Get a $120K to $250K+ Job in Azure AI, ML, Gen AI & Agentic AI.

A hands-on program where you build real things with Azure AI/ML - designed for architects, developers/builders, and engineers who learn by doing it, not by watching slides.

12 modules  ·  70+ hands-on labs  ·  6 certifications  ·  On-Job Support

  • 46,000+ professionals trained
  • $120K–$250K+ salary range
  • 16 real-world Azure projects
  • 6 certifications

Our coaches and alumni work at these companies

JPMorganChase
Capgemini
PwC
BNY Mellon
The Complete Program at a Glance

Everything you get, on one map.

Beginner courses, 12 modules and 70+ hands-on labs, 6 certifications including Python for AI/ML, 16 real-world projects, and full job support - the entire roadmap in a single view. Tap the image to zoom in.

The complete Azure AI/ML, GenAI and Agentic AI program roadmap at a glance - beginner courses, modules, labs, certifications, projects, and job support

Opens the full-size map in a new tab

Limited seats - live cohort

Next Batch Starts In

24 October 2026 | 7:00 AM - 10:00 AM PST
00Days
:
00Hours
:
00Minutes
:
00Seconds
PST: 7:00 AM - 10:00 AMEST: 10:00 AM - 1:00 PMGMT: 2:00 PM - 5:00 PMIST: 7:30 PM - 10:30 PM
The batch has started. Enrollment is now open.
Verified reviews

What our learners say.

Rated 4.6/5 on Trustpilot · 267+ reviews
Azure & Agentic AI career roadmap

12 modules. Three layers for everyone.
Then you choose your path.

The first three layers are mandatory for all learners - they build your cloud, AI and Python foundations. After that, pick the specialist track that matches your role and ambition. Tap any module to see every lesson and lab inside it.

Layer 1 - Common foundation for everyone
For everyone

Cloud for Beginners

What is cloud computing and why it matters - and 8 more core concepts.

Modules covered
  • What is cloud computing and why it matters
  • IaaS, PaaS, SaaS explained simply
  • AWS, Azure, GCP - what each is good for
  • Core cloud concepts: regions, availability zones, scalability
  • Cloud pricing models and cost basics
  • Lab: Create an AWS Free Tier Account
  • Lab: Create an Azure Free Tier Account
  • Lab: Install Visual Studio Code, Python, Git on a Cloud VM
  • Lab: Install GitHub Copilot with VS Code
For everyone

Azure for Beginners

Azure portal, subscriptions, and resource groups - and 11 more core concepts.

Modules covered
  • Azure portal, subscriptions, and resource groups
  • Core Azure services: Compute, Storage, Networking, Databases
  • Azure Active Directory and identity basics
  • Setting up your first Azure environment
  • Azure governance: policies, blueprints, management groups
  • Lab: Register Azure Free Trial Account
  • Lab: Create Budget & Billing Alerts
  • Lab: Create a Windows VM (Quick Start)
  • Lab: Troubleshooting - Connect to VM on Cloud
  • Lab: Open an Azure Support Request
  • Lab: Requesting Azure Service Quota Increases
  • Lab: Billing Q&A - Identify and Fix Storage Bill
For everyone

AI, ML and GenAI for Beginners

What is AI, Machine Learning, and Deep Learning? - and 10 more core concepts.

Modules covered
  • What is AI, Machine Learning, and Deep Learning?
  • Generative AI and Large Language Models explained
  • Real-world AI use cases across industries
  • Introduction to Microsoft Foundry and Azure AI services
  • How models learn: training, inference, and evaluation
  • Lab: Create API Keys for OpenAI GPT LLM Model
  • Lab: Create API Key for Anthropic (Claude) LLM Model
  • Lab: Get Started with Gen AI & Agents in Microsoft Foundry
  • Lab: Create Weather API Key (tool for agents)
  • Lab: Create Serper API Key (web search tool)
For everyone

MLOps on Azure for Beginners

MLOps fundamentals: the ML lifecycle, phases, and team responsibilities - and 11 more core concepts.

Modules covered
  • MLOps fundamentals: the ML lifecycle, phases, and team responsibilities
  • Common MLOps tools and platforms across Kubeflow, MLflow, AWS, Azure, and Google Cloud
  • Setting up MLOps infrastructure on Azure using YAML-based infrastructure as code
  • Building and deploying CI/CD ML pipelines with Azure DevOps
  • Running MLOps workflows on Azure using GitHub
  • Deploying ML models with Docker on an Azure Linux VM
  • Lab: Set Up MLOps on Azure - Infra using YAML
  • Lab: Deploy IaC Pipeline on Azure using GitHub
  • Lab: Set Up MLOps on Azure Using GitHub
  • Lab: Deploying ML Models with Docker on Azure
Layer 2 - Microsoft Azure fundamentals certifications
For everyone

Microsoft Azure Fundamentals

Cloud concepts, core Azure services, pricing, SLAs, governance, and compliance. Full exam prep included.

