Home 浏览课程 AI & Machine Learning MLOps Essentials: Deploying ML Models
Free course English 认证证书 Beginner #ai #machine learning #chatgpt #automation #models #mlops #machine #learning

MLOps Essentials: Deploying ML Models

Move your machine learning models from notebooks to real deployments with confidence. In this course, you will learn the basics of MLOps, model serving, packaging, release, deployment choices, and how to monitor and maintain models after launch.

4.0 (5 ratings)
120 learners enrolled
385 views
30 lessons
~30 min total
Self-paced, lifetime access

这门课程是关于什么的？

A practical beginner-friendly course for working professionals who want to move machine learning models from notebooks into reliable real-world deployments. The course explains the full deployment journey in clear, hands-on language: what MLOps is, how model serving works, how to package and release a model, how to choose deployment approaches, and how to monitor, maintain, and improve models after launch. Each module begins with a short orientation, and each lesson includes practical takeaways, key terms, and 1-2 quick exercises that can be completed in 5-15 minutes using simple tools and workplace-style scenarios.

这门课程的关键信息是什么？

价格 Free
证书 认证证书
时长 30 min
课程单元 30
语言 English
级别 Beginner
考试 Self-paced, lifetime access

Curriculum

6 modules · 30 lessons

1
Module 1: MLOps and the Model Deployment Lifecycle
5 lessons
  • What MLOps Is and Why It Matters
  • From Notebook to Production System
  • The ML Lifecycle at a High Level
  • Roles and Responsibilities in an MLOps Team
  • Common Deployment Risks and How to Think About Them
2
Module 2: Data, Features, and Training for Deployment
5 lessons
  • Training Data Quality and Validation
  • Feature Engineering for Consistent Production Use
  • Reproducible Training and Experiment Tracking
  • Model Evaluation with Deployment in Mind
  • Packaging the Model for Release
3
Module 3: Serving Models with APIs and Batch Jobs
5 lessons
  • Real-Time Inference and API Basics
  • Batch Prediction and Scheduled Scoring
  • Choosing the Right Deployment Pattern
  • Building a Simple Serving Workflow
  • Intro to Deployment Environments
4
Module 4: Containers, Cloud, and Deployment Tooling
5 lessons
  • Why Containers Help ML Deployment
  • Container Basics for Model Serving
  • Cloud Concepts for ML Workloads
  • Basic CI/CD for ML Deployments
  • Deployment Configuration and Secrets Management
5
Module 5: Monitoring, Logging, and Model Reliability
5 lessons
  • Why Monitoring Matters After Launch
  • Logging Predictions and System Events
  • Detecting Data Drift and Concept Drift
  • Monitoring Model Performance and Business Metrics
  • Alerts, Dashboards, and Incident Response
6
Module 6: Model Maintenance, Governance, and Continuous Improvement
5 lessons
  • Retraining Strategies and Model Refresh Cycles
  • Model Versioning and Change Management
  • Governance, Ethics, and Responsible Deployment
  • Documentation for Long-Term Maintainability
  • Putting It All Together: A Practical MLOps Roadmap

Certificate of completion

完成本课程后，您将解锁认证证书 — 可下载为 PDF，附带唯一编号和验证链接。

Each certificate has a unique verifiable link and QR code.

Frequently asked questions

Is this course free to start?

Yes. You can start learning this course for free.

Who is this course for?

This course is designed for learners who want practical, job-relevant skills and a structured path from basics to application.

Do I need prior experience?

No prior experience is required unless specific prerequisites are listed in the course requirements section.

How long does this course take?

The estimated total study time for this course is 30 min.

How does the certificate work?

完成本课程后，您可以下载带有唯一编号和公共验证链接的认证证书。

Learner reviews

4.0
5 ratings
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C
Christopher G. Verified · 18 5月 2026

Clear intro to deployment

Good walkthrough of moving models out of notebooks. I just wished the serving setup had a bit more depth.

S
Sarah M. Verified · 12 4月 2026

Solid overview of packaging, release, and monitoring. Felt a little fast in a few spots, but it was easy to follow.

H
Hannah F. Verified · 29 1月 2026

Useful deployment basics

The part on choosing deployment approaches stood out to me, especially how it compared serving options in plain language. I would've liked one more hands-on example, but the practical framing was good.

J
Jessica L. Verified · 9 12月 2025

Warm and practical

I liked how it tied MLOps to the real job of keeping a model running after release. Could've used a little more detail on monitoring alerts, though.

L
Laura C. Verified · 6 6月 2025

Straightforward

Covers the path from model serving to maintenance without a lot of fluff. I just wanted a bit more on release workflows.

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