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Opiniones y comentarios de aprendices correspondientes a Machine Learning Data Lifecycle in Production por parte de

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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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51 - 75 de 90 revisiones para Machine Learning Data Lifecycle in Production


22 de jul. de 2021

Great practice exercises!


14 de sep. de 2021

Great hands-on learning.

por Kamran S

25 de may. de 2022

very informative

por Manuja

9 de jun. de 2021

Fantastic course

por Raspiani

19 de ago. de 2021

Great, Thanks..

por EMO S L

20 de sep. de 2021

Great content

por Viktor K

4 de ago. de 2021


por Jennifer K

17 de dic. de 2021

T​his is a very thorough introduction to data issues that arise when you go from proof-of-concept to project in production. It uses TensorFlow Extended components to illustrate workflow concepts, and the labs involve using these components in programming assignments. If you do all the ungraded labs, the programming assignments are quite easy.

por Dennis M

13 de ago. de 2022

This course was interesting. However it did dive into the software side of the data life cycle. Not as much discussion was provided to accomplish the graded assignments. The ungraded labs did help, but did not provide enough depth in places. I would have liked to see more of the architecture of the entire data lifecycle solution presented.

por Ivan P

23 de nov. de 2021

T​o much emphasis on tensorflow, too few on underlying concepts, while we need it and alternative to TF. If the course was call "implementing <current course name> in TF" this would be fine, otherwise name is mileading. However, the course content is well structured and interesting, just 4 stars for a misleading name :)

por Søren J A

5 de ago. de 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.

por Hamad U R Q

28 de jun. de 2022

Contents are great. But the course gets v​ery 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.

G​ood overall experience

por Piero C

22 de nov. de 2021

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

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

por Jacob W

12 de ene. de 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.

por Carlos A L P

25 de nov. de 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

por Wanda R

27 de jul. de 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. :)

por Umberto S

15 de ago. de 2021

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

por Shayan H

13 de oct. de 2021

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

por Hassan K

5 de ago. de 2021

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

por Choo W

15 de ago. de 2021

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

por Khaerul U

30 de dic. de 2021

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

por Bharath P

29 de may. de 2021

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

por Gonzalo A M

27 de oct. de 2021

Sometimes this course is a little boring

por Kaltenbrunner T

15 de jul. de 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.

por Ryan C

25 de feb. de 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.