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Avis et commentaires pour d'étudiants pour Machine Learning Data Lifecycle in Production par

265 évaluations
48 avis

À propos du cours

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...

Meilleurs avis

2 juil. 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 oct. 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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1 - 25 sur 53 Avis pour Machine Learning Data Lifecycle in Production

par Tyler G

11 juin 2021

A somewhat disappointing and misleading followup to the excellent first course in this specialization. I​t's heavily focused on shallow learning on structured data, which is not at all what I think of when I think of the challenges in prod ML.

TFX feels more like a solution to technologies that were available well before the deep learning revolution. T​here are certainly some useful, albeit complicated, tools coming out of google/tensorflow. We'll see if TFX sticks or just becomes another tensorflow.estimator in the shadow of keras.

par Liqiang D

29 juin 2021

Too many concepts packed in the lecture videos. The lecturor basically reads the slide instead of going through them.

I have been using TF in a professional job for three years. I still find TF is too complex to used.

par Riju M

15 juin 2021

The labs and assignments were interesting but the lectures, content videos were not engaging.

par Kamlesh

28 juin 2021

Access to the code is not available. Most of the concepts are too complicated in implementation. Having used model management before, i think many things should be made much simpler and developer friendlier.

par Arthur F

16 août 2021

Most of the course feels like an advertisement for Tensorflow Extended data pipeline management tool. If you are using TF then the tool may be a necessity in some cases, but otherwise it is largely not useful. There is very little which is transferrable outside TF. For the parts which are high level and not TF specific either you know it because you've encountered production systems before, or you don't know it, in which case this course is not really going to help you that much to ramp up.

par Jungwei F

11 juil. 2021

I​ have no doubt in Robert's knowledge on the subject, but delivering clear instruction with just right amount of contexts is an art that takes another few years to master. Way to go!

par Hitesh K

16 juil. 2021

If you're new to ML pipelines this is an excellent course to understand ML pipelines. Moreover, the labs and assignment are of good quality. If you already are familiar with ML pipelines tech like Amazon sagemaker then this course might seem repetitive of many things but still you get to learn about google's Ml pipeline stack which is TFX.

par ChenChang S

23 juin 2021

This handful course allows me to understand how the tft works and how to inspect with statistic aspect of view about data. Much interesting is the practice, it offers much practical example about data preparation, especially the optional week 4 time series data !

par Aadidev S

16 mai 2021

This was quite exciting! A lot of new, innovative content in the TFX libraries along with all the theoretical background necessary for determining when to use each component in the data life-cycle, highly recommend!

par Gustavo Z F

12 août 2021

The course takes an overall look over the general data life-cycle pipeline in production. That includes: (1) data collection, labeling and validation; (2) feature engineering, transformation and selection; and (3) data journey and storage. The instructor, Robert Crowe (TF engineer), presented a plain domain of the studied subjects and was fully able to explain them understandably. The technologies and libraries presented through the course are modern and applicable to the majority of my current projects. I would recommend this course to anyone interested into better understanding the data behavior in the production environment, as well as, how to use the introduce libraries to correct data anomalies/problems (e.g. data skew, data drift, others).

par Dr. F T

15 août 2021

G​reat course. Looking for one on TFX since the tool was open sourced few years ago. While TFX could be quite technical and hard to undertsand, Robert may it clear with many examples to practice and better understand it. Data Scientist that plan to deploy model in production should take it.

par Reza M

8 sept. 2021

T​his is to understand that Data Lifecycle is the rest of the iceberg, compared to Machine Learning Models being the tip of the iceberg. It is very good demonstration of TensorFlow capabilities processing and maintaining the data for operation.

par raveesh k

1 sept. 2021

T​his is the best course for understanding the data lifecycle in production, everything has been explained in video and the assignments given in the course are the real life practical scenario for data pipeline management for machine learning.

par Srinesh C

3 juil. 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.

par Albeiro d J E P

1 août 2021

Thank you so much to DeepLearning.AI. You inspire me! This course is a key step that most part of enterprises should follow in order to construct robust ML systems

par ALAN S S

9 nov. 2021

Incredibly useful and well teached. Awesome hands-on guide delivered by Robert Crowe, he is indeed a master. Im looking foward to learn with him again!

par Adarsh W

14 oct. 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.

par Roger S P M

22 août 2021

Sadly the lectures are rather dull. But take heart, the material is much more interesting in the next course. Press on. Get this one over with. You will be happier in course #3.

par Francis Q L

9 sept. 2021

I am kind of disappointed with this class. First, I feel this class is way too oriented towards structured data. But more importantly, I find that the labs are overly simple and I can't say that I would feel proficient applying the concepts learned in production.

par Shreyas R C

21 juil. 2021

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

par 이영전

11 sept. 2021

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

par flurin

8 sept. 2021

Lessons are well structured and clear, and the labs are very instructive. Above all the course is fun!

par Fernandes M R

19 juin 2021

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

par Luis S S

10 sept. 2021

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

par Tom v D

21 août 2021

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