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

4.3
stars
808 ratings

About the Course

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...

Top reviews

SC

Jul 2, 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.

DD

Jul 20, 2023

Liked it for the most part. It was a bit dull when going over the details of schema updates and meta data. But that might be the nature of the beast.

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101 - 125 of 166 Reviews for Machine Learning Data Lifecycle in Production

By Søren J A

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Aug 5, 2021

This is a nice course. I specifically like the focus on data and implementation of trained models.

ML is much more than getting models trained , real life data, data quality control and continuous model maintenance is key to having succes with ML in a real setting.

By Hamad U R Q

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Jun 28, 2022

Contents are great. But the course gets very theoretical, more like reading a book. There should be examples in the video lectures on example datasets like with Feature selection methods in week 2.

Good overall experience

By Piero C

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Nov 22, 2021

Overall, a good course. The lab activities have been planned extremely well.

Some concepts and definitions were a bit loose, and some quiz questions didn't actually reflect what was discussed in the lessons.

By Jacob W

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Jan 12, 2022

A comprehensive course. My only criticism is that in some videos the pacing is inconsistent where half the video is reviewing what will be covered and then it is very quick to go through the actual content.

By Carlos A L P

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Nov 25, 2021

I liked the intro to several techniques for feature engineering, validate anomalies between training and serving dataset but sometimes the labs didn't explain in details the steps implemented in the code

By Vishal W

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Feb 24, 2023

Content covered was good. But I felt the slided where going mostly in reading which I had to slow down and understand carefully. More visualization in the slides should have been there I guess.

By Dimitris S

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Feb 23, 2024

Interesting course but the videos were a bit repeatable and the content should be better organized. The part I found to be most interesting was the optional Week 4.

By Wanda R

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Jul 27, 2021

It's a new course so sometimes there are mistakes in the translations or there is something off in the assignment's grading, but the content is great. :)

By Josian Q

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Aug 15, 2023

In general I quite like the course however I find it easy to get lost in the code and would have liked the lectures to refer more to the code

By Umberto S

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Aug 15, 2021

Really practical course with good examples and a lot of materials on MLOps and examples on TFX to build and manage ML Pipelines.

By Shayan H

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Oct 13, 2021

The course is exciting. Lab and exercises are informative, but the answer to the quizzes are a little ambiguous.

By Hassan K

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Aug 5, 2021

It will be more interesting if unstructured data such as image, audio, ... is used more in the course.

By Muhammad F

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Feb 8, 2024

Lot of new concept for learning, which are difficult to absorb. But overall course is good.

By Choo W

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Aug 15, 2021

useful insights, but tfx implementation might be invasive towards exisiting mlops pipelines

By Felype d C B

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Aug 3, 2023

I think that graded labs must be more hard. But the content of this course is really good.

By Khaerul U

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Dec 30, 2021

course material very good, but instructor very rare give example that make sense to me

By Bharath P

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May 29, 2021

excellent course. Nice to see how we can detect data drift and skew drift

By Gonzalo A M

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Oct 27, 2021

Sometimes this course is a little boring

By Prashantha R

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Dec 31, 2022

Good

By Thomas K

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Jul 15, 2022

Each step in the data processing pipeline is touched but I was hoping for more in depth. It treats the topic on a fairly high level and I would put this course into a beginners or early intermediate course rather than an advanced one.

Given the lecturer is affiliated with Google, they are using tfx which might not be the most relevant tool to learn. I would have much preferred an option which is more package agnostic.

By Bestman e E

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Nov 29, 2023

The course was very revealing regarding the providence and lineage of data. It brought to the fore the importance of treating data as a first-class citizen in a machine-learning project. More importantly, it gave access to resources and tools available for use as an MLoPs Engineer in production. I am grateful. I look forward to mentoring students on how to deploy these skills and networking us to a career job.

By Ryan C

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Feb 25, 2022

The course has some usefulness but the videos are often sparsely filled with information and often repetative. In one section on feature engineering the course leader states that we probably know how to do this but we spend a significant amount of time recapping the basics... Most importantly the teacher is very difficult to listen to.

By maher s

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Jun 9, 2023

The course is good and it explains a lot , especially in the theoretical part .But, I expected more explanation for the code. It is kind of hard to investigate every thing myself. especially for the metadata part. It is not clear how it is versioned for each experience and how can i go back in to the previous state !

By Arturo R

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Sep 23, 2023

Interesting and good content, but the instructor is nowhere near the level of Andrew Ng. I had trouble understanding many topics which were not properly introduced. Also, tensorflow tfx documentation is not that good, so it's hard to understand it.

By AGNUS F

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Jul 13, 2022

I have learned many interesting things. Thank you.

Nvertheless, teacher's speech is not well organized. He sometimes talks about a concept, then about another and then come back to the first one. And there are some repetitions.