Modelstages wijzigen

Zodra modellen zijn geregistreerd in de Model Registry, kan elke modelversie aan een stage worden toegewezen. Stages worden gebruikt om de levenscyclus van het model te beheren en staan voor verschillende ontwikkelomgevingen.

In deze oefening ga je met de mlflow client-class als client je "Insurance"-modellen door de verschillende stages in je ontwikkelcyclus zetten. Eerst zet je je meest stabiele model klaar voor productie. Daarna zet je een model in de stage voor testen en evalueren. Tot slot archiveer je een model dat niet meer nodig is.

De mlflow client-module wordt geïmporteerd.

Deze oefening maakt deel uit van de cursus

Introductie tot MLflow

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Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Transition version 2 of Insurance model to stable stage
client.____(____="____", ____=____,
        ____="____"
    )

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Introductie tot MLflow

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In this Chapter, you will be introduced to MLflow and how it aims to assist with some difficulties of the Machine Learning lifecycle. You will be introduced to the four main concepts that make up MLflow with a main focus on MLflow Tracking. You will learn to create experiments and runs as well as how to track metrics, parameters, and artifacts. Finally, you will search MLflow programmatically to find experiment runs that fit certain criteria.

Exercise 1: What is MLflow?Exercise 2: Components of MLflow Exercise 3: MLflow experiments Exercise 4: MLflow Tracking Exercise 5: Starting a run Exercise 6: Logging a run Exercise 7: How to retrieve active run data?Exercise 8: Querying runs Exercise 9: Search runs query options Exercise 10: Searching runs

In this Chapter, you will be introduced to MLflow Models. The MLflow Models component of MLflow plays an essential role in the Model Evaluation and Model Engineering steps of the Machine Learning lifecycle. You will learn how MLflow Models standardizes the packaging of ML models as well as how to save, log and load them. You will learn how to create custom MLflow Models to provide more flexibility to your use cases as well as how to evaluate model performance. You will utilize the powerful concept of “Flavors” and finally use the MLflow command line tool for model deployment.

Exercise 1: Introduction to MLflow Models Exercise 2: Package a machine learning model Exercise 3: Storage Format Exercise 4: What's in an MLmodel file?Exercise 5: Model API Exercise 6: Saving and loading a model Exercise 7: Logging and loading a model Exercise 8: Custom models Exercise 9: Creating a custom Python Class Exercise 10: Custom scikit-learn model Exercise 11: Scikit-learn flavor and evaluation Exercise 12: Model serving Exercise 13: Serving a model Exercise 14: Score from a served model

This Chapter introduces the concept of MLflow called the Model Registry. You will be introduced to how the Model Registry is used to manage the lifecycle of ML models. You will learn how to create and search for models in the Model Registry. You then learn how to register models to the Model Registry and learn how to transition models between predefined stages. Finally, you will also learn how to deploy models from the Model Registry.

Exercise 1: Introductie tot de MLflow Model Registry Exercise 2: Maak een model Exercise 3: Modellen zoeken Exercise 4: Modellen registreren Exercise 5: Bestaande modellen registreren Exercise 6: Nieuwe modellen registreren Exercise 7: Modelstadia Exercise 8: Modelfases in de Model Registry Exercise 9: Modelstages wijzigen

Huidige oefening

Exercise 10: Modelimplementatie Exercise 11: Modellen serveren vanuit de Model Registry Exercise 12: Modellen laden uit de Model Registry

In this chapter, you'll gain valuable knowledge on how to streamline your data science code for reusability and reproducibility using MLflow Projects. The chapter begins by introducing the concept of MLflow Projects and walking you through creating an MLproject file. From there, you'll learn how to run MLflow Projects through both the command-line and the MLflow Projects module while also discovering the power of using parameters for added flexibility in your code. Finally, you will learn how to manage steps of the machine learning lifecycle by creating a multi-step workflow using MLflow Projects.

Exercise 1: Introduction to MLflow Projects Exercise 2: MLproject file layout Exercise 3: Creating an MLproject Exercise 4: Running MLflow Projects Exercise 5: MLflow run command Exercise 6: MLflow projects module Exercise 7: Specifying parameters Exercise 8: Adding parameters to MLproject Exercise 9: Adding parameters to project run Exercise 10: Workflows Exercise 11: Creating an MLproject for the ML Lifecycle: Model Engineering Exercise 12: Creating an MLproject for the ML Lifecycle: Model Evaluation Exercise 13: Creating a multi-step workflow: Model Engineering Exercise 14: Creating a multi-step workflow: Model Evaluation Exercise 15: Wrap-up