Has some good and useful content but like the rest of the courses in this specialization it looks a lot like a Google cloud infomercial. The graded labs are ok. I have mixed feelings about ungraded ones as a few are really good and some others are a waste of time. I think that students need to be harder in the way they rate these types of courses to force the vendor to deliver quality labs end-to-end.

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.

If you are doing the entire MLOps specialization, this coures won't bring much insight. If you don't you might learn something, i.e. regarding model serving. Unfortunately the labs are pure copy/paste exercise (qwiklabs) and do not yield any practical inisght. A missed opportunity.

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.

Good overview of major concept in the field, but expect to get just conceptual ideas and long to-do list of what you need to study somewhere else. Exercise (both graded and graded) are buggy and wasted a lot of time on non-essential details (like setting up the environment or just trying something in a different PC).

I was hoping for a final project that I can use in my portfolio because the course content is so much and not easy to digest

This course is exceptional. Why? Because even for people that have worked in production environments (which the course is geared towards) professionals often ask themselves if there are tools they're missing or not optimally taking advantage of when considering new production grade ML models. This course provides that path of "best practices" when it seems like most cloud-based courses are 1-off's of a specific tool but not how they are, or can be, integrated together. Coming from a much heavier AWS-based knowledge it was additionally refreshing to get up-to-speed with what GCP is offering. The education around getting up with Kubernetes and Kubeflow was great. Often it feels like productionalizing ML models is hacking together components to get any solution rather "knowing" a best path. Again, this course does a great job of setting some finite path with different tools (albeit production machine learning is fairly subjective based on company requirements/budgets/etc..). I feel confident I now know enough about tools inside of GCP to help make those artistic decisions about when and why I might opt for more production-grade ETL tools (Dataflow + Apache Beam) and when an "easier" batch processing setup with less complexity is merited. Learning basics of specific tools like Kubernetes was also a big plus.

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

Very insightful, with a good high-level explanation of challenges surrounding model usage and deployments in a production environment.

This is really a good learning with real word prodtion deployment. There are many things which we got in this learning.

Really enjoyed it however to get he most out of it, the time commitment is large

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.

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.

This course is essential for data scientist if they want to embark on the journey of data scientist in industry. I learned a lot of useful techniques. Thank you team!

Great course with tons of meaningful information and excellent hands-on material. Also videos and lectures and well designed and very well explained

The most practical course for junior MLOPs engineers looking for the best productionization methodologie, and the tools that implement them.

The part I enjoyed most about this course is its real-life projects which one can apply directly in business scenarios

This course has been so helpful and taught me so much information. A big thank you to all the instructors!!

A wonderful course to get started with MLOps. I have really enjoyed reading through all of its contents

Really great course! Up to speed in understanding AI in relatively no time!

Excellent overview of ML Ops. Very useful for Data Science practitioners.

It's intense, applied, concrete and to the point. A very good course.

Broad overview of the many tools and techniques for real world ML ops

The first course of MLOps, and the best.

I like this course. Thank you so much.