Fasi del modello di transizione

Una volta che i modelli sono stati registrati nel Registro dei Modelli, ogni versione del modello ha diritto all'assegnazione di una fase. Le fasi vengono utilizzate per gestire il ciclo di vita del modello e sono utilizzate per rappresentare diversi ambienti di sviluppo.

In questo esercizio, utilizzando la classe mlflow client come client, prenderai i tuoi modelli "Insurance" e passerai ogni modello in base alla fase del ciclo di vita dello sviluppo. Per prima cosa, passerai il tuo modello più stabile all'uso in produzione. Poi passerai a un modello da testare e valutare. Infine, archivierai un modello non più necessario.

Il modulo mlflow client verrà importato.

Questo esercizio fa parte del corso

Introduzione a MLflow

Visualizza il corso

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

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

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Introduzione a MLflow

AvançadoNível de habilidade

4.8+

Inizia il corso 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: Introduzione al registro dei modelli MLflow Exercise 2: Creare un modello Exercise 3: Ricerca di modelli Exercise 4: Registrazione dei modelli Exercise 5: Registrazione di modelli esistenti Exercise 6: Registrazione di nuovi modelli Exercise 7: Fasi del modello Exercise 8: Fasi del modello nel Registro dei modelli Exercise 9: Fasi del modello di transizione

Esercizio in corso

Exercise 10: Modello di distribuzione Exercise 11: Servire i modelli dal Registro dei modelli Exercise 12: Caricare i modelli dal Registro dei modelli

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