Package: mlops-utilities
IntroInstallationUser GuideConcepts[NOT IMPLEMENTED] The simplest caseYou prepared / Project Structure:Library usage:[NOT IMPLEMENTED] The "simple" layoutThe "default" layoutYou prepared / Project Structure:Structure of Pipeline Definition ScriptComponent Structure and EnvironmentsLibrary usage:IntroMLOps Utilities provides:Implementation of high level operations most commonly occuring in workflows for production-ready ML models:Dataset versioningBuilding of training pipeline from initial code sources: Jupyter notebooks, python modules, etc.Training pipeline deploymentScheduling its execution on timely or event-triggered execution, e.g., new dataset versionZero-config lineage trackingZero-config model versioning and model registryModel packaging and deploymentModel endpoint managementData quality monitoring setupModel quality monitoring setupIn AWS cloud.Installationpip install mlops-utilitiesUser GuideConceptsThis library simplifies MLOps workflow implementation by greatly reducing amount of boilerplate code and configuration required. It does so by relying on specific conventions for project structure  described below.Use cases are sorted by increasing complexity.[NOT IMPLEMENTED] The simplest caseYou made a single Jupyter notebook that:takes as input a training dataset locationpreprocess data using Pandastrain model using scikit-learnevaluate model using scikit-learnuses one of the predefined kernels of Sagemaker Studio as an execution environment.You have not changed this environment withpip installs. If you did then check the next use case.You prepared / Project Structure:<PROJECT_ROOT>
  |-- my_project07.ipynbLibrary usage:To build and deploy pipeline (in SageMaker) use the following CLI command:mlops upsert-pipeline TODO header of help description for this commandor from code:frommlops_utilities.actionsimportupsert_pipeline...TODOupsert_pipeline(TODOargsexample)To execute the previously upserted pipeline:mlops run-pipeline TODOTraining pipeline execution produces new model version in model registry. To deploy it onto real-time endpoint use the following CLI command:mlops deploy-model TODO[NOT IMPLEMENTED] The "simple" layoutTODO - The same as default layout but without writing pipeline definition using SageMaker SDK.The "default" layoutIf you separated code of different pipeline steps and defined training pipeline using SageMaker SDK.You prepared / Project Structure:<PROJECT_ROOT>
  |-- components
          |-- preprocessing
                  |-- preprocess.py
                  |__ requirements.txt
          |-- training
                  |__ train.py
          |__ <folders for other steps>
  |-- pipelines
          |-- training_pipeline.py
          |-- training_pipeline.defaults.conf
          |-- inference_pipeline.py
          |__ inference_pipeline.defaults.confStructure of Pipeline Definition ScriptTODOComponent Structure and EnvironmentsTODOLibrary usage:To build and deploy pipeline (in SageMaker) use the following CLI command:mlops upsert-pipeline TODO header of help description for this commandor from code:frommlops_utilities.actionsimportupsert_pipeline...TODOupsert_pipeline(TODOargsexample)To execute the previously upserted pipeline:mlops run-pipeline TODOTraining pipeline execution produces new model version in model registry. To deploy it onto real-time endpoint use the following CLI command:mlops deploy-model TODO