TK
This was a good course along with google qwiklab which guide you through out the lab which makes a enrolled person a successful learner .
This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Machine Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. They work with (or can be) Data Scientists, who develop models, to enable velocity and rigor in deploying the best performing models.
This course is primarily intended for the following participants: Data Scientists looking to quickly go from machine learning prototype to production to deliver business impact. Software Engineers looking to develop Machine Learning Engineering skills. ML Engineers who want to adopt Google Cloud for their ML production projects. >>> By enrolling in this course you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service <<<
TK
This was a good course along with google qwiklab which guide you through out the lab which makes a enrolled person a successful learner .
RL
I​t's ok. There are example notebooks to understand the code. The pricing part is missing.
SC
Well designed course with Qwiklabs hands-on experience, awesome learning. Thanks to Google Cloud Team and Coursera
PA
VERY HELPFUL AND KNOWLEDGE BASED COURSE. THANKS TO ALL THE INTRUCTORS.
SR
It is a good designed course, but I would prefer to have basic knowledge of Machine learning and data science in order to understand this course even much better.
AM
The whole process of building the Kubeflow pipelines for MLOPs including the configuration part (what does into the Dockerfile, cloud build) has been explained fully.
DB
excellent experience. thank you very much coursera and google to give the oppurtunity to get certificate free.
SS
Excellent Curriculum. I enjoyed the whole lab assignments and the quiz.
PL
Course content was good. However, many of the Qwiklabs had bugs, resulting in not being able to complete the course with a grade of 100%.
PG
It was good experience learning about the deployment process of ML application on GCP.
JM
The content related to MLOps on GCP is quite good. If the labs were improved slightly to remove some of the bugs that are commonly posted in the message boards, this would be a 5 star.
AN
The course is quite educational, yet the lab material can sometimes be confusing, especially for beginner users
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The content is decent. But the labs are pretty broken, not well designed & maintained
Qwiklabs does not work!!
There's a few things underwhelming about this course. First, GCP has made MLops very complicated, technical and cumbersome. Since you would need to work with this tech on a regular basis, you really don't want this. Second, the tutorials are mostly challenging due to linux. The tutorials are also buggy and setting up the cloud resources takes a lot of time. Overall, not that happy with this course or the subject mater.
The qwiklabs have many issues, and due to the limited amount of tries I was not able to complete the course.
By far the worst experience. Videos and explanations are really good but all those goodness are killed by the Qwiklabs experience. Labs are frustrating because they don't simply work, not because you did something wrong. I would like to urge the team behind this course to put some effort and time fixing those labs and answer to the questions raised by the learners in discussion forums. By copy pasting the readymade answer to email qwiklabs support team won't help at all.
The Labs could be improved (bugs and clarity)
The content related to MLOps on GCP is quite good. If the labs were improved slightly to remove some of the bugs that are commonly posted in the message boards, this would be a 5 star.
Course content was good. However, many of the Qwiklabs had bugs, resulting in not being able to complete the course with a grade of 100%.
Accent is difficult to understand. Speaks to quickly. Cannot read subtitles and course content at the same time.
Some labs are impossible to complete due to incompatibility with github. Github requires verification email.
This was a good course along with google qwiklab which guide you through out the lab which makes a enrolled person a successful learner .
Great course to start for learning about MLOps. However, I hope there will have more videos to explain details on LABs.
I liked it. Made me realize how much of a pain MLOps really is.
while this course teaches some useful skills, in particular how to to offload ML workloads to GCP, and introduces Kubeflow well, it doesn't go into enough depth to really let the students master the material. It doesn't help that Kubeflow (and its GCP implementation) are fundamentally fairly complicated technologies that compete with other, more mature (but less specialized) tools like Airflow. All in all, a good starting point, but don't expect to master the material - further study will be required. This course only scratches the surface.
Finish the course , and need to pay for the certificate .Bad Way for Coursera like goes from EDX .
A neverending stream of jargon and self-promotion with occasional learning
quiklabs always have a trouble when I try this cource..
VERY HELPFUL AND KNOWLEDGE BASED COURSE. THANKS TO ALL THE INTRUCTORS.
Some Labs isn't working properly
Videos are nice and good for learning new perspectives but there is a huge problem in this course. Labs (required to complete if you want certificate) are bugy and for example i need to wait for one lab problem to be solved if i want my certificate (which is going on for more than 2 weeks as i can see in forums). Overall, good quality videos but unexpectedly very poor technical management.