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Learner Reviews & Feedback for Machine Learning in Production by DeepLearning.AI

4.8
stars
3,226 ratings

About the Course

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...

Top reviews

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.

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451 - 475 of 559 Reviews for Machine Learning in Production

By Ramil J

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May 22, 2021

Fantastic

By Jayant K

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Jul 8, 2024

Loved it

By Marcelo B

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Jan 10, 2022

AMAZING!

By Vyacheslav K

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May 25, 2021

Perfect!

By puneet g

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Jan 23, 2023

Awesome

By Khizar S

•

Jun 7, 2021

love it

By Amin T

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Jul 5, 2021

Great!

By degreeuserdf t

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Jun 3, 2024

Great

By Diyorbek T

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May 6, 2023

super

By Atif F

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Jun 27, 2022

great

By Trung N H

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Sep 21, 2021

good

By A K P

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Nov 10, 2024

GOOD

By slibtec s

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Mar 8, 2024

good

By kothakota S

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Aug 2, 2023

Good

By T Đ H

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Jul 14, 2023

good

By Vivek B

•

Dec 28, 2022

good

By Reza B

•

Nov 16, 2022

Top!

By Aman K D

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Apr 21, 2022

good

By Pritam G

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Apr 20, 2022

nice

By Duc A L

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Oct 11, 2021

Good

By Willah m

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Aug 7, 2021

nice

By MohammadSadegh Z

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Jul 16, 2021

By Jeffrey B

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Dec 28, 2021

I was a little disappointed that this was heavily focused on unstructured data, but it was still a wonderful course. Many of the techniques of being "Data Centric" do not carry over as well to structured data. I am hoping I will hear more in the next courses of this specialization that address being data centric with structured data (which would seem to be more applicable to many business analytics cases).

By Cristian C H

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Oct 21, 2021

While the overall content of the course for ML LifeCycle is great, the examples and general assumptions are for supervised learning and labeled data, in some real scenarios, having labeled data is just not possible but by no means this indicates there is no possible AI solutions and models that give business value. So a little inclussion of unsupervised and semisupervised learning examples would help.