Marketing your products and not actually teaching anything.

Multiple labs had issues loading. In particular, any labs that required me to open ungit in Datalab would hang for extended periods of time or fail altogether. I would have to reload or reopen the Datalab pages multiple times just to be able to click to the next section. These assignments are timed. The amount of time wasted was not critical to my project success, but they were very frustrating to complete.

The last lab (before the final module quiz) simply stopped working. I closed the lab and tried again. I closed Coursera and tried again. I closed my browser and tried again. I restarted the computer. After 4 attempts, this process just became too frustrating to be work fighting with - especially since my employer wants me to compare Google Cloud Platform to Microsoft Azure and choose the one that works.

After working with GCP in this course, I can say that the Google Cloud Platform has exceptional features and API support. However, I have no reason to fight to learn the Google Cloud Platform if the course fails to load key aspects of the Platform in a reliable manner.

Overall a good curated course to help understand the GCP offerings and high level architecture of how their offerings fit in the current landscape. Easy to follow along as this was fundamental course.

This course really helped me in understanding exactly 'How the Big data and Machine learning can be used in Cloud' and 'The ease to use it'. Thank you for summing all the fundamentals in this course.

Even when no one else believes in you Google does. I'm old, 57, no one believes I can understand IT and Data science. That doesn't matter, Google has given lots of opportunities to learn. I have done the Google IT Support Professional Certificate, I'm studying Digital Marketing through Google Garage and now I've passed my first exam in the Data Engineering with Google Cloud Platform.

Great Course...I knew nothing about cloud computing, but it is easy to see why this has become so popular. I am fascinated by how well GCP integrates so many services in one easy to manage platform.

It was very good training with some of the real-time use cases enjoyed a lot. As a new person to google cloud and big data, I think this the best basic fundamental which I have come across so far.

This was a great course to understand at a high level how to design and create my data ecosystem and how to do it sustainably. Hopefully, next courses provide more in-depth the technical features.

I love the content Lak and the team generates and this is a great "sneak peek", but actually my experience in production is that details matter - a lot! This is why e.g. a 30 minute tutorial on deploying a deep learning model on a VM/CMLE, with pipelines in dataflow would actually take a few days to figure out.

For the courses from GCP to be really effective, a section with gotchas is needed: how does that work in real scenarios, like VPNs, security, package management, dataflow packages conflicts, limitations of language choice (e.g streaming and python)

Also, it wouldn't hurt to make the code cleaner. The repositories are a mess and the spagetti shell scripts, honestly, aren't the best idea.

I completed the "Google Cloud Platform Big Data and Machine Learning Fundamentals". I found that this course has the required material one should get to do hands on. I have seen people in my organization who says that they have given other cloud certification but they didn't do hands on. And these people who hasn't done hands on are unable to implement there knowledge in real time scenarios. For example if you have to schedule a job that loads the data (daily) from Datstore (GCS) then these guys says to open a bigquery and to do it manually. Whereas one who has done hands on will schedule a job and will call a wrapper (shell script or Python code) that will do this activity without manual intervention. The one who has not done hands on says why you are running Bigquery from Python or shell script. I am very much found of this course as this course connects you to the every possibility of connecting GCP with real world applications.

It is a great course suitable for any beginner without prior experience and knowledge about Google Cloud Platform.It is aimed for those who interested in Big Data and Machine Learning.Some of the Big Data and Machine Learning products in GCP are explored and learnt about BigQuery using SQL,Cloud Data Pub/Sub,AutoML Vision and lastly about how to build Tensorflow model.The hands-on labs given are helpful and gave step-by-step instructions to the user.The videos are designed to teach someone who is not familiar with common query language,SQL,data modeling and Machine Learning.It is totally fun and enjoyable throughout the learning journey.Can't wait to learn more and deeper about Machine Learning with Tensorflow in GCP or the specialization for Big Data and Machine Learning!Aim to become Google certified Associate Cloud Engineer next!!

Very good introductory course. I particularly like the labs as they bring everything to life. It also helps to practice with your own data as you will notice little details that would require time to complete e.g. data prep but these aren't so apparent during the lab because the data were already prepared for you plus worth noting that the sessions are designed to show possibilities.

