Een MLproject maken voor de ML-levenscyclus: Model Engineering

Het MLproject-bestand kan meer dan één entry point bevatten. Je kunt dus één MLproject-bestand gebruiken om meerdere entry points uit te voeren, waardoor je met één MLproject-bestand een workflow met meerdere stappen kunt draaien.

In deze oefening ga je het begin maken van een MLproject-bestand dat het entry point model_engineering bevat. Dit entry point voert een Python-script uit dat parameters accepteert die worden gebruikt als hyperparameterwaarden voor fit_intercept en n_jobs in een Logistic Regression-model. Dit model wordt gebruikt om het geslacht van een persoon te voorspellen op basis van een verzekeringsclaim.

Deze oefening maakt deel uit van de cursus

Introductie tot MLflow

Cursus bekijken

Oefeninstructies

Maak een entry point voor de Model Engineering-stap van de ML-levenscyclus met de naam model_engineering.
Stel de eerste entrypoint-parameter in op n_jobs en de tweede op fit_intercept.
Plaats de parameters binnen het command.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

"""
name: insurance_model
python_env: python_env.yaml
entry_points:
  # Set the entry point
  ____:
    parameters: 
      # Set n_jobs 
      ____:
        type: int
        default: 1
      # Set fit_intercept
      ____:
        type: bool
        default: True
    # Pass the parameters to the command
    command: "python3.9 train_model.py {____} {____}"
"""

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Introductie tot MLflow

SkillTag.level.advancedSkillTag.label

4.8+

Begin de cursus gratis

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

Huidige oefening

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 Exercise 15: Afronding