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MLOps for SageMaker Endpoint Deployment

This is a sample code repository for demonstrating how you can organize your code for deploying an realtime inference Endpoint infrastructure. This code repository is created as part of creating a Project in SageMaker.

This code repository has the code to find the latest approved ModelPackage for the associated ModelPackageGroup and automaticaly deploy it to the Endpoint on detecting a change (build.py). This code repository also defines the CloudFormation template which defines the Endpoints as infrastructure. It also has configuration files associated with staging and prod stages.

Upon triggering a deployment, the CodePipeline pipeline will deploy 2 Endpoints - staging and prod. After the first deployment is completed, the CodePipeline waits for a manual approval step for promotion to the prod stage. You will need to go to CodePipeline AWS Managed Console to complete this step.

You own this code and you can modify this template to change as you need it, add additional tests for your custom validation.

A description of some of the artifacts is provided below:

Layout of the SageMaker ModelBuild Project Template

jenkins/seed_job.groovy

  • this file is used to create Jenkins pipeline using Jenkinsfile.

jenkins/Jenkinsfile

  • this file contains pipeline definition for stages in Jenkins pipeline, and you can modify it to add/delete/update stages in the pipeline.

build.py

  • this python file contains code to get the latest approve package arn and exports staging and configuration files. This is invoked from the Build stage.

endpoint-config-template.yml

  • this CloudFormation template file is packaged by the build step in the CodePipeline and is deployed in different stages.

staging-config.json

  • this configuration file is used to customize staging stage in the pipeline. You can configure the instance type, instance count here.

prod-config.json

  • this configuration file is used to customize prod stage in the pipeline. You can configure the instance type, instance count here.

test\buildspec.yml

  • this file is used by the CodePipeline's staging stage to run the test code of the following python file

test\test.py

  • this python file contains code to describe and invoke the staging endpoint. You can customize to add more tests here.

