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

4.8
stars
3,219 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 5, 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 15, 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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551 - 557 of 557 Reviews for Machine Learning in Production

By SRIKANTH M

•

Sep 7, 2021

its very good experience

By Gal H

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May 7, 2024

tensorflow..

By Tman

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Apr 4, 2023

Well, I am a big fan of Andrew Ng, his initial ML course is what kickstarted my career change from a computer scientist to an established data scientist, I quite liked the Deep Learning Specalization, but this course is absolutely not what I hoped it would be. Explaining what a confusion matrix is in an MLOps course? Explaining precision, recall and F1 score? Come on. That is not content I want to hear about when paying for an MLOps course. Data augmentation and feature engineering? Also, not MLOps topics. A lot of important topics are briefly discussed, but not in detail. Quite a bit of content is rehashed from the Deep Learning Specalization. Good content, but this course is not the right place for that.

By Matthieu G

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Feb 28, 2024

I was quite disappointed, as it feel that this 1 week course could have been summarised in a 15min article: there are a lot of generalities, repetitions... and no hands-on assignments where you are effectively expected to code something (which is, to my point, fundamental to get something of ML mooc).

By Shahzad H

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Jul 11, 2023

We need more practical graded exercise lab to hone our technical skills, the labs in most cases are easy and not job specific

By S. H

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Aug 19, 2024

Coursera refuses to issue the specialisation certificate even though I spent over 300 euros and completed all courses. If I was told this beforehand, I would not spend anything on the course. The customer support team has not been helpful.

By Youssef A

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Dec 10, 2022

too much theory, the course could include some lab practices and be more fun and memorable