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Learn MLOps for Machine Learning

Learn MLOps for Machine Learning

位教师：Pearson

包含在 Coursera Plus 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

6 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

6 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

要了解的详细信息

可分享的证书

添加到您的领英档案

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249分钟

Course Introduction2分钟
Learning objectives0分钟
Gather the data7分钟
Analyze the data7分钟
Prepare the data7分钟
Train a model8分钟
Evaluate the model8分钟
Validate the model6分钟
Deploy the model7分钟
Monitor the model7分钟
Learning objectives0分钟
Determine what the data sources are5分钟
Create ETL pipelines to compile the data10分钟
Understand the data schema with respect to the model5分钟
Identify data that can be used for the model4分钟
Perform feature engineering10分钟
Version the data with DVC14分钟
Make multiple data sets4分钟
MLOps best practices for data5分钟
Learning objectives0分钟
Use common Python libraries9分钟
Code versioning with Git9分钟
Perform hyperparameter tuning10分钟
Track experiments with MLFlow10分钟
Track experiments with DVC10分钟
Evaluate the models10分钟
Learning objectives0分钟
Decide the best deployment method10分钟
Test on pre-production environments10分钟
Deploy to production5分钟
Monitor the model for drift9分钟
Validate the pipeline flow11分钟
Automation points in MLOps9分钟
Set up redeploy pipeline3分钟
Learn MLOps for Machine Learning: Summary1分钟

5个作业总计150分钟

Learning the MLOps Pipeline Quiz30分钟
Handling the Data Quiz30分钟
Creating a Model Quiz30分钟
Working with Production Models Quiz30分钟
End of Course Assessment30分钟

位教师

Pearson

268 门课程9,454 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.