Een workflow met meerdere stappen maken: Model Evaluation

In deze oefening maak je de Model Evaluation-stap van onze meerstapsworkflow om een deel van de ML-lifecycle te beheren. Je gebruikt de run()-methode uit de MLflow Projects-module en stelt het entrypoint in op model_evaluation. Vervolgens neem je de model_engineering_run_id als parameter, die als output in de vorige oefening is gegenereerd, en geef je die door aan het commando.

Het MLproject dat in de vorige stap is gemaakt, is beschikbaar in de IPython-shell met print(MLproject).

De mlflow-module is geïmporteerd.

Deze oefening maakt deel uit van de cursus

Introductie tot MLflow

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Oefeninstructies

Ken de run()-methode uit de MLflow Projects-module toe aan model_evaluation.
Stel het entrypoint-argument in op "model_evaluation".
Stel een parameter in met de naam "run_id" met de waarde model_engineering_run_id.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Set the MLflow Projects run method
model_evaluation = ____.____.____(
    uri="./",
    # Set the entry point to model_evaluation
    ____="____",
  	# Set the parameter run_id to the run_id output of previous step
    parameters={
        "____": ____,
    },
    env_manager="local"
)

print(model_evaluation.get_status())

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: Introduction to MLflow Model Registry Exercise 2: Create a Model Exercise 3: Searching Models Exercise 4: Registering Models Exercise 5: Registering existing models Exercise 6: Registering new models Exercise 7: Model stages Exercise 8: Model stages in Model Registry Exercise 9: Transitioning model stages Exercise 10: Model deployment Exercise 11: Serving models from the Model Registry Exercise 12: Loading models from the 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: Introductie tot MLflow Projects Exercise 2: Structuur van een MLproject-bestand Exercise 3: Een MLproject maken Exercise 4: MLflow Projects uitvoeren Exercise 5: MLflow run-opdracht Exercise 6: Module MLflow projects Exercise 7: Parameters opgeven Exercise 8: Parameters toevoegen aan MLproject Exercise 9: Parameters toevoegen aan project-run Exercise 10: Workflows Exercise 11: Een MLproject maken voor de ML-levenscyclus: Model Engineering Exercise 12: Een MLproject maken voor de ML-levenscyclus: Modelbeoordeling Exercise 13: Een workflow met meerdere stappen maken: Model engineering Exercise 14: Een workflow met meerdere stappen maken: Model Evaluation

Huidige oefening

Exercise 15: Afronding