When will I receive my Course Certificate?

If you complete the course successfully, your electronic Course Certificate will be added to your Accomplishments page - from there, you can print your Course Certificate or add it to your LinkedIn profile.

Why can’t I audit this course?

This course is currently available only to learners who have paid or received financial aid, when available.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Learn MLOps for Machine Learning

通过 Coursera Plus 提高技能，仅需 239 美元/年（原价 399 美元）。立即节省

Learn MLOps for Machine Learning

位教师：Pearson

包含在中

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1个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

7 小时完成

灵活的计划

自行安排学习进度

1个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

7 小时完成

灵活的计划

自行安排学习进度

您将学到什么

Capitalize on MLOps as an emerging field. Data-focused companies are looking for engineers with these skill sets.
Build a basic MLOps pipeline from scratch with open-source tools - take a working template with you for your own projects.
Take ChatGPT into account to provide a practical bridge for engineers and DevOps teams.

您将获得的技能

您将学习的工具

要了解的详细信息

可分享的证书

添加到您的领英档案

作业

5 项作业

授课语言：英语（English）

了解顶级公司的员工如何掌握热门技能

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Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

该课程共有1个模块

When teams are working with machine learning models, changing features, different data sets, new algorithms, and unique computing resources all influence a machine learning model's performance. Tracking all of these items can be complicated. With tools such as DVC, MLFlow, AWS, you can meet the challenge. Milecia McGregor demonstrates how to use MLOps tools to improve machine learning and automate some of the steps in the process.

This module introduces MLOps for machine learning engineers, covering the end-to-end pipeline from data collection to production deployment. Learners explore data handling and versioning, model creation and experiment tracking, and best practices for deploying and monitoring models in production. Through practical workflows and industry-standard tools, the course emphasizes automation, reproducibility, and maintaining robust ML systems, equipping participants to apply MLOps principles to real-world projects.

涵盖的内容

35个视频5个作业

35个视频总计250分钟

Course Introduction 2分钟
Learning objectives 1分钟
Gather the data 8分钟
Analyze the data 8分钟
Prepare the data 8分钟
Train a model 8分钟
Evaluate the model 8分钟
Validate the model 7分钟
Deploy the model 8分钟
Monitor the model 7分钟
Learning objectives 1分钟
Determine what the data sources are 5分钟
Create ETL pipelines to compile the data 10分钟
Understand the data schema with respect to the model 5分钟
Identify data that can be used for the model 5分钟
Perform feature engineering 10分钟
Version the data with DVC 15分钟
Make multiple data sets 4分钟
MLOps best practices for data 6分钟
Learning objectives 1分钟
Use common Python libraries 10分钟
Code versioning with Git 10分钟
Perform hyperparameter tuning 11分钟
Track experiments with MLFlow 10分钟
Track experiments with DVC 10分钟
Evaluate the models 10分钟
Learning objectives 1分钟
Decide the best deployment method 11分钟
Test on pre-production environments 10分钟
Deploy to production 5分钟
Monitor the model for drift 10分钟
Validate the pipeline flow 11分钟
Automation points in MLOps 9分钟
Set up redeploy pipeline 4分钟
Learn MLOps for Machine Learning: Summary 2分钟

5个作业总计150分钟

Learning the MLOps Pipeline Quiz 30分钟
Handling the Data Quiz 30分钟
Creating a Model Quiz 30分钟
Working with Production Models Quiz 30分钟
End of Course Assessment 30分钟

位教师

Pearson

268 门课程 48,070 名学生

提供方

Pearson

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常见问题

Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.

If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.

Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.