Getting Started with Google Kubernetes Engine

This one-week, accelerated online class equips students to containerize workloads in Docker containers, deploy them to Kubernetes clusters provided by Google Kubernetes Engine, and scale those workloads to handle increased traffic. Students also learn how to continuously deploy new code in a Kubernetes cluster to provide application updates. At the end of the course, you will be able to: • Understand container basics • Containerize an existing application • Understand Kubernetes concepts and principles • Deploy applications to Kubernetes using the CLI • Set up a continuous delivery pipeline using Jenkins • Locate more documentation and training. To get the most of out of this course, learners should have basic proficiency with command-line tools and Linux operating system environments, as well as Web server technologies such as Nginx. We also recommend systems operations experience, including deploying and managing applications, either on-premises or in a public cloud environment.

Who is this class for


This course is primarily aimed at application developers, cloud solutions architects, DevOps engineers, and IT managers. This course is also intended for individuals using Google Cloud Platform to create new solutions or to integrate existing systems, application environments, and infrastructure with the Google Cloud Platform.

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Syllabus

WEEK 1

Welcome to Getting Started with Google Kubernetes Engine

Welcome to the course.

2 videos

1. Video: Welcome to the Course
2. Video: Course Overview

Introduction to Containers and Docker

Acquaint yourself with containers, Docker, and the Google Container Registry.

3 videos

1. Video: What are Containers?
2. Video: What is Docker?
3. Video: Containers and Docker Summary

Graded: Qwiklabs – Introduction to Containers and Docker

Graded: Containers and Docker Quiz

Kubernetes Basics

Deploy an application with microservices in a Kubernetes cluster.

4 videos

1. Video: Clusters, Nodes, and Pods
2. Video: Services, Labels, and Selectors
3. Video: Volumes
4. Video: Kubernetes Basics Summary

Graded: Qwiklabs – Kubernetes Basics

Graded: Kubernetes Basics Quiz

Deploying to Kubernetes

Create and manage Kubernetes deployments.

3 videos

1. Video: Deployments and Rolling Updates
2. Video: Canary and Blue-Green Deployments
3. Video: Deploying to Kubernetes Summary

Graded: Qwiklabs – Deploying to Kubernetes

Graded: Deploying to Kubernetes Quiz

Creating a Continuous Delivery Pipeline

Build a continuous delivery pipeline.

3 videos

1. Video: Provisioning Jenkins
2. Video: Creating the Pipeline
3. Video: Continuous Deployment with Jenkins Summary

Graded: Qwiklabs – Continuous Deployment with Jenkins

Graded: Continuous Deployment with Jenkins Quiz

Level	Intermediate
Commitment	1 week of study, 5-7 hours/week
Language	English
Hardware Req	You'll need a desktop web browser to run this course's interactive labs via Qwiklabs.
How To Pass	Pass all graded assignments to complete the course.
User Ratings	Average User Rating 4.5

Google Cloud

We help millions of organizations empower their employees, serve their customers, and build what’s next for their businesses with innovative technology created in—and for—the cloud. Our products are engineered for security, reliability, and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping customers apply our technologies to create success.

Create an A/B test - Optimize Help

Create an A/B test

Follow these steps to create a simple A/B test.

An A/B test is a randomized experiment using two or more variants of the same web page (A and B). Variant A is the original and variant B through n each contain at least one element that is modified from the original.

In this article:

Before you can start testing, you need something to test on. Rather than randomly testing items on your homepage or adapting your checkout flow, start small. Test a change to a call-to-action (CTA), change the color of a button, or remove an extraneous form field. Once you're comfortable creating variants and experiments, you can expand the scope of your testing.

Find more idea starters in the Example experiments article.

Create a hypothesis

Before diving into your first experiment you should identify a problem and create a hypothesis that's backed up by data.

Start by identifying a problem that you want to solve. For example, have your conversions dropped off? Have traffic patterns changed? Have your demographics shifted? A close examination of trends in your Google Analytics behavior reports is a great place to start.

Once you've identified a problem, assemble a team within your organization and solicit their opinion about the cause of the problem. Use feedback from this cross-functional team to form your hypothesis, an educated guess that you'll validate or invalidate with experimentation.

Example hypothesis

Changing the color of the 'Add to cart' button from blue to green will increase revenue by 10 percent."

After you've identified a problem (low conversions), and worked with your team on a hypothesis (changing the button color) your ready to test your hypothesis on your website.

