model ■ Metadata in MLmodel file ■ Python library dependencies ○ Notebook code: ■ Notebook revision in workspace ■ Git link (if applicable) ○ Delta Table - Captured in several ways: ■ Run.inputs aka mlflow.data ■ Run tag sparkDatasourceInfo (undocumented)
MLflow Object Relationships - Details ● Native Model- native model’s serialized format, e.g. pickle file ● MLflow Model - metadata wrapper around native model ● Run - Contains MLflow model + params and metrics ● ModelVersion- deployable unit in registry pointingto an MLflow model ● RegisteredModel - bucketof model versions ● Experiment - bucket of runs
MLflow Object Relationships - Class Diagram ● An experiment can have 0 or more runs ● A run can belong to only one experiment ● A run can have 0 or more MLflow models ● A model version points to one run ● A run can be linked to one or more model versions ● A registered model can 0 or more model versions ● A model version can belong to only one registered model
MLOps Pipeline - Model First - Unity Catalog Workflowsteps 1. Dev environment 1a. Train model 1b. Register best model in dev catalog 1b. Copy MLflow run to staging workspace 2. Staging environment 2a. Run model evaluation and non-ML code tests 2b. Copy (promote) model version to staging catalog 2c. Copy model version to prod catalog when ready 2c. Copy MLflow run to prod workspace for lineage and governance 3. Prod environment - run model inference ondata
Copy Model Version - Unity Catalog Registry
Copy Model Version - Workspace Registry
Github ● Python script: copy-model-version ○ https://github.com/mlflow/mlflow-export-import/blob/master/README_copy.md#copy-model-version ● Notebook: Copy_Model_Version ○ https://github.com/mlflow/mlflow-export-import/blob/master/databricks_notebooks/copy/Copy_Model_Version.py e2-demo-west workspace ● Notebook: Copy_Model_Version Copy Model Version Code
● Copies a model version and its run (optional). ● Two types of model version copy: ○ Shallow copy - does not copy the source version's run. The destination model version will point to the source version's run. ○ Deep copy - the destination model version will point to a new copy of the run in the destination workspace. ■ Recommended for full governance and lineage tracking. ● Supports both the WS registry and UC registry copying including WS to UC copying. ● For WS registry, the destination model version can be either in the same workspace or in another workspace. ● For UC registry, the destination model version can be either in the same UC metastore or in another UC metastore. ● Databricks registry URIs should be Databricks profiles. ● Note MLflow 2.8.0 introduced MlflowClient.copy_model_version. However it is only a shallow copy and does not work across external workspaces or UC metastores. ● Source: ○ Copy_Model_Version.py - Python script ○ Copy_Model_Version - Databricks notebook Copy Model Version Details
Copy Model Version - Script - Examples copy-model-version --src-model dev.models.sklearn_wine --src-version 1 --dst-model prod.models.sklearn_wine --dst-experiment-name /Users/first.last@mycompany.com/My_Experiment --src-registry-uri: databricks-uc://test-env --dst-registry-uri: databricks-uc://test-env Copy UC model version in the same UC metastore Copy UC model version to another UC metastore copy-model-version --src-model dev.models.sklearn_wine --src-version 1 --dst-model prod.models.sklearn_wine --dst-experiment-name /Users/first.last@mycompany.com/My_Experiment --src-registry-uri: databricks-uc://test-env --dst-registry-uri: databricks-uc://prod-env
Copy Model Version - Notebook - Example

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MLflow MLOps Architecture - Technical Perspective

