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

4.5
stars
318 ratings

About the Course

In the fourth course of Machine Learning Engineering for Production Specialization, you will learn how to deploy ML models and make them available to end-users. You will build scalable and reliable hardware infrastructure to deliver inference requests both in real-time and batch depending on the use case. You will also implement workflow automation and progressive delivery that complies with current MLOps practices to keep your production system running. Additionally, you will continuously monitor your system to detect model decay, remediate performance drops, and avoid system failures so it can continuously operate at all times. 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: Model Serving Introduction Week 2: Model Serving Patterns and Infrastructures Week 3: Model Management and Delivery Week 4: Model Monitoring and Logging...

Top reviews

MN

Apr 21, 2022

This course is essential for data scientist if they want to embark on the journey of data scientist in industry. I learned a lot of useful techniques. Thank you team!

RF

Sep 19, 2022

Great course with tons of meaningful information and excellent hands-on material. Also videos and lectures and well designed and very well explained

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26 - 50 of 50 Reviews for Deploying Machine Learning Models in Production

By Franco V

Oct 2, 2021

Excellent course and methodology. It helps me to improve my skills and expand my knowledge around the practice of MLOps. Exploring different tools and comparing them helps me to choose easily between them depending on each scenario.

By Masoud A N

Apr 22, 2022

This course is essential for data scientist if they want to embark on the journey of data scientist in industry. I learned a lot of useful techniques. Thank you team!

By Walt H

Sep 11, 2021

The most practical course for junior MLOPs engineers looking for the best productionization methodologie, and the tools that implement them.

By KAMAU I M

Dec 20, 2022

The part I enjoyed most about this course is its real-life projects which one can apply directly in business scenarios

By John L

Apr 17, 2022

This course has been so helpful and taught me so much information. A big thank you to all the instructors!!

By Laxmikanta G

Dec 22, 2021

A wonderful course to get started with MLOps. I have really enjoyed reading through all of its contents

By Javier H A

Dec 29, 2022

Really great course! Up to speed in understanding AI in relatively no time!

By Sri V D

Jan 6, 2023

Excellent overview of ML Ops. Very useful for Data Science practitioners.

By Vincent L

May 17, 2022

It's intense, applied, concrete and to the point. A very good course.

By Kevin S

Feb 6, 2022

Broad overview of the many tools and techniques for real world ML ops

By Fernandes M R

Sep 24, 2021

The first course of MLOps, and the best.

By Thành H Đ T

Oct 6, 2021

I like this course. Thank you so much.

By Bonginhlanhla M

Sep 4, 2023

The course was really great

By Alexandre B

Mar 5, 2022

Le meilleur cours de MLOps

By Liang L

Oct 9, 2021

Relatable and hands-on.

By Ekemini S

Jun 28, 2023

it is well structured

By Raspiani

Oct 2, 2021

Great, Thank's

By EMO S L

Oct 18, 2021

Great course

By Saurabh A

Aug 5, 2022

Done

By Prasanna M R

Oct 6, 2021

Awesome course with very good instructors . However in instructions in graded google cloud labs could be improved.

By Afif A

Apr 20, 2022

it's a pretty good overview, only downside is the focus on GCP

By Anup K M

Jun 11, 2023

Good

By Ashkan R

Jan 18, 2024

Outdated, google's oriented tools. Doesn't involve open-source guidance.

By Guido S

Dec 14, 2023

The syllabus is somewhat random at times and sometimes information is outdated. There is a disconnect between the complexity of the labs (what is actually done there, not the copy-paste that one has to do technically to pass) and the superficiality of the videos. What is really cool though is that actual deployments can be carried out on GCloud.

By Justin H

Aug 11, 2023

Two google cloud labs were broken. Forum assistant was very helpful and got it working after some down time. Nonetheless, action was taken. Kudos to him. Coursera and Google labs partner which is Qwiklabs need to get their shit together.