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Lab Overview

This repository contains hands-on labs that help you practice and build skills associated with Intel® Certified Developer – MLOps Professional Certification exam.

Whether you are deploying an AI project into production or adding AI to an existing application, building a performant and scalable machine learning operations (MLOps) environment is crucial to maximizing your resources. This curriculum teaches you to incorporate compute awareness into the AI solution design process to maximize performance across the AI pipeline.

Table of Contents

Lab 1: Building REST API Endpoints with FastAPI

Lab 2: Practice creating Architecture diagrams from Application Specs

Lab 3: Implementing Model Development Components with MLflow

Lab 4: Building an Inference Endpoint using FastAPI

Lab 5: Intel Deep Learning Optimizations

Lab 6: Hugging Face LLM Inference

Lab 7: Optimizing the Full Stack with OneAPI

Lab 8: Retrieval Augmented Generation with PyTorch 2.0 and LangChain

Lab 9: Your First Open Source Contribution

NOTE: This code is educational in nature and should not be used in production.

About

Official repository of the Intel Certified Developer Program

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aimachinelearningmlops

Resources

Readme
BSD-3-Clause license

Code of conduct

Code of conduct

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Contributing

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Security policy
Activity
Custom properties

Stars

87 stars

Watchers

1 watching

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54 forks
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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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Lab Overview
This repository contains hands-on labs that help you practice and build skills associated with Intel® Certified Developer – MLOps Professional Certification exam.
Whether you are deploying an AI project into production or adding AI to an existing application, building a performant and scalable machine learning operations (MLOps) environment is crucial to maximizing your resources. This curriculum teaches you to incorporate compute awareness into the AI solution design process to maximize performance across the AI pipeline.
Table of Contents
Lab 1: Building REST API Endpoints with FastAPI
Lab 2: Practice creating Architecture diagrams from Application Specs
Lab 3: Implementing Model Development Components with MLflow
Lab 4: Building an Inference Endpoint using FastAPI
Lab 5: Intel Deep Learning Optimizations
Lab 6: Hugging Face LLM Inference
Lab 7: Optimizing the Full Stack with OneAPI
Lab 8: Retrieval Augmented Generation with PyTorch 2.0 and LangChain
Lab 9: Your First Open Source Contribution
NOTE: This code is educational in nature and should not be used in production.
About
Official repository of the Intel Certified Developer Program
Topics
aimachinelearningmlops
Resources
Readme
BSD-3-Clause license
Code of conduct
Code of conduct
Contributing
Contributing
Security policy
Security policy
Activity
Custom properties
Stars
87 stars
Watchers
1 watching
Forks
54 forks
Report repository
Used by
Contributors
Languages

, '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('^' + ".*" + '

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This repository was archived by the owner on Jul 25, 2025. It is now read-only.

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Lab Overview
This repository contains hands-on labs that help you practice and build skills associated with Intel® Certified Developer – MLOps Professional Certification exam.
Whether you are deploying an AI project into production or adding AI to an existing application, building a performant and scalable machine learning operations (MLOps) environment is crucial to maximizing your resources. This curriculum teaches you to incorporate compute awareness into the AI solution design process to maximize performance across the AI pipeline.
Table of Contents
Lab 1: Building REST API Endpoints with FastAPI
Lab 2: Practice creating Architecture diagrams from Application Specs
Lab 3: Implementing Model Development Components with MLflow
Lab 4: Building an Inference Endpoint using FastAPI
Lab 5: Intel Deep Learning Optimizations
Lab 6: Hugging Face LLM Inference
Lab 7: Optimizing the Full Stack with OneAPI
Lab 8: Retrieval Augmented Generation with PyTorch 2.0 and LangChain
Lab 9: Your First Open Source Contribution
NOTE: This code is educational in nature and should not be used in production.
About
Official repository of the Intel Certified Developer Program
Topics
aimachinelearningmlops
Resources
Readme
BSD-3-Clause license
Code of conduct
Code of conduct
Contributing
Contributing
Security policy
Security policy
Activity
Custom properties
Stars
87 stars
Watchers
1 watching
Forks
54 forks
Report repository
Used by
Contributors
Languages

, '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

This repository was archived by the owner on Jul 25, 2025. It is now read-only.

BranchesTags
Repository files navigation
Lab Overview
This repository contains hands-on labs that help you practice and build skills associated with Intel® Certified Developer – MLOps Professional Certification exam.
Whether you are deploying an AI project into production or adding AI to an existing application, building a performant and scalable machine learning operations (MLOps) environment is crucial to maximizing your resources. This curriculum teaches you to incorporate compute awareness into the AI solution design process to maximize performance across the AI pipeline.
Table of Contents
Lab 1: Building REST API Endpoints with FastAPI
Lab 2: Practice creating Architecture diagrams from Application Specs
Lab 3: Implementing Model Development Components with MLflow
Lab 4: Building an Inference Endpoint using FastAPI
Lab 5: Intel Deep Learning Optimizations
Lab 6: Hugging Face LLM Inference
Lab 7: Optimizing the Full Stack with OneAPI
Lab 8: Retrieval Augmented Generation with PyTorch 2.0 and LangChain
Lab 9: Your First Open Source Contribution
NOTE: This code is educational in nature and should not be used in production.
About
Official repository of the Intel Certified Developer Program
Topics
aimachinelearningmlops
Resources
Readme
BSD-3-Clause license
Code of conduct
Code of conduct
Contributing
Contributing
Security policy
Security policy
Activity
Custom properties
Stars
87 stars
Watchers
1 watching
Forks
54 forks
Report repository
Used by
Contributors
Languages

, '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

This repository was archived by the owner on Jul 25, 2025. It is now read-only.

