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홈 Machine Learning

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MLOps 개념

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업데이트됨 2025. 12.

MLOps가 어떻게 머신러닝 모델을 로컬 노트북에서 실제 비즈니스 가치를 창출하는 운영 환경의 실행 가능한 모델로 전환하는지 알아보세요.

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Understanding Machine Learning Understanding Data Engineering

1

Introduction to MLOps

First, you’ll learn about the core features of MLOps. You’ll explore the machine learning lifecycle, its phases, and the roles associated with MLOps processes.

What is MLOps?

Explaining MLOps

MLOps principles

Different phases in MLOps

The ML lifecycle

Tasks per phase

Roles in MLOps

Your MLOps team

Core roles in MLOps processes

2

Design and Development

Next, you’ll learn about the design and development phase in the machine learning lifecycle. You’ll explore added value estimation, data quality, feature stores, and experiment tracking.

MLOps design

Identifying business requirements

Key metrics

Data quality and ingestion

Determining data quality

Data quality dimensions

Feature engineering

Improving model performance

Helping a friend

Experiment tracking

What can we track?

Skipping experiment tracking

3

Deploying Machine Learning into Production

In this chapter, you’ll dive into the concepts relevant to deploying machine learning into production, such as runtime environments, containerization, CI/CD pipelines, and deployment strategies.

Preparing model for deployment

Runtime environments

To containerize or not to containerize

Machine learning deployment architecture

Microservice

API data flow

CI/CD and deployment strategy

CI/CD pipelines

Deployment strategies

Automation and scaling

How components automate and scale

MLOps components

4

Maintaining Machine Learning in Production

Finally, you’ll learn about maintaining machine learning in production, with concepts such as statistical and computational monitoring, retraining, different levels of MLOps maturity, and tools that can be used within the machine learning lifecycle to simplify processes.

Monitoring machine learning models

Machine learning monitoring

Statistical vs. computational monitoring

Retraining a machine learning model

Examples of drift

Levels of MLOps maturity

MLOps maturity

MLOps maturity levels

MLOps tools

Using a tool

Pick the right tool

Recap: MLOps concepts

MLOps 개념

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