ML Yaşam Döngüsü için bir MLproject oluşturma: Model Mühendisliği

MLproject dosyası birden fazla giriş noktası içerebilir. Bu da tek bir MLproject dosyasıyla birden fazla giriş noktasını çalıştırabileceğin anlamına gelir; böylece birden fazla adımdan oluşan bir iş akışını tek bir MLproject dosyasıyla çalıştırabilirsin.

Bu egzersizde, model_engineering adlı giriş noktasını içeren bir MLproject dosyasının başlangıcını oluşturacaksın. Bu giriş noktası, fit_intercept ve n_jobs için hiperparametre değerleri olarak kullanılan parametreleri kabul eden bir Python betiğini çalıştıracak. Bu model, bir sigorta talebinden bir kişinin cinsiyetini tahmin etmek için kullanılır.

Bu egzersiz

MLflow’a Giriş

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

ML yaşam döngüsünün Model Mühendisliği adımı için model_engineering adlı bir giriş noktası oluştur.
İlk giriş noktası parametresini n_jobs, ikincisini fit_intercept olarak ayarla.
Parametreleri komutun içine yerleştir.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

"""
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 {____} {____}"
"""

Kodu Düzenle ve Çalıştır

Bu egzersiz

MLflow’a Giriş

kursunun bir parçasıdır

AvançadoNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

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: MLflow Projects’e Giriş Exercise 2: MLproject dosya düzeni Exercise 3: Bir MLproject oluşturma Exercise 4: MLflow Projects Çalıştırma Exercise 5: MLflow run komutu Exercise 6: MLflow projects modülü Exercise 7: Parametreleri belirtme Exercise 8: MLproject dosyasına parametre ekleme Exercise 9: Projeyi çalıştırmaya parametre ekleme Exercise 10: İş Akışları Exercise 11: ML Yaşam Döngüsü için bir MLproject oluşturma: Model Mühendisliği

Geçerli Egzersiz

Exercise 12: ML Yaşam Döngüsü için bir MLproject oluşturma: Model Değerlendirme Exercise 13: Çok adımlı bir iş akışı oluşturma: Model Mühendisliği Exercise 14: Çok adımlı bir iş akışı oluşturma: Model Değerlendirme Exercise 15: Kapanış