Menyimpan dan memuat model

Dengan Model API, model dapat dibagikan antar pengembang yang mungkin tidak memiliki akses ke server MLflow Tracking yang sama dengan menggunakan sistem berkas lokal.

Pada latihan ini, Anda akan melatih model LinearRegression baru dari model yang sudah ada menggunakan himpunan data Unicorn. Pertama, Anda akan memuat model yang sudah ada dari sistem berkas lokal. Lalu Anda akan melatih model baru berdasarkan model yang ada dan menyimpannya kembali ke sistem berkas lokal.

Model yang sudah ada telah disimpan ke sistem berkas lokal dalam direktori bernama "lr_local_v1". Modul mlflow sudah diimpor.

Latihan ini adalah bagian dari kursus

Pengantar MLflow

Petunjuk latihan

Muat model dari direktori sistem berkas lokal "lr_local_v1" menggunakan pustaka scikit-learn dari modul MLflow.
Gunakan pustaka scikit-learn dari modul mlflow untuk menyimpan model secara lokal ke direktori bernama "lr_local_v2".

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

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

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Pengantar MLflow

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4.8+

Mulai Kursus 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: Pengantar MLflow Models Exercise 2: Kemaskan model machine learning Exercise 3: Format Penyimpanan Exercise 4: Apa yang ada di dalam file MLmodel?Exercise 5: API Model Exercise 6: Menyimpan dan memuat model

Latihan Saat Ini

Exercise 7: Mencatat dan memuat model Exercise 8: Model kustom Exercise 9: Membuat Kelas Python kustom Exercise 10: Model scikit-learn kustom Exercise 11: Flavor scikit-learn dan evaluasi Exercise 12: Penyajian model Exercise 13: Menyajikan model Exercise 14: Skor dari model yang disajikan

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