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

4.4
stars
408 ratings

About the Course

In the third course of Machine Learning Engineering for Production Specialization, you will build models for different serving environments; implement tools and techniques to effectively manage your modeling resources and best serve offline and online inference requests; and use analytics tools and performance metrics to address model fairness, explainability issues, and mitigate bottlenecks. Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills. Week 1: Neural Architecture Search Week 2: Model Resource Management Techniques Week 3: High-Performance Modeling Week 4: Model Analysis Week 5: Interpretability...

Top reviews

JS

Sep 13, 2021

Excellent content and lectures from Mr. Robert . Thank you very much Sir for the excellent way of explaining these difficult topics . Thank you !!!

MB

Oct 20, 2021

I enjoyed this course a lot. It gave me a lot of ideas on how I can improve my models and make my workflow more efficient. Thank you.

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51 - 75 of 81 Reviews for Machine Learning Modeling Pipelines in Production

By EMO S L

Sep 29, 2021

Nice !!!!

By Diyorbek T

Mar 18, 2024

super

By kothakota S

Aug 17, 2023

Good

By Naveen K

Nov 23, 2022

Good

By Vijay

Nov 20, 2022

What's Good

- The selection of the topics for the programming assignments is outstanding - I am very experienced at completing QwikLab assignments with ML Pipelines and I still learned something new.

- The topic coverage is great - I learned a lot of things in the field that I was not aware of.

What can be improved

- The lectures are being read from notes and it's hard to listen to without increasing the playback speed to 2x. I think there should be more readings and the videos should be more engaging (i.e. like the GAN specialization's lectures).

- The quizzes should count as part of the grade. It's possible to complete the all QwikLab assignments within a couple hours and the entirely of the course completion credit is based on that.

By Shamiso C

Sep 30, 2023

The course is great, the instructors teach exceptionaly well. I didn't like the qwiklabs assignments. The instructions were not so clear and also everything was just copy and paste, I would have prefered to do things through the coursera lab or google collab like how it was with the first two courses in the specialization.

By Fernando F

Mar 9, 2022

Very nice course. The reason I graded it as 4 (and not 5) was related to the educational value of the labs based on Google's console. Per se, the exercises were flawless but I felt like I was just running the steps without much understanding of what I was doing.

Yet, an awesome course. I learned a lot! Thank you very much!

By Carlos A L P

Jan 3, 2022

Great course, you can learn new concepts related to MLOps and new technologies like major Cloud vendors, packages and platforms like TensorFlow for the ML model. I would like to have more exercises to apply the various terms and processes seen during the course

By V. A

Aug 31, 2022

It covers a vast territory of material. However, there is plenty to learn in terms of concepts. Some of the graded labs can make you dizzy. Overall, it is worth the effort. Get a financial waiver if possible.

By Giannis A

Jun 15, 2022

There were a lot of useful information and practical insights about the subject of the course. The material on Tensorflow-specific modules felt a bit unorganized and cumbersome to go through.

By Jerry Z

Apr 4, 2022

Lots of hands-on exercises accompanying knowledge learned in this course 3, but could be difficult for someone without prior working knowledge on Google Cloud platform/services.

By AG S

May 14, 2023

It is a great course but the QwickLabs are not really useful and sometimes result in errors and a waste of time.

By Suet Y M

Jun 8, 2022

The assignments are just quizes, and no practical programming exercise

By Avinash R c

Dec 10, 2023

very good course to understand the principals behind MLOPS

By Ruan L D

Nov 19, 2021

Good but I think that is much content for low time

By Aero

Jun 1, 2023

Good, but labs are quite complex.

By Mohamed N M

Feb 9, 2023

MLOps Engineers are not Data Scientists. The course and the specialization under which it falls give the impression that the focus is bringing ML workloads to production, but this course went too much into ML topics. Certainly vital topics, but ML Engineers will have a hard time. Admitted, the boundaries aren't that clear-cut, but MLOps as it's trending right now is not the Data Science work itself.

By Shubhendu V

Dec 2, 2022

Many important concepts and topics of MLOps are discussed in this course, although there's too much focus on Tensorflow and associated libraries/tools. It would have been better to have hands-on with other MLOps open source libraries and tools.

By Fares E

Sep 23, 2022

Amazing course, very well explained and Robert is a great instructor however the assignments are just BAD most of the labs are bugged and or broken would be much better if the assignments were on coursera's platform

By Justin H

Aug 10, 2023

The graded lab assignments are broken. :(

By Frank S

Mar 17, 2023

Too vague, unclear questions

By Simon B

Oct 3, 2022

I have to say that i did not like this course and I'm happy it is done. It is certainly not because the topics are not up to date or anything but it is the way it is presented. The slides are cluttered with buzzwords and nonsense and the actual content is only spoken. This makes the slides completely useless. From the labs, i learned pretty much nothing and will have to do another course about the same thing somewhere else.

By Yushi Y

Feb 14, 2023

The material and the way to content is delivered is poor. If I were to learn something about "in production", I really want to SEE a real thing IN PRODUCTION, instead of some theoretical discussions or unrealistic and trivial examples.

By Fan Z

Mar 5, 2024

Not very relating to production - it reviews different topics in ML and in introductory depth.

By Sagar D

Jun 15, 2022

Disconnected