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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MLOps for SageMaker Endpoint Deployment
This is a sample code repository for demonstrating how you can organize your code for deploying an realtime inference Endpoint infrastructure. This code repository is created as part of creating a Project in SageMaker.
This code repository has the code to find the latest approved ModelPackage for the associated ModelPackageGroup and automaticaly deploy it to the Endpoint on detecting a change (build.py). This code repository also defines the CloudFormation template which defines the Endpoints as infrastructure. It also has configuration files associated with staging and prod stages.
Upon triggering a deployment, the CodePipeline pipeline will deploy 2 Endpoints - staging and prod. After the first deployment is completed, the CodePipeline waits for a manual approval step for promotion to the prod stage. You will need to go to CodePipeline AWS Managed Console to complete this step.
You own this code and you can modify this template to change as you need it, add additional tests for your custom validation.
A description of some of the artifacts is provided below:
Layout of the SageMaker ModelBuild Project Template
jenkins/seed_job.groovy
  • this file is used to create Jenkins pipeline using Jenkinsfile.
jenkins/Jenkinsfile
  • this file contains pipeline definition for stages in Jenkins pipeline, and you can modify it to add/delete/update stages in the pipeline.
build.py
  • this python file contains code to get the latest approve package arn and exports staging and configuration files. This is invoked from the Build stage.
endpoint-config-template.yml
  • this CloudFormation template file is packaged by the build step in the CodePipeline and is deployed in different stages.
staging-config.json
  • this configuration file is used to customize staging stage in the pipeline. You can configure the instance type, instance count here.
prod-config.json
  • this configuration file is used to customize prod stage in the pipeline. You can configure the instance type, instance count here.
test\buildspec.yml
  • this file is used by the CodePipeline's staging stage to run the test code of the following python file
test\test.py
  • this python file contains code to describe and invoke the staging endpoint. You can customize to add more tests here.
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
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MLOps for SageMaker Endpoint Deployment
This is a sample code repository for demonstrating how you can organize your code for deploying an realtime inference Endpoint infrastructure. This code repository is created as part of creating a Project in SageMaker.
This code repository has the code to find the latest approved ModelPackage for the associated ModelPackageGroup and automaticaly deploy it to the Endpoint on detecting a change (build.py). This code repository also defines the CloudFormation template which defines the Endpoints as infrastructure. It also has configuration files associated with staging and prod stages.
Upon triggering a deployment, the CodePipeline pipeline will deploy 2 Endpoints - staging and prod. After the first deployment is completed, the CodePipeline waits for a manual approval step for promotion to the prod stage. You will need to go to CodePipeline AWS Managed Console to complete this step.
You own this code and you can modify this template to change as you need it, add additional tests for your custom validation.
A description of some of the artifacts is provided below:
Layout of the SageMaker ModelBuild Project Template
jenkins/seed_job.groovy
  • this file is used to create Jenkins pipeline using Jenkinsfile.
jenkins/Jenkinsfile
  • this file contains pipeline definition for stages in Jenkins pipeline, and you can modify it to add/delete/update stages in the pipeline.
build.py
  • this python file contains code to get the latest approve package arn and exports staging and configuration files. This is invoked from the Build stage.
endpoint-config-template.yml
  • this CloudFormation template file is packaged by the build step in the CodePipeline and is deployed in different stages.
staging-config.json
  • this configuration file is used to customize staging stage in the pipeline. You can configure the instance type, instance count here.
prod-config.json
  • this configuration file is used to customize prod stage in the pipeline. You can configure the instance type, instance count here.
test\buildspec.yml
  • this file is used by the CodePipeline's staging stage to run the test code of the following python file
test\test.py
  • this python file contains code to describe and invoke the staging endpoint. You can customize to add more tests here.
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
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MLOps for SageMaker Endpoint Deployment
This is a sample code repository for demonstrating how you can organize your code for deploying an realtime inference Endpoint infrastructure. This code repository is created as part of creating a Project in SageMaker.
This code repository has the code to find the latest approved ModelPackage for the associated ModelPackageGroup and automaticaly deploy it to the Endpoint on detecting a change (build.py). This code repository also defines the CloudFormation template which defines the Endpoints as infrastructure. It also has configuration files associated with staging and prod stages.
Upon triggering a deployment, the CodePipeline pipeline will deploy 2 Endpoints - staging and prod. After the first deployment is completed, the CodePipeline waits for a manual approval step for promotion to the prod stage. You will need to go to CodePipeline AWS Managed Console to complete this step.
You own this code and you can modify this template to change as you need it, add additional tests for your custom validation.
A description of some of the artifacts is provided below:
Layout of the SageMaker ModelBuild Project Template
jenkins/seed_job.groovy
  • this file is used to create Jenkins pipeline using Jenkinsfile.
jenkins/Jenkinsfile
  • this file contains pipeline definition for stages in Jenkins pipeline, and you can modify it to add/delete/update stages in the pipeline.
build.py
  • this python file contains code to get the latest approve package arn and exports staging and configuration files. This is invoked from the Build stage.
endpoint-config-template.yml
  • this CloudFormation template file is packaged by the build step in the CodePipeline and is deployed in different stages.
staging-config.json
  • this configuration file is used to customize staging stage in the pipeline. You can configure the instance type, instance count here.
prod-config.json
  • this configuration file is used to customize prod stage in the pipeline. You can configure the instance type, instance count here.
test\buildspec.yml
  • this file is used by the CodePipeline's staging stage to run the test code of the following python file
test\test.py
  • this python file contains code to describe and invoke the staging endpoint. You can customize to add more tests here.
About
No description, website, or topics provided.
Resources
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1 watching
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0 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
Repository files navigation
MLOps for SageMaker Endpoint Deployment
This is a sample code repository for demonstrating how you can organize your code for deploying an realtime inference Endpoint infrastructure. This code repository is created as part of creating a Project in SageMaker.
This code repository has the code to find the latest approved ModelPackage for the associated ModelPackageGroup and automaticaly deploy it to the Endpoint on detecting a change (build.py). This code repository also defines the CloudFormation template which defines the Endpoints as infrastructure. It also has configuration files associated with staging and prod stages.
Upon triggering a deployment, the CodePipeline pipeline will deploy 2 Endpoints - staging and prod. After the first deployment is completed, the CodePipeline waits for a manual approval step for promotion to the prod stage. You will need to go to CodePipeline AWS Managed Console to complete this step.
You own this code and you can modify this template to change as you need it, add additional tests for your custom validation.
A description of some of the artifacts is provided below:
