Een model opslaan en laden

Met de Model API kun je modellen delen tussen ontwikkelaars die mogelijk geen toegang hebben tot dezelfde MLflow Tracking-server, via het lokale bestandssysteem.

In deze oefening train je een nieuw LinearRegression-model op basis van een bestaand model met de Unicorn-gegevensset. Je laadt eerst een bestaand model vanaf het lokale bestandssysteem. Daarna train je een nieuw model op basis van het bestaande model en sla je het weer op naar het lokale bestandssysteem.

Het bestaande model is opgeslagen op het lokale bestandssysteem in een map met de naam "lr_local_v1". De mlflow-module wordt geïmporteerd.

Deze oefening maakt deel uit van de cursus

Introductie tot MLflow

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Oefeninstructies

Laad het model uit de lokale map "lr_local_v1" met de scikit-learn-bibliotheek uit de MLflow-module.
Gebruik de scikit-learn-bibliotheek uit de mlflow-module om het model lokaal op te slaan in een map met de naam "lr_local_v2".

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# 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
____.____.____(____, "____")

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Introductie tot MLflow

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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: Introductie tot MLflow Models Exercise 2: Verpak een Machine Learning-model Exercise 3: Opslagformaat Exercise 4: Wat staat er in een MLmodel-bestand?Exercise 5: Model-API Exercise 6: Een model opslaan en laden

Huidige oefening

Exercise 7: Een model loggen en laden Exercise 8: Aangepaste modellen Exercise 9: Een aangepaste Python-klasse maken Exercise 10: Aangepast scikit-learn-model Exercise 11: Scikit-learn flavor en evaluatie Exercise 12: Model serving Exercise 13: Een model serveren Exercise 14: Score ophalen van een geserveerd 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: 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