Mar 03, 2019
Definitely adds a unique perspective on thinking about machine learning systems at scale. This course is suitable for Data Scientists, Data Engineers and Machine Learning Engineers.
Oct 07, 2018
A very helpful course, we were able to practically apply all the knowledge we received from the First Specialization. I feel much more confident to do ML after this course!
创建者 Vinícius P A•
Aug 07, 2019
Nice recap of all previously concepts and a great hands-on with ML Engine.
创建者 Sachin K M•
Jun 14, 2019
this is what I needed for overall understanding for ML-based solution and it's deployment to production.
It really helps me to achieve my goals.
创建者 Muhammad Z H•
Sep 10, 2019
创建者 Rupam P•
Aug 22, 2019
it was really an awesome Experience
创建者 Subham S•
Sep 15, 2019
Aug 28, 2019
Tensorflow를 GCP상에서 구현하는 과정에 대해서 익힐 수 있어서 좋았습니다.
创建者 Pranshu A•
Sep 21, 2019
Learned a lot from this course on Data-Preprocessing to Deploying a model and making predictions.
创建者 Naman M•
Sep 23, 2019
创建者 Shubham M•
Sep 17, 2019
It's great if one wants to know actual deployment pipeline.
创建者 Pierre L M•
Sep 30, 2019
Good course overall, maybe the labs are too short or too easy, maybe it would be better to have a link to a doc with some related tasks.
Two labs were missing, one I could see while looking around in the notebooks, but the last one I didn't, i would have appreciated since I don't know flask yet
创建者 Ayush T•
Oct 12, 2019
Without a doubt, a great recap of the last specialization.
创建者 Elthon D F•
Oct 14, 2019
创建者 Lloyd P•
Jan 01, 2019
The qwiklabs interface to GCP is a little cumbersome. The need to start and stop sessions with each lesson wastes some time. I would prefer if the course came with a GCP credit and we were able to use our own accounts and still have a way to keep track of progress,..
创建者 Win S•
Nov 21, 2018
Very hard to understand all the code, is there any prerequisite for this course? // It is seriously hard.
创建者 Hemant D K•
Nov 24, 2018
Its good one.
创建者 arnaud k•
Jan 09, 2019
Overall this is a very well structured and well delivered course i learned a lot from it.
But I couldn't reproduce some of the examples on local machine so 4 stars for now.
创建者 vincent p•
Feb 06, 2019
Needs more explanations about the performance.
I do not understand why processing is so slow.
it is dozens of minutes or even more than 1 hour to process a few gigabytes.
Datalab takes more than 5 minutes to start, why ?
创建者 Rohan S•
Feb 24, 2019
This course is more suitable for learners who have some prior familiarity with machine learning. For people who are unfamiliar with the Google Cloud Platform, this course walks you through all the steps required to build a simple model on Google Cloud Platform. Overall, the course was good, but I would recommend previous experience with machine learning to avoid stalling.
创建者 Jonathan S•
Oct 13, 2018
It is an amazing demonstration of what Google Cloud can do in just a few lines of code, but a couple of the labs did not completely work for me, especially when it came to running jobs on Cloud ML. They were not essential, and the experience was still great.
创建者 Aditya h•
Sep 12, 2018
Good overview of end to end ML utilizing GCP starting from preparing the data set from Bigquery , utilizing data lab for building the model on a smaller dataset, Moving to Cloud ML engine to perform distributed training on a larger dataset, using Apache beam for pre-processing the data before serving and google app engine to finally serve the model
创建者 Mauro B•
Sep 19, 2018
Interesting hands-on course. You can grasp the full workflow from exploring a dataset, select/validate and transform inputs, define the model, train and validate it in a small scale and on Google Cloud Engine.
创建者 Cristobal S•
Oct 29, 2018
Great overview of the tools needed for deploying models for GCP. 4 stars are only because of lab technical issues.
创建者 Luis E O•
Apr 04, 2019
创建者 Jun W•
May 27, 2019
Nice content. Would be nice if students are required to write more codes, not just running the written codes .
创建者 Putcha L N R•
Jun 20, 2019
Pretty good start to the specialization, by reviewing the topics of the previous specialization! Looking forward to the rest of the specialization!