Data Pipelines with TensorFlow Data Services
DeepLearning.AI · View on Coursera ↗
Taught by Laurence Moroney
Data ScienceComputer Science
About the course
Bringing a machine learning model into the real world involves a lot more than just modeling. This Specialization will teach you how to navigate various deployment scenarios and use data more effectively to train your model. In this third course, you will: - Perform streamlined ETL tasks using TensorFlow Data Services - Load different datasets and custom feature vectors using TensorFlow Hub and TensorFlow Data Services APIs - Create and use pre-built pipelines for generating highly reproducible
The dissent
The most-upvoted critical review (34 votes, 1.0★).
“I'm sorry but, it does not seem realistic pipelines, it clearly show the capability of tensorflow, but real world data pipeline on my point of view is completely different from that. I was expecting something like how to handle large amounts of data coming into the cloud, or onpremise cluster, and get it into a retraining pipeline, improving the models... but was completely different... If you are expecting something like, How to retrain a large model with large amounts of new data, realtime... that is not the course for you.”
Coursera's 4.5 comes from 537 ratings. Of those, 5.8% are 1–2★.
Starisk reads the reviews learners voted most helpful, and weights each by the square root of its votes: a review with 80 upvotes counts about five times one with 2. Unvoted reviews are not skipped out of laziness — by this model they carry no evidence that other students endorsed them.
That measures what the most-endorsed reviews say, not “true quality.” The rating distribution above is the whole population, so you can see the sample next to it.
Reviews read on 23 Oct 2026.Read them on Coursera ↗