This is the second of two Quests of hands-on labs derived from the exercises from the book Data Science on Google Cloud Platform by Valliappa Lakshmanan, published by O'Reilly Media, Inc. In this second Quest, covering chapter 9 through the end of the book, you extend the skills practiced in the first Quest, and run full-fledged machine learning jobs with state-of-the-art tools and real-world data sets, all using Google Cloud Platform tools and services.
In this quest, you will learn about Google Cloud’s IoT Core service and its integration with other services like GCS, Dataprep, Stackdriver and Firestore. The labs in this quest use simulator code to mimic IOT devices and the learning here should empower you to implement the same streaming pipeline with real world IoT devices.
This Quest of hands-on labs demonstrates the power of integrating Google Cloud Platform services and tools with G Suite applications. With integration technologies such as App Script and the Clasp Command Line environment, you will create and publish web apps and add-ons for G Suite products: Sheets, Docs, Forms, and Slides. With App Maker you will build a ready-to-use app that has a Google Cloud SQL Database, Google Maps integration, and a Mobile Responsive Design. Other labs create direct connections to GCP data sources-- using the BigQuery API, Sheets, and Slides to collect, analyze and present data.
Containerized applications have changed the game and are here to stay. With Kubernetes, you can orchestrate containers with ease, and integration with the Google Cloud Platform is seamless. In this advanced-level quest, you will be exposed to a wide range of Kubernetes use cases and will get hands-on practice architecting solutions over the course of 9 labs. From building Slackbots with NodeJS, to deploying game servers on clusters, to running the Cloud Vision API, Kubernetes Solutions will show you first-hand how agile and powerful this container orchestration system is.
C# has powered Windows .NET application development for nearly two decades and Google Cloud is committed to supporting developers getting their .NET workloads up and running on the GCP platform. In this quest, you will learn how to run C# apps in GCP, and specifically how to take your apps to the next level by interfacing them with the big data and machine learning APIs that are accessible now from C#. By enrolling in Developing Data and Machine Learning Apps with C# you will see firsthand how seamlessly GCP integrates with .NET workloads and what the possibilities are for leveraging big data and ML services in your own C# projects.
Security is an uncompromising feature of Google Cloud Platform services, and GCP has developed specific tools for ensuring safety and identity across your projects. In this fundamental-level quest, you will get hands-on practice with GCP’s Identity and Access Management (IAM) service, which is the go-to for managing user and virtual machine accounts. You will get experience with network security by provisioning VPCs and VPNs, and learn what tools are available for security threat and data loss protections.
In this Quest, the experienced user of Google Cloud will learn how to describe and launch cloud resources with Terraform, an open source tool that codifies APIs into declarative configuration files that can be shared amongst team members, treated as code, edited, reviewed, and versioned. In these nine hands-on labs, you will work with example templates and understand how to launch a range of configurations, from simple servers, through full load-balanced applications.
In this advanced-level quest, you will learn how you to harness serious GCP power and infrastructure. The hands-on labs will give you use cases, and you will be tasked with implementing scaling practices utilized by Google’s very own Solutions Architecture team. From developing enterprise grade load balancing and autoscaling, to building continuous delivery pipelines, Google Cloud Solutions I: Scaling your Infrastructure will teach you best practices for taking your GCP projects to the next level.
In this advanced-level quest, you will learn how to harness serious GCP computing power to run big data and machine learning jobs. The hands-on labs will give you use cases, and you will be tasked with implementing big data and machine learning practices utilized by Google’s very own Solutions Architecture team. From running Big Query analytics on tens of thousands of basketball games, to training TensorFlow image classifiers, you will quickly see why GCP is the go-to platform for running big data and machine learning jobs.
In this fundamental-level quest, you will learn the ins and outs of Stackdriver: an important GCP service for generating insights into applications’ health. Stackdriver provides a wealth of information in application monitoring, report logging, and diagnoses. The labs in this quest will give you hands-on practice with Stackdriver, and will teach you how to monitor virtual machines, generate logs and alerts, and create custom metrics for application data.