Layout of the SageMaker ModelBuild Project Template
jenkins/seed_job.groovy
  • this file is used to create Jenkins pipeline using Jenkinsfile.
jenkins/Jenkinsfile
  • this file contains pipeline definition for stages in Jenkins pipeline, and you can modify it to add/delete/update stages in the pipeline.
build.py
  • this python file contains code to get the latest approve package arn and exports staging and configuration files. This is invoked from the Build stage.
endpoint-config-template.yml
  • this CloudFormation template file is packaged by the build step in the CodePipeline and is deployed in different stages.
staging-config.json
  • this configuration file is used to customize staging stage in the pipeline. You can configure the instance type, instance count here.
prod-config.json
  • this configuration file is used to customize prod stage in the pipeline. You can configure the instance type, instance count here.
test\buildspec.yml
  • this file is used by the CodePipeline's staging stage to run the test code of the following python file
test\test.py
  • this python file contains code to describe and invoke the staging endpoint. You can customize to add more tests here.
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
MLOps for SageMaker Endpoint Deployment
This is a sample code repository for demonstrating how you can organize your code for deploying an realtime inference Endpoint infrastructure. This code repository is created as part of creating a Project in SageMaker.
This code repository has the code to find the latest approved ModelPackage for the associated ModelPackageGroup and automaticaly deploy it to the Endpoint on detecting a change (build.py). This code repository also defines the CloudFormation template which defines the Endpoints as infrastructure. It also has configuration files associated with staging and prod stages.
Upon triggering a deployment, the CodePipeline pipeline will deploy 2 Endpoints - staging and prod. After the first deployment is completed, the CodePipeline waits for a manual approval step for promotion to the prod stage. You will need to go to CodePipeline AWS Managed Console to complete this step.
You own this code and you can modify this template to change as you need it, add additional tests for your custom validation.
A description of some of the artifacts is provided below:
Layout of the SageMaker ModelBuild Project Template
jenkins/seed_job.groovy
  • this file is used to create Jenkins pipeline using Jenkinsfile.
jenkins/Jenkinsfile
  • this file contains pipeline definition for stages in Jenkins pipeline, and you can modify it to add/delete/update stages in the pipeline.
build.py
  • this python file contains code to get the latest approve package arn and exports staging and configuration files. This is invoked from the Build stage.
endpoint-config-template.yml
  • this CloudFormation template file is packaged by the build step in the CodePipeline and is deployed in different stages.
staging-config.json
  • this configuration file is used to customize staging stage in the pipeline. You can configure the instance type, instance count here.
prod-config.json
  • this configuration file is used to customize prod stage in the pipeline. You can configure the instance type, instance count here.
test\buildspec.yml
  • this file is used by the CodePipeline's staging stage to run the test code of the following python file
test\test.py
  • this python file contains code to describe and invoke the staging endpoint. You can customize to add more tests here.
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
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Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
MLOps for SageMaker Endpoint Deployment
This is a sample code repository for demonstrating how you can organize your code for deploying an realtime inference Endpoint infrastructure. This code repository is created as part of creating a Project in SageMaker.
This code repository has the code to find the latest approved ModelPackage for the associated ModelPackageGroup and automaticaly deploy it to the Endpoint on detecting a change (build.py). This code repository also defines the CloudFormation template which defines the Endpoints as infrastructure. It also has configuration files associated with staging and prod stages.
Upon triggering a deployment, the CodePipeline pipeline will deploy 2 Endpoints - staging and prod. After the first deployment is completed, the CodePipeline waits for a manual approval step for promotion to the prod stage. You will need to go to CodePipeline AWS Managed Console to complete this step.
You own this code and you can modify this template to change as you need it, add additional tests for your custom validation.
A description of some of the artifacts is provided below:
Layout of the SageMaker ModelBuild Project Template
jenkins/seed_job.groovy
  • this file is used to create Jenkins pipeline using Jenkinsfile.
jenkins/Jenkinsfile
  • this file contains pipeline definition for stages in Jenkins pipeline, and you can modify it to add/delete/update stages in the pipeline.
build.py
  • this python file contains code to get the latest approve package arn and exports staging and configuration files. This is invoked from the Build stage.
endpoint-config-template.yml
  • this CloudFormation template file is packaged by the build step in the CodePipeline and is deployed in different stages.
staging-config.json
  • this configuration file is used to customize staging stage in the pipeline. You can configure the instance type, instance count here.
prod-config.json
  • this configuration file is used to customize prod stage in the pipeline. You can configure the instance type, instance count here.
test\buildspec.yml
  • this file is used by the CodePipeline's staging stage to run the test code of the following python file
test\test.py
  • this python file contains code to describe and invoke the staging endpoint. You can customize to add more tests here.
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
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Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
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MLOps for SageMaker Endpoint Deployment
This is a sample code repository for demonstrating how you can organize your code for deploying an realtime inference Endpoint infrastructure. This code repository is created as part of creating a Project in SageMaker.
This code repository has the code to find the latest approved ModelPackage for the associated ModelPackageGroup and automaticaly deploy it to the Endpoint on detecting a change (build.py). This code repository also defines the CloudFormation template which defines the Endpoints as infrastructure. It also has configuration files associated with staging and prod stages.
Upon triggering a deployment, the CodePipeline pipeline will deploy 2 Endpoints - staging and prod. After the first deployment is completed, the CodePipeline waits for a manual approval step for promotion to the prod stage. You will need to go to CodePipeline AWS Managed Console to complete this step.
You own this code and you can modify this template to change as you need it, add additional tests for your custom validation.
A description of some of the artifacts is provided below:
Layout of the SageMaker ModelBuild Project Template
jenkins/seed_job.groovy
  • this file is used to create Jenkins pipeline using Jenkinsfile.
jenkins/Jenkinsfile
  • this file contains pipeline definition for stages in Jenkins pipeline, and you can modify it to add/delete/update stages in the pipeline.
build.py
  • this python file contains code to get the latest approve package arn and exports staging and configuration files. This is invoked from the Build stage.
endpoint-config-template.yml
  • this CloudFormation template file is packaged by the build step in the CodePipeline and is deployed in different stages.
staging-config.json
  • this configuration file is used to customize staging stage in the pipeline. You can configure the instance type, instance count here.
prod-config.json
  • this configuration file is used to customize prod stage in the pipeline. You can configure the instance type, instance count here.
test\buildspec.yml
  • this file is used by the CodePipeline's staging stage to run the test code of the following python file
test\test.py
  • this python file contains code to describe and invoke the staging endpoint. You can customize to add more tests here.
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