Mencari run

Dalam latihan ini, Anda akan melakukan kueri run eksperimen dari beberapa eksperimen Unicorn dan hanya mengembalikan run yang memenuhi kriteria tertentu. Ini bermanfaat selama siklus hidup ML saat Anda perlu membandingkan data run.

Pertama, Anda akan membuat string filter untuk mengambil run dengan metrik R-squared lebih besar dari .70. Menggunakan fungsi dari modul mlflow yang melakukan pencarian run, Anda kemudian akan mengurutkannya secara menurun dan mencari hanya eksperimen "Unicorn Sklearn Experiments" dan "Unicorn Other Experiments".

Eksperimen telah dibuat di MLflow beserta metrik R-squared-nya. Modul MLflow akan diimpor.

Latihan ini merupakan bagian dari kursus

Pengantar MLflow

Instruksi latihan

Untuk variabel r_squared_filter, buat string filter untuk mengambil metrik "r2_score" yang "> .70".
Cari run untuk eksperimen "Unicorn Sklearn Experiments" dan "Unicorn Other Experiments".
Urutkan hasil R-squared secara menurun.

Latihan interaktif langsung praktik

Cobalah latihan ini dengan melengkapi kode contoh ini.

# Create a filter string for R-squared score
r_squared_filter = "____.____ > .70"

# Search runs
mlflow.____(experiment_names=["____", "____"], 
                   filter_string=____, 
                   order_by=["____.____ ____"])

Edit dan Jalankan Kode

Latihan ini merupakan bagian dari kursus

Pengantar MLflow

SkillTag.level.advancedSkillTag.label

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: Apa itu MLflow?Exercise 2: Komponen MLflow Exercise 3: Eksperimen MLflow Exercise 4: MLflow Tracking Exercise 5: Memulai sebuah run Exercise 6: Mencatat sebuah run Exercise 7: Bagaimana cara mengambil data run yang aktif?Exercise 8: Mengkueri run Exercise 9: Opsi kueri untuk mencari run Exercise 10: Mencari run

Latihan Saat Ini

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: 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