  • 1. Andre Mesarovic 27 December 2023 MLflow MLOps Architecture A Technical Perspective
  • 2. Peeling Back the MLflow MLOps Tech Stack Onion
  • 3. The Many Meanings of a Model
  • 4. Model is an overloaded term with several meanings in MLflow: ● Native model - simply the native flavor’s serialized format. For sklearn it’s a pickle file, for TensorFlow it’s a directory with SaveModel format files. ● MLflow model - wrapper around the native model with metadata in the MLmodel file and environment information in conda.yaml and requirements.txt files. Lives in an MLflow run. ● Model version ○ Wrapper around an MLflow model of which there are two: ■ The source run's MLflow model used for UI and lineage purposes - lives in workspace. ■ A copy of the run's MLflow model that lives in model registry storage. For UC it lives in UC storage, for non-UC it lives in the workspace locked-down DBFS. ● Registered model - a bucket of model versions. See MLflow documentation page Components of a Model in MLflow Meanings of a Model
  • 5. MLflow Object Relationships - Overview ● Model Version - points to a Run ● Run points to: ○ MLflow Model - contains: ■ Native Model ■ Metadata in MLmodel file ■ Python library dependencies ○ Notebook code: ■ Notebook revision in workspace ■ Git link (if applicable) ○ Delta Table - Captured in several ways: ■ Run.inputs aka mlflow.data ■ Run tag sparkDatasourceInfo (undocumented)
  • 6. MLflow Object Relationships - Details ● Native Model- native model’s serialized format, e.g. pickle file ● MLflow Model - metadata wrapper around native model ● Run - Contains MLflow model + params and metrics ● ModelVersion- deployable unit in registry pointingto an MLflow model ● RegisteredModel - bucketof model versions ● Experiment - bucket of runs
  • 7. MLflow Object Relationships - Class Diagram ● An experiment can have 0 or more runs ● A run can belong to only one experiment ● A run can have 0 or more MLflow models ● A model version points to one run ● A run can be linked to one or more model versions ● A registered model can 0 or more model versions ● A model version can belong to only one registered model
  • 8. MLOps Pipeline - Model First - Unity Catalog Workflowsteps 1. Dev environment 1a. Train model 1b. Register best model in dev catalog 1b. Copy MLflow run to staging workspace 2. Staging environment 2a. Run model evaluation and non-ML code tests 2b. Copy (promote) model version to staging catalog 2c. Copy model version to prod catalog when ready 2c. Copy MLflow run to prod workspace for lineage and governance 3. Prod environment - run model inference ondata
  • 9. Copy Model Version - Unity Catalog Registry
  • 10. Copy Model Version - Workspace Registry
  • 11. Github ● Python script: copy-model-version ○ https://github.com/mlflow/mlflow-export-import/blob/master/README_copy.md#copy-model-version ● Notebook: Copy_Model_Version ○ https://github.com/mlflow/mlflow-export-import/blob/master/databricks_notebooks/copy/Copy_Model_Version.py e2-demo-west workspace ● Notebook: Copy_Model_Version Copy Model Version Code
  • 12. ● Copies a model version and its run (optional). ● Two types of model version copy: ○ Shallow copy - does not copy the source version's run. The destination model version will point to the source version's run. ○ Deep copy - the destination model version will point to a new copy of the run in the destination workspace. ■ Recommended for full governance and lineage tracking. ● Supports both the WS registry and UC registry copying including WS to UC copying. ● For WS registry, the destination model version can be either in the same workspace or in another workspace. ● For UC registry, the destination model version can be either in the same UC metastore or in another UC metastore. ● Databricks registry URIs should be Databricks profiles. ● Note MLflow 2.8.0 introduced MlflowClient.copy_model_version. However it is only a shallow copy and does not work across external workspaces or UC metastores. ● Source: ○ Copy_Model_Version.py - Python script ○ Copy_Model_Version - Databricks notebook Copy Model Version Details
  • 13. Copy Model Version - Script - Examples copy-model-version --src-model dev.models.sklearn_wine --src-version 1 --dst-model prod.models.sklearn_wine --dst-experiment-name /Users/first.last@mycompany.com/My_Experiment --src-registry-uri: databricks-uc://test-env --dst-registry-uri: databricks-uc://test-env Copy UC model version in the same UC metastore Copy UC model version to another UC metastore copy-model-version --src-model dev.models.sklearn_wine --src-version 1 --dst-model prod.models.sklearn_wine --dst-experiment-name /Users/first.last@mycompany.com/My_Experiment --src-registry-uri: databricks-uc://test-env --dst-registry-uri: databricks-uc://prod-env
  • 14. Copy Model Version - Notebook - Example

Editor's Notes

  1. what are they trying to do? recommendation? content curation? how does that work? How come Delta and Spark and those things can help with that thing (recommendation, or whatever they do)?
  2. What did they do with us? what are they trying to do? recommendation? content curation? how does that work? How come Delta and Spark and those things can help with that thing (recommendation, or whatever they do)?
  3. What did they do with us? what are they trying to do? recommendation? content curation? how does that work? How come Delta and Spark and those things can help with that thing (recommendation, or whatever they do)?
  4. what are they trying to do? recommendation? content curation? how does that work? How come Delta and Spark and those things can help with that thing (recommendation, or whatever they do)?
  5. what are they trying to do? recommendation? content curation? how does that work? How come Delta and Spark and those things can help with that thing (recommendation, or whatever they do)?
  6. what are they trying to do? recommendation? content curation? how does that work? How come Delta and Spark and those things can help with that thing (recommendation, or whatever they do)?
  7. what are they trying to do? recommendation? content curation? how does that work? How come Delta and Spark and those things can help with that thing (recommendation, or whatever they do)?
  8. what are they trying to do? recommendation? content curation? how does that work? How come Delta and Spark and those things can help with that thing (recommendation, or whatever they do)?
  9. what are they trying to do? recommendation? content curation? how does that work? How come Delta and Spark and those things can help with that thing (recommendation, or whatever they do)?
  10. What did they do with us? what are they trying to do? recommendation? content curation? how does that work? How come Delta and Spark and those things can help with that thing (recommendation, or whatever they do)?
  11. What did they do with us? what are they trying to do? recommendation? content curation? how does that work? How come Delta and Spark and those things can help with that thing (recommendation, or whatever they do)?
  12. What did they do with us? what are they trying to do? recommendation? content curation? how does that work? How come Delta and Spark and those things can help with that thing (recommendation, or whatever they do)?
  13. what are they trying to do? recommendation? content curation? how does that work? How come Delta and Spark and those things can help with that thing (recommendation, or whatever they do)?