Greatly curated scenarios for all the various possible aspects where applications or systems can be migrated to the cloud platform and thus leverage the features and services provided. For a beginner to the whole dimension too, this course would enlighten them to the various methodologies and technologies which are being used to implement modern time's intelligent systems.

The course was very well presented. Before taking this course, I was only exposed to ML theory. But after taking this course I came to know HOW EASY IS TO APPLY ML using GCP. I have planned to take on other courses to complete the specialization. The instructor was very passionate about the topics. I am happy to take this course. Thanks Google and Coursera for the course.

It felt a bit long in some places but I appreciate the breadth of information being squeezed in a small time frame. The qwik labs and datalab notebooks were great to learn some things by trying them out. I think it would be even better if there was a task to create a simple notebook from scratch - that is something that I will need to do to continue learning.

This course provides the basics of Google Cloud Platform and makes us understand Google Products and services on Cloud. I would love to explore GCP more and will opt for the remaining GCP courses.

A good intoduction. I liked that the integration capabilities of GCP services were shown in almost every module.

Great high level overview.

Very Basic course.. But helpful to get started with GCP

There is valuable information in the course and some good exercises, but they are hidden among many information which is not so useful. In overall it is an entry class to use the technology, but many customers already know this sort of things because they tested our products or the features we comment here are similar to features of other vendors' products. Their reaction when they are presented the technology for the first time is: "Thank you. My questions and issues are quite specific and not reflected here".

Superficial. Little learning occurred. Basically a product presentation.

This course doesn't introduce you to the concepts; more so it is an advertisement for Google Cloud Products. The labs don't explain how things work, they are just naive click-along activities.

disappointing, Lab instructions and Video is not at all matching, and there are numbers of errors in lab instructions

and also Video should be some more informative

In this course, I learned How to use the Google cloud platform(GCP) and it's tools like BigQuery, Cloud Storage, Vision, Dataproc, Pub/Sub, Dataflow, compute engine, etc.

In GCP, we can generate the instance of Virtual Machine(VM). It's a serverless platform (Google has it's own data centers). We can develop a complete software through GCP.

IN GCP, we can build custom models. It is very handy to operate for BigData. The Data in GB, TB, or PB can be processed in seconds or minutes on GCP.

Also, I deployed the ML model for Classifying Images with Pre-built ML Models using Cloud Vision API and AutoML.

In this model, we classified the Images of clouds in three categories., viz cirrus, cumulonimbus, and cumulus.

The cool features of AutoML and Vision API-

-We don't have to write code for building the machine learning(ML) model.

-AutoML decides the dataset splits for training and testing.

-If you are working with a dataset that isn't already labeled, AutoML Vision provides an in-house human labeling service.

- We have to just evaluate the model by adjusting the Confidence threshold and the confusion matrix.

Sometimes the training time will be more because of large datasets, node training time as well as infrastructure set up and tear down.

Though it is cost-efficient because you have to pay for the memory you use, The time processing takes place(for Training the nodes in ML), etc.

The bottom line is GCP offers IaaS (Infrastructure as a Service) in the form of Google Compute Engine, and it offers Paas (Platform as a Service) in the form of Google App Engine. As for FaaS (Function as a Service), GCP offers it in the form of Google Cloud Functions.

I am fascinated to learn how Google Cloud successfully builds applications that use our big data and machine learning products. This course helped me to understand real-world data and ML challenges and gave practical hands-on expertise in solving those challenges using Google Cloud Qwiklabs.What were the challenges faced?1. Migrating existing big data workloads to an environment where we can effectively analyze all of your data, interactively analyzing large datasets using BigQuery2. Building scalable pipelines that can handle streaming data, so that businesses can make data-driven decisions more quickly using Cloud Pub/Sub and Cloud DataFlow3. Building machine learning models(recommendation, prediction and classify images) so that we are able to make predictive forward-looking actions using our data using Cloud SQL, Spark, VisionAPI and Cloud AutoML