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Beranda Machine Learning

Kursus

Pengantar MLflow

LanjutanTingkat Keterampilan

Diperbarui 11/2024

Mulai Kursus Gratis

MLflowMachine Learning4 jam16 videos51 Latihan3,750 XP13,125Bukti Prestasi

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Deskripsi Kursus

Mengelola siklus hidup end-to-end dari aplikasi Machine Learning dapat menjadi tugas yang menantang bagi para ilmuwan data, insinyur, dan pengembang. Aplikasi Machine Learning bersifat kompleks dan memiliki rekam jejak yang terbukti sulit dilacak, sulit direproduksi, dan problematis untuk diimplementasikan.

Dalam kursus ini, Anda akan mempelajari apa itu MLflow dan bagaimana MLflow berusaha menyederhanakan tantangan dalam siklus hidup Machine Learning, seperti pelacakan, reproduibilitas, dan deployment. Setelah mempelajari MLflow, Anda akan memiliki pemahaman yang lebih baik tentang cara mengatasi kompleksitas dalam membangun aplikasi Machine Learning dan cara menavigasi berbagai tahap dalam siklus hidup Machine Learning.

Selama kursus ini, Anda akan mempelajari secara mendalam empat komponen utama yang membentuk platform MLflow. Anda akan mempelajari cara melacak model, metrik, dan parameter menggunakan MLflow Tracking, mengemas kode ML yang dapat direproduksi menggunakan MLflow Projects, membuat dan mengimplementasikan model menggunakan MLflow Models, serta menyimpan dan mengelola versi model menggunakan Model Registry.

Seiring Anda melanjutkan kursus ini, Anda juga akan mempelajari praktik terbaik dalam menggunakan MLflow untuk versi model, cara mengevaluasi model, menambahkan kustomisasi pada model, dan cara mengintegrasikan otomatisasi ke dalam proses pelatihan. Kursus ini akan mempersiapkan Anda untuk sukses dalam mengelola siklus hidup aplikasi Machine Learning Anda berikutnya.

Persyaratan

Supervised Learning with scikit-learn MLOps Concepts

1

Introduction to MLflow

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.

What is MLflow?

Components of MLflow

MLflow experiments

MLflow Tracking

Starting a run

Logging a run

How to retrieve active run data?

Querying runs

Search runs query options

Searching runs

2

MLflow Models

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.

Introduction to MLflow Models

Package a machine learning model

Storage Format

What's in an MLmodel file?

Saving and loading a model

Logging and loading a model

Custom models

Creating a custom Python Class

Custom scikit-learn model

Scikit-learn flavor and evaluation

Model serving

Serving a model

Score from a served model

3

Mlflow Model Registry

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.

Introduction to MLflow Model Registry

Create a Model

Searching Models

Registering Models

Registering existing models

Registering new models

Model stages

Model stages in Model Registry

Transitioning model stages

Model deployment

Serving models from the Model Registry

Loading models from the Model Registry

4

MLflow Projects

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.

Introduction to MLflow Projects

MLproject file layout

Creating an MLproject

Running MLflow Projects

MLflow run command

MLflow projects module

Specifying parameters

Adding parameters to MLproject

Adding parameters to project run

Creating an MLproject for the ML Lifecycle: Model Engineering

Creating an MLproject for the ML Lifecycle: Model Evaluation

Creating a multi-step workflow: Model Engineering

Creating a multi-step workflow: Model Evaluation

Pengantar MLflow

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