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学生对 DeepLearning.AI 提供的 Machine Learning in Production 的评价和反馈

4.8
3,329 个评分

课程概述

In this Machine Learning in Production course, you will build intuition about designing a production ML system end-to-end: project scoping, data needs, modeling strategies, and deployment patterns and technologies. You will learn strategies for addressing common challenges in production like establishing a model baseline, addressing concept drift, and performing error analysis. You’ll follow a framework for developing, deploying, and continuously improving a productionized ML application. Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need experience preparing your projects for deployment as well. Machine learning engineering for production combines the foundational concepts of machine learning with the skills and best practices of modern software development necessary to successfully deploy and maintain ML systems in real-world environments. Week 1: Overview of the ML Lifecycle and Deployment Week 2: Modeling Challenges and Strategies Week 3: Data Definition and Baseline...

热门审阅

RG

Jun 4, 2021

really a great course. It'll really change your way of thinking ML in production use and will help you better understand how can you leverage the power of ML in a way that I'll really create a value

DT

Aug 14, 2021

Excellent course, as always. Very well explain for both Data Sicientist, Software engineer and Manager (with some basics undertsanding of ML). One of these courses that Data Sientist should follow.

筛选依据:

251 - Machine Learning in Production 的 275 个评论(共 579 个)

创建者 Bob K

Aug 17, 2021

Overall, an excellent and well presented introduction to ML in production.

创建者 Abishek K

Jul 27, 2023

Brilliantly explained with real-life examples. Thank you so much, Andrew!

创建者 María L A S

Feb 18, 2023

I am very satisfied with my first course of the specialization. Thank you

创建者 Viraj H

Oct 31, 2021

It is quite good course I learned a lot of basic things from this course.

创建者 Kuber C

Jul 24, 2024

Good to understand hurdles and tips for moving ML models into production

创建者 Salman K

May 21, 2024

Andrew is awesome and presents course concepts very well in his examples

创建者 Zoran H

Jun 25, 2022

It is a nice introduction to MLOps segment general terms and background.

创建者 Ghada S

Jan 11, 2022

Very insightful course. Many thanks for all the provided efforts over it

创建者 秀文

Sep 21, 2022

It provide a lot of tips & information for machine learning lifecycle

创建者 Saqueeb A

Apr 19, 2022

Learned some basics of MLOps and terms in this course. Looking forward!

创建者 DAYANANDA K

Sep 20, 2021

This Course covers all the basic practical aspects about ML Production.

创建者 Juan A

May 19, 2021

Very Excellent Course. Allows me to seek and dig deep into the ML World

创建者 D B

Sep 15, 2022

Andrew is awesome. So deeply knowledgable, hands-on and down-to-earth.

创建者 Aymen S

Jun 7, 2022

The is a very good introduction course for this amazing spezialisation

创建者 andre m

Jun 14, 2021

Amazing course, theory based on experience. Lessons that no book have.

创建者 Hoang V

Jun 6, 2021

Once of the best! I learnt a lot of useful insights from this series

创建者 Dev J

Jan 5, 2023

Deep analysis and learning for machine learning in production system

创建者 Iradat H

Aug 26, 2022

explanation was very good, even a beginner could get his point easy.

创建者 JAYESH D

Aug 1, 2021

Another great specialization. Grateful to whole deeplearning.ai team

创建者 Rob B

Jun 3, 2021

Great course with high-level information not covered anywhere else.

创建者 Ryan C

Feb 3, 2022

Great content, shame the video of Andrew NG talking is stuttering.

创建者 Cayo S

Oct 5, 2021

Great introduction to key concepts and practices in production ML

创建者 Raspiani

Aug 1, 2021

Great, Awesome, Wonderful, Fantastic, Thank You So much Prof.. :)

创建者 Ginni G

Sep 23, 2025

Very informative, very interesting course, able to learn a lot !!

创建者 Stefan L

Jan 5, 2022

Great introductory course that touches a lot of important topics.