30 Days of Google Cloud
A Platform to learn, discover, experiment, and win exciting rewards
Turning from neophyte to skilled individuals!
Developer Students Clubs by Google in association with VSSUT, Burla organized the 30 days of Google Cloud Challenge facilitated by AITIK DANDAPAT(DSC, VSSUT lead).
The 30 days of cloud program kick-started to provide hands-on training on Google Cloud Platform — the tool that powers apps like Google Search, Gmail, and YouTube. As a first step, the participants had to enroll in the program and sign in with Qwiklabs, which provides a real cloud environment with no simulations and temporary credentials to perform hands-on lab activities. The Cloud program targeted two of the most in-demand skills booming in the technologically driven world. They were:
1. Cloud Engineering Track
2. Data Science and Machine learning Track
Each of the tracks was followed by six quests with two quests common in both and each quest consisted of at least five labs and one challenge lab that tested our learning in the labs already taken. The labs were assisted with detailed instructions and demo videos to help any individual get an insight of the context. An online help desk was maintained by a group of DSC members who were always ready to help and guide the participants facing difficulty while performing the tasks.
Let’s dive into a brief summary of the quests in the individual tracks:
CLOUD ENGINEERING TRACK
The first quest was Getting Started: Create and Manage Cloud Resources. It focused on making us write Cloud Shell commands and deploy a virtual machine. It dealt with running applications on Kubernetes Engine or Load balancer. Basically, it was an introduction to the fundamentals of Google cloud.
The second quest Perform Foundational Infrastructure Tasks in Google Cloud helped individuals to dive into cloud storage and application services like Cloud monitoring and cloud functions.
The third quest Set up and Configure a Cloud Environment in Google Cloud introduced us to Kubernetes engine deployment, fundamental SQL clauses, and helped in running structured queries in Big Query and Cloud SQL.
The fourth quest Deploy and Manage Cloud Environments with Google Cloud dealt with provisioning a complete Kubernetes cluster and breaking an application into microservices using Kubernetes Deployments and services.
The fifth quest was Build and Secure Networks in Google Cloud. This quest focused on creating load balancers and provided a practical experience to build robust networks.
The last and sixth quest was Deploy to Kubernetes in Google Cloud where the participants were introduced to docker images, containers and were involved in deploying fully-fledged Kubernetes Engine applications and managing deployment.
DATA SCIENCE AND MACHINE LEARNING TRACK
The first quest Getting Started: Create and manage Cloud Resources as stated earlier, was common in both the tracks.
The second quest Perform Foundational Data, ML, and AI Tasks in Google Cloud showed up ways to create a streaming pipeline using a Google provided cloud dataflow template. It helped in creating a Dataproc cluster, run a simple Apache Spark, and integrate speech recognition into web-apps.
The third quest was Insights from Data with Big Query. It dealt in teaching the fundamental SQL clauses and helped in troubleshooting common SQL errors and use the query validator.
The fourth quest Engineering data with the cloud helped in creating a data transformation pipeline, manage devices using cloud IOT core, use Big Query to analyze the data, and build an end to end machine learning solution using Tensorflow and AI platforms.
The fifth quest was Integrating cloud with Machine Learning APIs that stressed on the basic functioning of the APIs, extract, analyze and translate text from images with the cloud Machine learning APIs and detect labels, faces, and landmarks in images.
The last quest Explore Machine learning models with explainable AI provided hands-on practice with explainable AI which is a set of tools and frameworks that helps us to develop interpretable and inclusive ML models as well as deploy the models.
At the end of each lab, scores out of 100 were displayed with a green check mark. A quest was considered to be completed only on scoring a cent percent in individual labs. On successful completion of each quest, a skill badge was awarded on the Qwiklabs profile page which indeed encouraged one to move on with smooth cloud experiences.
Towards the end of the Google cloud event, Schwags Claim form were received by the participants via email and the one’s qualifying for the same were sent exciting goodies from Google Cloud.
This year 180 members from VSSUT, Burla registered for the 30 days of Google cloud challenge with 60 participants completing both the tracks and 85 participants completed at least one track.
Being a participant of the 30 days Google Cloud challenge, I would say,
“It was a very well- planned program that imparted knowledge providing hands-on experience on Cloud learning resources and helped in personal enrichment in a lot many ways. I seriously look forward to more such events. Thank You, DSC, VSSUT for such an exciting platform and I express my gratitude to Aitik Dandapat( DSC, VSSUT Lead, and Google Cloud facilitator) for being an amazing mentor throughout.”
