par Jordi W•
So you have a fairly good understanding of ML modelling techniques, you played around with code in Jupyter notebooks and perhaps even got a TensorFlow docker image with GPU support to run on your local machine. You readily admit that there always is more to learn about modelling techniques, but you wonder how models run and are made available to users in a production environment? This course/specialization dives into just that question and a wide set of related subjects. A most important dimension of ML.
par Roger S P M•
Robert's lectures are terribly boring and there was no work to make his slides useful, they are just the words he is going to say.
par Arthur F•
pretty helpful broad overview of some of the tools and techniques used in deployment of ML models. Gives a good starting point for personal implementation since the field is clearly deep and fast evolving
par Gordon L W C•
This course is what I think is missing in the market. A machine learning course with much emphasis on the practical aspects of running a machine learning platforms. I recommend it to anyone who is looking for the next step after you have finished training your model in Jupyter notebook. It is not the end but only the beginning.
par Franco V•
Excellent course and methodology. It helps me to improve my skills and expand my knowledge around the practice of MLOps. Exploring different tools and comparing them helps me to choose easily between them depending on each scenario.
par Walt H•
The most practical course for junior MLOPs engineers looking for the best productionization methodologie, and the tools that implement them.
par Fernandes M R•
The first course of MLOps, and the best.
par Thành H Đ T•
I like this course. Thank you so much.
par Liang L•
Relatable and hands-on.
par EMO S L•
par Prasanna M R•
Awesome course with very good instructors . However in instructions in graded google cloud labs could be improved.