Aggiunta di parametri all'esecuzione del progetto

I parametri possono essere utilizzati per configurare il comportamento di un modello passando come variabili al modello durante l'addestramento. Questo ti permette di addestrare il modello più volte utilizzando parametri diversi senza modificare il codice di addestramento stesso.

In questo esercizio, utilizzerai il modulo mlflow projects per eseguire un progetto utilizzato per addestrare un modello di regressione logistica per il tuo esperimento sulle assicurazioni. Creerai del codice utilizzando il modulo mlflow projects che eseguirà il tuo progetto. Aggiungerai quindi dei parametri che verranno passati come iperparametri al modello durante l'addestramento.

Questo esercizio fa parte del corso

Introduzione a MLflow

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Istruzioni dell'esercizio

Chiama la funzione mlflow.projects.run() dal modulo mlflow projects.
Crea il dizionario dei parametri e imposta n_jobs_param su 2 e fit_intercept_param su False.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

import mlflow

# Set the run function from the MLflow Projects module
____.____.____(
    uri='./',
    entry_point='main',
    experiment_name='Insurance',
  	env_manager='local',
  	# Set parameters for n_jobs and fit_intercept
  	____={
        '____': ____, 
        '____': ____
    }
)

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Introduzione a 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: Introduzione ai progetti MLflow Exercise 2: Layout del file MLproject Exercise 3: Creare un progetto ML Exercise 4: Esecuzione di progetti MLflow Exercise 5: Comando di esecuzione di MLflow Exercise 6: Modulo progetti MLflow Exercise 7: Specificare i parametri Exercise 8: Aggiungere parametri a MLproject Exercise 9: Aggiunta di parametri all'esecuzione del progetto

Esercizio in corso

Exercise 10: Flussi di lavoro Exercise 11: Creare un progetto ML per il ciclo di vita di ML: Modellistica Exercise 12: Creare un progetto ML per il ciclo di vita di ML: Valutazione del modello Exercise 13: Creare un flusso di lavoro in più fasi: Modellistica Exercise 14: Creare un flusso di lavoro in più fasi: Valutazione del modello Exercise 15: Conclusione