Salvare e caricare un modello

Con l'API Model, i modelli possono essere condivisi tra sviluppatori che non hanno accesso allo stesso server MLflow Tracking utilizzando un filesystem locale.

In questo esercizio, addestrerai un nuovo modello di LinearRegression a partire da uno esistente utilizzando il dataset Unicorn. Per prima cosa, caricherai un modello esistente dal filesystem locale. Poi addestrerai un nuovo modello a partire da quello esistente e lo salverai nel filesystem locale.

Il modello esistente è stato salvato nel filesystem locale in una directory chiamata "lr_local_v1". Il modulo mlflow verrà importato.

Questo esercizio fa parte del corso

Introduzione a MLflow

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Istruzioni dell'esercizio

Carica il modello dalla directory del filesystem locale "lr_local_v1" utilizzando la libreria scikit-learn del modulo MLflow.
Utilizzando la libreria scikit-learn del modulo mlflow, salva il modello localmente in una directory chiamata "lr_local_v2".

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Load model from local filesystem
model = ____.____.____("____")

# Training Data
X = df[["R&D Spend", "Administration", "Marketing Spend", "State"]]
y = df[["Profit"]]
X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.7,random_state=0)
# Train Model
model.fit(X_train, y_train)

# Save model to local filesystem
____.____.____(____, "____")

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: Introduzione ai modelli MLflow Exercise 2: Pacchetto di un modello di apprendimento automatico Exercise 3: Formato di archiviazione Exercise 4: Cosa c'è in un file MLmodel?Exercise 5: Modello API Exercise 6: Salvare e caricare un modello

Esercizio in corso

Exercise 7: Registrazione e caricamento di un modello Exercise 8: Modelli personalizzati Exercise 9: Creare una classe Python personalizzata Exercise 10: Modello scikit-learn personalizzato Exercise 11: Valutazione e sapore di Scikit-learn Exercise 12: Modello che serve Exercise 13: Al servizio di un modello Exercise 14: Punteggio di un modello servito

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: 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