Chevron Left
Volver a Machine Learning Data Lifecycle in Production

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

4.4
estrellas
451 calificaciones
83 reseña

Acerca del Curso

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

SC

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.

AW

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.

Filtrar por:

76 - 89 de 89 revisiones para Machine Learning Data Lifecycle in Production

por Daniel E

3 de mar. de 2022

There is quite a bit of support coding that is required to perform many of the tasks in the final lab. It is what it is and I got through it.

por Sagar D

14 de may. de 2022

Content is difficult to relate with, feels disconnected between modules and between different chapters.

por Michael L

13 de jul. de 2022

Didn't find the labs especially practical, some presentations felt bogged down by definitions.

por Carlos C

24 de oct. de 2021

It is too much Google oriented

por Will N

28 de mar. de 2022

I found this course very dissapointing, especially in comparison to the previous course that this expands on. Given the reputation of the speaker, I was expecting a higher standard.

To begin with, there is far too much focus on TensorFlow. The concepts in this course are important to know, however they are briefly introduced in the videos, which are followed by a TensorFlow coding lab. The key information is hidden behind what are called "programming assignments", which unfortunately are nothing more than regurgitating TensorFlow code. For week 3, the videos total 40 minutes, and for week 4, 31 minutes. This course would be improved by spending more time explaining the MLOps principles.

Many of the principles encountered in this course I have already been practising during my PhD, choosing to handcode basic pipelines to automate my ML analysis. I would say that while this course is useful, knowing how to automate a machine learning pipeline does not prepare you for the working world. Without understanding exactly what is being done when you run each TensorFlow command, you will not be able to understand what you are doing and this will limit the impact of the work produced. I have learned more through my own work than I have during this course.

There are alternatives to TensorFlow for automating ML pipelines and the demonstrations are not hidden behind a paywall. For that matter, there are a large number of videos online that demonstrate the use of TensorFlow.

por Roberto N L

28 de ene. de 2022

After the first wonderful course in this specialization, this one was quite a disappointment. While the topic is quite dense, the material covered in this course was very superficial and served only to sponsor TFX even though there are many other tools in this landscape. A more fitting name for this course would be "An introduction to TFX for the Machine Learning Data Lifecycle in Production:"

por Nikki A

11 de ene. de 2022

I was pretty disappointed in this course, particularly compared to the previous Andrew Ng course in this specialization. The last course was very informative and general, where as this one felt like a sales pitch for TFX. I learned very little, especially since my focus is on deep learning, not the shallow, tabular data that was discussed here.

por Max A

17 de sep. de 2021

The course is informative and well made, but the bugs in the grading algorithm are super annoying!

por Merlin S

4 de jul. de 2021

I​ts incomprehensible to me why this course has such a good average score.

por Chandramouli B

6 de abr. de 2022

This course was nothing short of painful for someone who has had some industry experience, as well as som experience teaching. The video instructions were disjointed, unclear and did precious little to prepare one for the graded lab assignments. There was significant lack of cohesion between the ungraded and graded labs. Finally, the TFX ecosystem is esoteric, unnecessarily complex and a nightmare to use. As someone looking to adopt a data pipeline for their production ML model, all this course has done is convince me not to use TFX.

por Panagiotis S

25 de ene. de 2022

Very poor content. Also it was not engaging at all. The instructor was just reading the slides and gave only a slight explanation on more advanced concepts. Also the graded assignments were too easy and only focused on Tensorflow products which not everybody out there uses. Personally I was dissapointed that using ONLY tensorflow components that cannot be used alongside with other libraries like Pytorch or MXNet. Very dissapointing..

por Germán G

28 de may. de 2021

Traté en varios navegadores de enviar mi trabajo para ser sometido a evaluación, sin éxito. No obtuve respuesta ni soporte.

El contenido es interesante pero el soporte y habilitación no está al nivel de lo requerido: es lamentable que no reembolsen.

por Longlong F

16 de may. de 2022

The assignment grading system is not working. I submit the right expected answer but got 0/120. This is pretty annoying and discouraging.

por Koraldo K

6 de ago. de 2022

Feel free to skip this course, you've learned most of this content in the intro and the rest is just TensorFlow marketing.