BranchesTags
Repository files navigation
Lab Overview
This repository contains hands-on labs that help you practice and build skills associated with Intel® Certified Developer – MLOps Professional Certification exam.
Whether you are deploying an AI project into production or adding AI to an existing application, building a performant and scalable machine learning operations (MLOps) environment is crucial to maximizing your resources. This curriculum teaches you to incorporate compute awareness into the AI solution design process to maximize performance across the AI pipeline.
Table of Contents
Lab 1: Building REST API Endpoints with FastAPI
Lab 2: Practice creating Architecture diagrams from Application Specs
Lab 3: Implementing Model Development Components with MLflow
Lab 4: Building an Inference Endpoint using FastAPI
Lab 5: Intel Deep Learning Optimizations
Lab 6: Hugging Face LLM Inference
Lab 7: Optimizing the Full Stack with OneAPI
Lab 8: Retrieval Augmented Generation with PyTorch 2.0 and LangChain
Lab 9: Your First Open Source Contribution
NOTE: This code is educational in nature and should not be used in production.
About
Official repository of the Intel Certified Developer Program
Topics
aimachinelearningmlops
Resources
Readme
BSD-3-Clause license
Code of conduct
Code of conduct
Contributing
Contributing
Security policy
Security policy
Activity
Custom properties
Stars
87 stars
Watchers
1 watching
Forks
54 forks
Report repository
Used by
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

This repository was archived by the owner on Jul 25, 2025. It is now read-only.

BranchesTags
Repository files navigation
Lab Overview
This repository contains hands-on labs that help you practice and build skills associated with Intel® Certified Developer – MLOps Professional Certification exam.
Whether you are deploying an AI project into production or adding AI to an existing application, building a performant and scalable machine learning operations (MLOps) environment is crucial to maximizing your resources. This curriculum teaches you to incorporate compute awareness into the AI solution design process to maximize performance across the AI pipeline.
Table of Contents
Lab 1: Building REST API Endpoints with FastAPI
Lab 2: Practice creating Architecture diagrams from Application Specs
Lab 3: Implementing Model Development Components with MLflow
Lab 4: Building an Inference Endpoint using FastAPI
Lab 5: Intel Deep Learning Optimizations
Lab 6: Hugging Face LLM Inference
Lab 7: Optimizing the Full Stack with OneAPI
Lab 8: Retrieval Augmented Generation with PyTorch 2.0 and LangChain
Lab 9: Your First Open Source Contribution
NOTE: This code is educational in nature and should not be used in production.
About
Official repository of the Intel Certified Developer Program
Topics
aimachinelearningmlops
Resources
Readme
BSD-3-Clause license
Code of conduct
Code of conduct
Contributing
Contributing
Security policy
Security policy
Activity
Custom properties
Stars
87 stars
Watchers
1 watching
Forks
54 forks
Report repository
Used by
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

This repository was archived by the owner on Jul 25, 2025. It is now read-only.

BranchesTags
Repository files navigation
Lab Overview
This repository contains hands-on labs that help you practice and build skills associated with Intel® Certified Developer – MLOps Professional Certification exam.
Whether you are deploying an AI project into production or adding AI to an existing application, building a performant and scalable machine learning operations (MLOps) environment is crucial to maximizing your resources. This curriculum teaches you to incorporate compute awareness into the AI solution design process to maximize performance across the AI pipeline.
Table of Contents
Lab 1: Building REST API Endpoints with FastAPI
Lab 2: Practice creating Architecture diagrams from Application Specs
Lab 3: Implementing Model Development Components with MLflow
Lab 4: Building an Inference Endpoint using FastAPI
Lab 5: Intel Deep Learning Optimizations
Lab 6: Hugging Face LLM Inference
Lab 7: Optimizing the Full Stack with OneAPI
Lab 8: Retrieval Augmented Generation with PyTorch 2.0 and LangChain
Lab 9: Your First Open Source Contribution
NOTE: This code is educational in nature and should not be used in production.
About
Official repository of the Intel Certified Developer Program
Topics
aimachinelearningmlops
Resources
Readme
BSD-3-Clause license
Code of conduct
Code of conduct
Contributing
Contributing
Security policy
Security policy
Activity
Custom properties
Stars
87 stars
Watchers
1 watching
Forks
54 forks
Report repository
Used by
Contributors
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

This repository was archived by the owner on Jul 25, 2025. It is now read-only.

BranchesTags
Repository files navigation
Lab Overview
This repository contains hands-on labs that help you practice and build skills associated with Intel® Certified Developer – MLOps Professional Certification exam.
Whether you are deploying an AI project into production or adding AI to an existing application, building a performant and scalable machine learning operations (MLOps) environment is crucial to maximizing your resources. This curriculum teaches you to incorporate compute awareness into the AI solution design process to maximize performance across the AI pipeline.
Table of Contents
Lab 1: Building REST API Endpoints with FastAPI
Lab 2: Practice creating Architecture diagrams from Application Specs
Lab 3: Implementing Model Development Components with MLflow
Lab 4: Building an Inference Endpoint using FastAPI
Lab 5: Intel Deep Learning Optimizations
Lab 6: Hugging Face LLM Inference
Lab 7: Optimizing the Full Stack with OneAPI
Lab 8: Retrieval Augmented Generation with PyTorch 2.0 and LangChain
Lab 9: Your First Open Source Contribution
NOTE: This code is educational in nature and should not be used in production.
About
Official repository of the Intel Certified Developer Program
Topics
aimachinelearningmlops
Resources
Readme
BSD-3-Clause license
Code of conduct
Code of conduct
Contributing
Contributing
Security policy
Security policy
Activity
Custom properties
Stars
87 stars
Watchers
1 watching
Forks
54 forks
Report repository
Used by
Contributors
Languages