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

4.8
stars
2,792 ratings

About the Course

In the first course of Machine Learning Engineering for Production Specialization, you will identify the various components and design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment constraints and requirements; and learn how to establish a model baseline, address concept drift, and prototype the process 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 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: Overview of the ML Lifecycle and Deployment Week 2: Selecting and Training a Model 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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51 - 75 of 499 Reviews for Introduction to Machine Learning in Production

By Vadym H

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Sep 7, 2023

I wish all product managers and C-level people took this course before starting AI projects;) Jokes aside, a lot of content in the course is very specific and will definitely help when you encounter those issues during development of ML projects. Looking forward to continue the specialization.

By Eagle S

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Feb 3, 2022

Very well structured course, informative, interesting, logical with clear examples, I have had a clear view of a life cycle of an AI project and tips and pitfalls, hands on opportunities to test step. Andrew always remind students their responsibility of being ethical, highly recommended.

By Luc R

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Feb 10, 2024

Another one of Andrew Ng and DeepLearning.AI's amazing courses. The course was very well designed with plenty of helpful labs and practical examples that helped you understand the material well. I would highly recommended taking this course if you are interested in Machine Learning.

By Abhilash G

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Jan 10, 2022

The whole specialisation is the best place to start if you are looking to productionize your machine learning models. The way they put forward each and every concept of MLOps life cycle will be a big eye opener, for people who are taking your machine learning models to production.

By Maryam B

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Nov 23, 2022

I am an experienced MLE and I found this course very useful. However, in the beginning I thought it might be basic concepts, but after finishing the course I still need to go back to take some notes. It improves my skill that I will apply in my current job. Thanks a lot Andrew.

By Michael S

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Jan 24, 2022

The course goes through error analysis, experiment tracking, scoping and more concepts related to machine learning projects. Very well taught by Andrew Ng.

It is an excellent introductory course to ML projects management in a production framework and has my warm recommendation.

By Joyce

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Jun 16, 2021

This course is very hands-on. It clearly teaches Machine learning beyond python notebook. I enjoyed this course and currently taking the second part of this specialization "Machine Learning Data Lifecycle in Production". Great content from Andrew Ng and Robert Crowe.

By Ibrahim Y

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Oct 24, 2022

This course is full of practical insights on how to tackle real-world machine learning problems. Specifically for me, I was working on a problem for a long time and after taking this course and applying the skills I learned, I was able to get a wonderful outcome.

By Shekhar S

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Dec 30, 2021

It's really refreshing to see the "behind the scenes" perspective on ML algorithm development. Although I have been working in Computer Vision for more than 10 years, I found Andrew's frameworks to think about the project lifecycle and data very useful. Thank you!

By Omar M A

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Sep 26, 2022

This is really one of the most important courses I ever took! Andrew as always explaining in a very clear and interesting way. The course material are so useful, I have been working in the ML field for 2 years and I learned a lot of new concepts in this course.

By Carlos A L P

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Nov 4, 2021

Great theorical material to understand ML projects. The 1st (ungraded) lab exercise was not very clear though when playing with the front end and back end application, it would be nice to provide more information or tips on how to complete it

By G A

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Apr 13, 2022

Great course, concise but valuable insights on how ML is actually used in the real world and what problems we typically face when deploying ML to solve actual business problems. Looking forward to the upcoming courses in this specialization.

By Subramaniam S

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Feb 28, 2024

Very compact but covers all the necessary topics. Every Machine Learning Engineer should learn to take his/her ML model to production. This is the course to attend to learn about production deployment of machine learning model-code.

By Hernán Q

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Jul 23, 2021

It covers a lot of the real world problems data scientists find when trying to build machine learning solutions. Many of the best practices reviewed here are a common sense thing but having it wrapped toghteter here was really great !

By Nilesh G

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Jun 26, 2021

Deep learning courses are always best, cover all aspects in theoretical as well as more emphasize on practical knowledge which helps a learner ready for the real life challenges in Data science domain...Thank You Andrew NG and Team

By Varshaneya V

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Jan 29, 2022

This course gives useful insights about deploying machine learning systems in production from PoC stage. These insights are the same that an experienced ML engineer would have got in his/her practical experience in the industry.

By Iosif D

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Oct 17, 2021

Amazing introductory course that gives you the full scope immediately, as well as many theoretical details on each section. I expect the following courses of the specialization to dive into more technical things and frameworks.

By Paulo A A M

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Jul 26, 2021

Excellent course!! A new way to understand the key factors to master the Machine Learning lifecycle. This is much more than one course, this is an invitation to change our mindset through an exciting journey with Andrew Ng!!

By Martin H

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Oct 5, 2022

For someone not starting out with machine learning in production it is a good introduction and for someone with experience it can be good with another perspective on ml in production, just run the videos on 2x speed.

By aitha v

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Oct 18, 2022

I needed to reset my deadline and start my learning again from week1 but it say error and was unable to reset the deadline. Not happy with the support.

this is not about the course content but support for new users.

By Taku F

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Jun 6, 2021

The course was fairly compact and you would be able to finish each week lesson every day if you eager to do so. It was fun and educational. I loved the surprise in the last question of the optional quiz in week 3.

By Motilal R S

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Jun 13, 2021

Great course explaining concepts on ML lifecycle and deployment, especially touching topics like concept and model drift, monitoring models, error analysis, experiment tracking, pipeline and lineage. I loved it.

By ChenChang S

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Jun 23, 2021

This is a great introduction for how the mature machine learning product could be morph into mature products with multiple challenges. It helps me a lot for understanding how future AI industry looks like !

By Daniel Y

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Dec 17, 2021

This course would be very useful if you are ML-engineers, data scientists. However, this course does not teach you how to code. To code, you need to take Deep Learning specialization or some other courses.

By Xiaonan S

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Sep 23, 2021

Very practical materials and application-focused methodology! A lot of rule-of-thumb gathered from ML pipeline experiences. Clear definition on acronyms and mainly easy-to-follow non-technical guidances.