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*Can enable the Horizontal Pod Autoscaler for my applications
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*Can add Blue/Green deployment method into pipeline
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*Can run a split test on OpenShift with GitOps - splitting traffic between two tests
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#### 🧑🏫 Learning Outcomes 🧑💻
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As a learner, I can perform the following tasks by the end of this exercise:
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*Enable the Horizontal Pod Autoscaler for my applications
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*Add a Blue/Green deployment method to the pipeline
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*Run a split test on OpenShift with GitOps - splitting traffic between two tests
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<!--
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--->
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#### What is it?
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Advanced Deployments 🚚📦📦📦👷
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* Autoscaling - horizontally scale applications based on observed metrics
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* Blue / Green Deployments - release, validate and switch
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* A/B Deployments - great for testing new features in small groups. One release with traffic routing between each
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* Autoscaling - horizontally scale applications based on observed metrics.
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* Blue / Green Deployments - release, validate and switch.
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* A/B Deployments - great for testing new features in small groups. One release with traffic routing between each.
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* Don't forget to take measures!!
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* Canary Release - Like a canary in the mines ... Exposing a small number of real users to a new version
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* Feature Toggling (Flags) - modifying system behaviours without changing the code
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* Canary Release - Like a canary in the mines ... Exposing a small number of real users to a new version.
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* Feature Toggling (Flags) - modifying system behaviors without changing the code.
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@@ -62,24 +62,24 @@ Advanced Deployments 🚚📦📦📦👷
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#### Design of Experiments
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All our ideas about new products, new services, new feature, any changes we can introduce to make things better (growth, revenue, experience, etc.) start as an idea, a hypothesis, an assumption.
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All our ideas about new products, new services, new features, and changes that we can introduce to make things better (growth, revenue, experience, etc.) start as an idea, a hypothesis, or an assumption.
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The Design of Experiments is the practice we use to turn ideas, hypothesis and/or assumptions into a concrete welldefined set of experiments which can be carried out in order to validate those ideas, hypothesis and assumptions, i.e. provide us with valuable learning.
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The Design of Experiments is the practice we use to turn ideas, hypotheses, and assumptions into a concrete, well-defined set of experiments that can validate those ideas, hypotheses, and assumptions, providing valuable learning.
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#### Why do it?
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Design of Experiments...
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*is a fail safe way to advance a solution and learn fast
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*can provide a quick way to evolve a product
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*helps drive innovation in existing as well as new products
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*enables autonomous team to deliver on leadership intent by placing small bets
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*is essential for realising the Build-Measure-Learn loop
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Design of Experiments
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*Is a fail safe way to advance a solution and learn fast
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*Can provide a quick way to evolve a product
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*Helps drive innovation in new and existing products
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*Enables an autonomous team to deliver on leadership intent by placing small bets
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*Is essential to realizing the Build-Measure-Learn loop
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#### Split testing
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Using the platform to support running an experiment
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#### Split Testing
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Using the platform to support running an experiment.
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@@ -93,22 +93,23 @@ Using the platform to support running an experiment
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### 💥 Prioritize and Extend your Kanban 💥 <!-- .element: class="title-bottom-left" -->
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### 💥 Prioritize and extend your Kanban 💥 <!-- .element: class="title-bottom-left" -->
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