Learn how to set up Jira Software Cloud and integrate it with other products and applications.
Learn how to configure your Jira Software Cloud company-managed projects to suit your agile development processes.
Learn how to create, search, and work with issues in software projects, manage your profile, and more.
Learn how to get started, enable features, and manage and administer team-managed projects.
Create powerful rules to start automating your manual, repetitive processes.
Plan and view work across multiple teams, projects, and releases with Advanced Roadmaps.
A guide on how to deploy and monitor an application built on AWS using Atlassian and third-party tools.
To demonstrate how to develop, deploy, and manage applications using Jira Software and various connected tools, our team created ImageLabeller, a simple demo application built on AWS that uses machine learning to apply labels to images.
This page covers how to setup an AWS SageMaker predefined model, a prerequisite for ImageLabeller to successfully process images. Before you begin, we recommend reading the ImageLabeller architecture for context.
Log in to AWS Console and navigate to Amazon SageMaker.
Go to Amazon SageMaker Studio.
Choose Quick start > Execution role > Create an IAM role.
Click Create role. Then click Submit.
SageMaker will take a while to setup. Once it’s ready, click Open Studio.
Click Go to SageMake JumpStart.
Locate, and click on Inception V3.
Change the Machine Type to ml.m5.large, change the Endpoint Name to something more readable like “image-labeller-endpoint”, and click Deploy.
AWS SageMaker will start to deploy the model.
Click Open Notebook when the deployment is finished.
Run all three of the notebook code blocks in order to verify things are working. Make a note of the endpoint_name in query_endpoint. You need to add this, and the region your AWS SageMaker notebook is in to the InvokeLabeller AWS Lambda.
Open InvokeLabeller’s src/app.py file and look for query_endpoint. change the endpoint_name, and client region_name to match your AWS SageMaker notebook.
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