Create an A/B test

In this experiment, we’ll test changing the color of a button:

1. Go to your Optimize Account (Main menu > Accounts).
2. Click on your Container name to get to the Experiments page.
3. Click CREATE EXPERIMENT.
4. Enter an Experiment name (up to 255 characters).
5. Enter an Editor page URL (the web page you'd like to test).
6. Click A/B test.
7. Click CREATE.

Use a fully resolved URL in the Editor Page field. Redirects aren't supported here.

The variants card

The top of the experiment page includes the variants card. This where you'll create the specific changes to your web page that you wish to test, called variants. You can create as many variants as you wish to test against your original page (the Editor page in Optimize).

Create a variant

To get started, click CREATE VARIANT (bottom right), enter a variant name, then click ADD. Repeat this process to create additional variants. When finished, you'll see a list of your new variants on the variants card.

To start making changes. click anywhere in the variant row (which will say "0 changes"). This will launch the Optimize visual editor – an overlay on top of your editor page consisting of two components: the app bar (at the top of the page) and the editor panel (floating in the lower right).

Start editing:

1. Click on any web page element you wish to edit (e.g. a button).
2. Use the editor panel to make a change (e.g. change the button color).
3. Click SAVE.
4. Continue making edits as necessary.
5. Click DONE.

Learn more about the visual editor.

Variant weighting

All variants are weighted equally by default in Optimize. A visitor who is included in your experiment has an equal chance of seeing any of your variants. If you want to direct more, less, or even all of your traffic to a specific variant, you can adjust your variant weights on the experiment details page.

Learn more about variant weighting.

The configuration card

Objectives tab

Configure your experiment objectives on the OBJECTIVES tab:

1. Select a Google Analytics view.
2. Select a Primary Objective.
3. (Optional) Click + ADD AN OBJECTIVE to add an objective. Optimize users can use up to three pre-selected objectives per experiment and see data for those objectives in Optimize reports. Optimize 360 customers can select additional objectives and view additional goals.
4. Add a description and hypothesis.
5. Click SAVE.

Targeting tab

Configure who and when to target on the TARGETING tab.

Who to target

The Who section of the Targeting tab is used to select the visitors to whom you wish to target your variant. Enter a numeric value (in tenths of a percent) or use the slider to specify the percentage of your visitors to include in your experiment.

When to target

The When section of the Targeting tab determines where the experiment is shown. When is evaluated each time a user visits the experiment page. Use the rules to set where your experiment is seen. To create a targeting rule, click AND, then select a targeting rule type.

You must create either a URL or path targeting rule to start your experiment. The quickest way to get started is to create a URL matching rule with the same Editor page URL that you used when creating your experiment.

Targeting rules

Optimize includes the following targeting rule types, which you can read more about in the following articles:

Learn more about targeting.

Start your experiment

Click START EXPERIMENT. When the status field says "running," your experiment is live on the web. Most updates happen within a minute.

How long should your experiment run?

Keep an experiment running until at least one of these conditions has been met:

1. Two weeks have passed, to account for cyclical variations in web traffic during the week.
2. At least one variant has a 95 percent probability to beat baseline.

Reports

To monitor a running experiment or see the results of a concluded experiment, click the Reporting tab at the top of the experiment detail page. The report is broken down into a series of cards that contain data about your experiment, including its status and how your variants perform against your objectives.

In addition to the reports included in Optimize, you can also see Optimize reports in Google Analytics. Sign in to Google Analytics, select the Reporting tab and select Behavior > Experiments in the report navigation. Learn more about Optimize reports.

Related resources

Measure

Measure

Cinematique Review: ‘The Ashram’

WHOAH DUDE: White Castle is in Kal Penn's rearview.

Haunted by a vision of a vanished girlfriend, Jamie (Sam Keeley) searches the remote foothills of the Himalayas for her, lured by various clues to a guru with a glorious wooden palace in the pines.

Chandra (Melissa Leo) the blissed-out disciple of the guru claims no knowledge of the lady in question. Then Jamie starts to get clues from Nitin (Kal Penn) that someone isn’t telling the truth. Mountain View-bred filmmaker Ben Rekhi cooks up haunting atmosphere that dispels when the story changes course in the third act—what starts out like Laura ends up as Doctor Strange, and, worse, The Celestine Prophecy.

Still: Penn’s explanation of why he decides to help—“It’s the right thing to do”—is the kind of straightforward exposition that filmmakers too often avoid. The scenery is splendid, and it’s always worthwhile to see this much of the Oscar-winning Leo (The Fighter); usually responsible for ten or fifteen great minutes in a movie, Leo gets a chance to be alternately warm and ominous.

IMDB: The Ashram

Alchemical Banality: Achieving and Undermining the Avant Garde

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