Introduction to Google Cloud

Editor's Notes

• #2: Now it’s time to introduce you to the 30 Days of Google Cloud program, but before that let’s dive into why should you consider building a career in cloud right now.
• #4: [Read out the agenda on this slide.]
• #6: The learning opportunities with Google Cloud are a great way to prepare for cloud careers. What’s possible with cloud? Let’s look at an example that we are all likely familiar with.
• #8: There are three main types of cloud-based services: Infrastructure as a service Platform as a service Software as a service
• #9: Consider the infrastructure needed for an online game. You can use Google Cloud services to store: Object data such as image and video files High-volume or semi-structured data such as streaming or game data Relational data such as user id, name and so on There are multiple options such as Kubernetes Engine for your application runtime environment. This is where your application code executes. You can also build apps which use other services like Google Maps and YouTube. With cloud-based services, you don’t have to set up and manage your own infrastructure. You can simply focus on developing your app and then use managed cloud services to make it available globally in just a few minutes!
• #10: Now it’s time to introduce you to our hands on learning platform, Qwiklabs. We hope that by now you have created your Qwiklabs account and have also redeemed the 600 credits that you would need for the program. If you have still not done that, then please follow the steps that I am about to show in order to get them.
• #11: [Read the content on the screen] Let’s go over each track one by one.
• #12: First track that we have in the program is the “Cloud Engineering” track. By completing the quests under the Cloud Engineering track, you will learn to explore and deploy solution elements, including infrastructure components such as networks, systems, and applications services. We have a total of 6 quests under this track and you will be doing the first one today i.e. Getting Started: Create and Manage Cloud Resources. The rest of the quests you will have to complete on your own by the end of this week. Not to worry, we will provide you with enough support so that you can complete them.
• #13: Next, we have the “Data Science & Machine Learning” track. By completing the quests in this track, you will learn and demonstrate proficiency in key topics related to big data and machine learning to prepare for roles such as data analyst, marketing analyst, business intelligence professional, and data engineer. This track also consists of 6 quests and we will be doing Perform Foundational Data, ML, and AI Tasks in Google Cloud quest today.
• #14: After completing each quest from either of the tracks, you have a chance of earning Google Cloud Skill Badges. Skill badges are used to show that you have actually applied your new learned skills using a challenge lab at the end where you have to take your new learned skills for a test drive.
• #15: Incognito window makes it easier for you to understand the whole process from scratch.
• #17: You are all set to take part in the hands on lab experience now. As discussed previously, today we will be going through the Getting Started: Create and Manage Cloud Resources quest - one of the foundational quest on Qwiklabs that will help you get started with GCP.
• #18: This is what you should see. Click on “Enrol in this Quest” button. Skip the video, if you saw a video after you enrolled and wait for the next steps.
• #19: You should now see the 6 labs listed in the quest. We will go through the first lab in the quest together right now. The rest labs you will have to do on your own. We will give you a small intro for the rest labs as well so that you don’t face any difficulties.
• #20: You’ve likely worked with a physical computer before. A physical computer has resources such as CPUs, memory, disk storage, network configuration, operating system, and other application software. A virtual machine is a software-based computer and like a physical computer it has resources like CPU, memory, operating system etc. A virtual machine uses the physical resources of the host that it is running on. Every virtual machine has virtual devices that provide the same functionality as physical hardware. They also have additional benefits related to portability, manageability, and security. For example, you can launch a virtual machine, run applications for some time, and then destroy the virtual machine when the resources are no longer needed. Images: https://pixabay.com/vectors/computer-case-desktop-network-156129/ https://pixabay.com/vectors/cloud-computing-host-server-2023902/
• #21: Troubleshooting: For the first objective, make sure to use the exact configuration while creating the virtual machine i.e. same name, region/zone, machine-type etc. For the second objective in the lab make sure to use Cloud shell only.
• #23: But Google does. And it has used this training data to train its models and exposed it as Machine Learning APIs. One great thing about these APIs is that they require ZERO prior knowledge of ML. Basically, Google Cloud handles the “training” aspect of machine learning - gathering data and building the predictive models - allowing you to jump straight to the “Predict” phase where you give the API data and get back information.
• #24: Speaker Notes: Google Cloud provides several pre-trained machine learning models. You can build applications with ML, even if you are not an ML expert. Here are our five Google Cloud Machine Learning APIs - Vision, Video Intelligence, Speech, Natural Language, and Translate. Demo the Speech to Text API Links: cloud.google.com/vision - cloud.google.com/video-intelligence cloud.google.com/speech-to-text - maybe ask for a volunteer from the audience cloud.google.com/text-to-speech cloud.google.com/natural-language cloud.google.com/translate
• #25: You are all set to take part in the hands on lab experience now. As discussed previously, today we will be going through the Perform Foundational Data, ML, and AI Tasks in Google Cloud quest.
• #28: We’ve got a few ways to get help while you are completing the quest.
• #29: We’ve got a few ways to get help while you are completing quests. [Give a small walkthrough by taking them to the Qwiklabs page]
• #30: We’ve got a few ways to get help while you are completing quests.
• #31: We’ve got a few ways to get help while you are completing quests.
• #32: We’ve got a few ways to get help while you are completing quests.
• #33: You need to complete the rest of the quests & skill badges remaining in the Cloud Engineering track! You would have already completed 2 labs in the Getting Started: Create and Manage Cloud Resources quest in this session - do remember to complete the remaining labs immediately after the session while the knowledge is still fresh. Also, remember we talked about skill badges earlier. It’s really easy to earn one when you have already completed a quest. All you have to do is complete one more challenge lab after completing the quest related to that skill badge. Let us explain how it works! [Give the students an example by taking them to first the “Google Cloud Essentials” quest and then the “Getting Started: Create and Manage Cloud Resources” skill badge]
• #34: Once you complete all the quests & the skill badges from Cloud Engineering track, this is what your profile would look like. You would have earned all of these amazing badges issued by Google Cloud which you can share with your network and add to your resume and professional profiles and showcase that you have the necessary skills and hands on practice on Google Cloud.
• #35: You need to complete the rest of the quests & skill badges remaining in the Data Science & Machine Learning track! You would have already completed one lab in the Perform Foundational Data, ML, and AI Tasks in Google Cloud quest in this session - do remember to complete the remaining labs immediately after the session while the knowledge is still fresh. Also, remember we talked about skill badges earlier. It’s really easy to earn one when you have already completed a quest. All you have to do is complete one more challenge lab after completing the quest related to that skill badge. Let us explain how it works! [Give the students an example by taking them to first the “Google Cloud Essentials” quest and then the “Getting Started: Create and Manage Cloud Resources” skill badge]
• #36: Once you complete all the quests & the skill badges from Data Science & Machine Learning track, this is what your profile would look like. You would have earned all of these amazing badges issued by Google Cloud which you can share with your network and add to your resume and professional profiles and showcase that you have the necessary skills and hands on practice on Google Cloud.
• #37: Share profile and pictures with your social/professional network. Use hashtag: #30DaysofGoogleCloud Do remember to follow us on Twitter to stay updated with all the latest information about Google Cloud.