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In the second course of Machine Learning Engineering for Production Specialization, you will build data pipelines by gathering, cleaning, and validating datasets and assessing data quality; implement feature engineering, transformation, and selection with TensorFlow Extended and get the most predictive power out of your data; and establish the data lifecycle by leveraging data lineage and provenance metadata tools and follow data evolution with enterprise data schemas. 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: Collecting, Labeling, and Validating data Week 2: Feature Engineering, Transformation, and Selection Week 3: Data Journey and Data Storage Week 4: Advanced Data Labeling Methods, Data Augmentation, and Preprocessing Different Data Types...

Principales reseñas


2 de jul. de 2021

Interesting material. There are quite a lot of typos and many code snippets are directly from the tfx manual pages however the instructions provided and logic of the course is clear.


13 de oct. de 2021

It is a very informative course. I learned a lot about data, metadata, schema and feature engineering, Also, Robert Crowe sir is a very good teacher.

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26 - 50 de 89 revisiones para Machine Learning Data Lifecycle in Production

por Enrique C

4 de ene. de 2022

Good intro but it looks like in other courses from, while they teach you something, they also try to "sell" people a specific framework. In this case, they seem to be selling TFX, whose API seems to be in constant flux with no guarantee (maybe not effort at all) of backwards compatibilty. It is very likely that if you download a notebook and try it in your computer, unless you're using the same library versions, it would not work. Some quizes seem to be not in sync with the lesons content (questions are about the content off the next session). not acceptable for a platform like Coursera that has horrible customer support and that is ruthless with users that have issues with their payment method.

I still recall how they sold people the Trax library in the NLP specialization which seems to have replaced Trax with huggingface. I take what is useful from these courses but I distrust their agenda.

por Antonis S

9 de mar. de 2022

+ New cool way of working with many possibilities

-Many new concepts and code with no clear connection to the "known" way of working.

-New code concepts not very clearly explained Urgent suggestions for improvement: Make the new concepts and code clear to the audience. Connect the examples to the previous way of ML

por Hui J

4 de ene. de 2022

A lot of the concepts are not well-explained. I feel like my mind is constantly drifting away when watching the video, to me, this course is more like a workshop/ads for tensorflow rather than explaining the data lifecycle properly.

por Reto A W

10 de nov. de 2021

I was not happy with the course. In the part 1 the lecturer showed a lot of real world example of developing big ML-systems. The lecturer for this course is more a library creator than a user of it. And therefore also it feels like an advertisement for tensorflow. Which is an odd combination for me. So it does not teach a lot of useful theory because it focuses on how tensorflow manages pipelines and not a lot about the concepts. But also the programming examples are very artificial examples taken from the tensorflow tutorials or documentation. What I liked in the first course was the practical view on a specific problem. The programming exercises I also did no like because I did not learn anything useful. I only "learned" to use tensorflow a bit. But the concepts implemented are so basic that they are not interesting at all. I am aware that this has to be like this if we are not expected to program for two day but I don't see the benefit for me of solving mandatory useless exercises. The result of this was: I was skipping through the videos in 2x and was solving the quizzes as fast as I could. Speaking of quizzes. There were quizzes asking questions never mentioned in the videos and once the quiz was posed before the video where the things were explained. Also the quizzes used unusual wording for concepts plus not clearly written questions. In the end there were some useful insights here and there but it was quite an effort for me to filter them out as my motivation was lacking after some time.

por Arturo M

10 de may. de 2022

I'm quite dissapointed by this continuation of the otherwise excellent Andrew Ng specialization.

I was expecting a course on frameworks and best practices for managing data in MLOps environments. Instead, this course is basically a commercial of Tensor Flow Extended, a MLOps framework by Google. Other tools often used in commercial applications (like cloud ML platforms) are not even mentioned.

It's true that the course does provide some tips, but they are often too general to be of practical use, specially for people with some experience in the field (e.g. "you need to validate your inputs").

I hope the next courses in the specialization are better.

por Nithiwat S

23 de jun. de 2022

The course is poorly prepared and presented. The instructor basically talks through slides with no concrete technical content, simply babling from one bullet point to another, from one slide to the next, unorganized. Lectures were horrible -- broad, technical content barely scratches the surface, uninteresting way to deliver and speak. This is a practical course. The intructor should have structured the lecture around a practical implementation through a real-life example. It's not there at all. Very difficult to continue listening and it's very frustrating. Lab and Assignments in Jupyter Notebook are good. Overall, a huge disappointment considering the first course in the Specialization taught by Andrew Ng was so good.

por Shreyas R C

21 de jul. de 2021

Best course for the professionals looking to upgrade there ML skills at production level! Thanks to the brilliant and wonderful course instructor.

por Youngjeon L

11 de sep. de 2021

Nice, Awesome MLOps Pipeline with TFX! I recommend this course anyone who want to build ml pipeline! Good Luck! :)

por Nam H T

16 de ene. de 2022

Great course with useful exercises to get learner familiar with ML Data pipeline using TensorFlow Extended!

por Fernandes M R

19 de jun. de 2021

Its good, I think was a little difficult because TensorFlow, but it was very explicative.

por Luis S S

10 de sep. de 2021

E​xcellent course. Theory and practice well combined, to fit diverse curiositiy levels.

por Han B

15 de ene. de 2022

instruction on debugging jupyter and submission issue is important for learners

por Tom v D

21 de ago. de 2021

This was my first course with Robert, which was a very pleasant experience.

por Zanuar E R

24 de dic. de 2021

It is really good course, the detail explanation of Data LifeCycle in TFX!

por Walt H

8 de sep. de 2021

Y​ou can immediately apply everything you learn in this course!

por Hieu D T

15 de ago. de 2021

Some questions are difficult. Lots of new terms. Great course!

por Pierre-Alexandre P

9 de jul. de 2021

Very good training about data lifecycle for ML projects

por Meng C

13 de ene. de 2022

Great overview and labs for cutting-edge TFX platform.

por Fady S

25 de jul. de 2022

Excellent material and comprehensive assignments

por David B M

26 de dic. de 2021

Podría ser cool el modo dark en los laboratorios

por Barata O

5 de ago. de 2022

Many Hands-on to help understand the materials.

por Chandan k

22 de jun. de 2021

A good course indeed to pursue my dream job !

por Thành H Đ T

8 de ago. de 2021

it's very nice. thank you so much

por Shannen L

29 de jul. de 2021

very helpful for ml engineers

por Shan-Jyun W

15 de ene. de 2022

Great course! Very Useful!