AZ-900Modules covered
  • Cloud concepts - defining cloud computing and the shared responsibility model
  • Cloud models: public, private and hybrid - and when each is the right call
  • The consumption-based model - CapEx vs OpEx and what actually drives your bill
  • Benefits of the cloud: high availability, scalability, elasticity and reliability
  • Benefits of the cloud: predictability, security, governance and manageability
  • Cloud service types - IaaS, PaaS and SaaS, with the right use case for each
  • Azure architecture - regions, region pairs and sovereign regions
  • Availability zones and Azure datacentres
  • Resources, resource groups, subscriptions and management groups - the full hierarchy
  • Compute services - virtual machines, scale sets, availability sets and Azure Virtual Desktop
  • Containers on Azure - Azure Container Instances, Container Apps and AKS
  • Azure Functions and serverless compute
  • Application hosting options - App Service, containers and virtual machines
  • Networking - virtual networks, subnets, peering and Azure DNS
  • Connectivity - VPN Gateway, ExpressRoute, public and private endpoints
  • Storage services - Blob, File, Queue and Table storage
  • Storage tiers and redundancy - LRS, ZRS, GRS and GZRS
  • Migration and file movement - Azure Migrate, Data Box, AzCopy, Storage Explorer and File Sync
  • Identity and security - Microsoft Entra ID and Entra Domain Services
  • Authentication vs authorization, single sign-on, MFA and passwordless
  • Conditional access, role-based access control and the Zero Trust model
  • Defence in depth and Microsoft Defender for Cloud
  • Cost management - factors that affect cost, the pricing and TCO calculators
  • Azure Cost Management, budgets and resource tags
  • Governance and compliance - Microsoft Purview, Azure Policy and resource locks
  • Deployment tools - Azure portal, Cloud Shell, CLI, PowerShell, ARM templates and Bicep
  • Infrastructure as code and Azure Arc
  • Monitoring - Azure Advisor, Service Health, Azure Monitor, Log Analytics and Application Insights
  • Full AZ-900 exam strategy, question styles and guided revision
  • Lab: AZ-900 Full Mock Exam - Practice Test 1
  • Lab: AZ-900 Full Mock Exam - Practice Test 2
For everyone

Azure AI Fundamentals

AI workloads, ML principles, computer vision, NLP, GenAI workloads, Azure OpenAI, and responsible AI.

AI-901Modules covered
  • AI workloads and considerations - content moderation and content personalization
  • Computer vision, natural language processing and document intelligence workloads
  • Knowledge mining and generative AI workloads
  • Responsible AI - fairness, reliability and safety
  • Responsible AI - privacy and security, inclusiveness, transparency and accountability
  • Machine learning principles - regression, classification and clustering
  • Deep learning and when it beats classical ML
  • Core concepts - features and labels, training and validation datasets
  • Azure Machine Learning - automated ML and the designer
  • Azure Machine Learning - data, compute, model management and deployment
  • Computer vision on Azure - image classification and object detection
  • Optical character recognition and facial detection
  • Azure AI Vision, Azure AI Face and Azure AI Video Indexer
  • Natural language processing - key phrase extraction and entity recognition
  • Sentiment analysis and language modelling
  • Speech recognition, speech synthesis and translation
  • Azure AI Language, Azure AI Speech and Azure AI Translator
  • Generative AI on Azure - language, image and code generation
  • Large and small language models, transformers, tokenization and embeddings
  • Microsoft Foundry and the Azure OpenAI model catalog
  • Prompt engineering, grounding and retrieval-augmented generation
  • Fine-tuning - when it is worth it and what it costs
  • Responsible generative AI and Azure content filters
  • Full AI-901 exam strategy, question styles and guided revision
  • Lab: Get Started with Microsoft Foundry
  • Lab: Explore the Azure Machine Learning workspace
  • Lab: Get Started with Computer Vision in Microsoft Foundry
  • Lab: Get Started with Text Analysis in Foundry
  • Lab: Get Started with Speech in Microsoft Foundry
  • Lab: Get Started with Foundry IQ in Foundry
  • Lab: AI-901 Full Mock Exam - Practice Test 1
  • Lab: AI-901 Full Mock Exam - Practice Test 2
Layer 3 - Python for AI/ML, GenAI, Agentic AI & data science
  • NumPy
  • pandas
  • Matplotlib
  • scikit-learn
  • Jupyter
  • Python
For everyone

Introduction to Python for Machine Learning

Overview of Python - key features and benefits for AI/ML - and 11 more topics & labs.

Modules covered
  • Overview of Python - key features and benefits for AI/ML
  • Setting up Python virtual environments
  • Running Python code - scripts vs notebooks
  • Variables in Python - declaration, types, assignment
  • Syntax, comments, variables, and data types
  • Booleans, Strings, Numbers - hands-on walkthroughs
  • Overview of Anaconda Navigator
  • Basics of Machine Learning - what it is and why Python is the language of choice
  • Lab: Installing Python in Windows, Mac, Linux
  • Lab: Install and use Jupyter Notebook
  • Lab: Google Colab - complete setup and usage guide
  • Lab: Basics - variables, data types, operations
For everyone

Python Data Structures, Control Flow & Functions

Tuples - definition, use cases, hands-on - and 10 more topics & labs.

Modules covered
  • Tuples - definition, use cases, hands-on
  • Lists - creation, indexing, slicing, methods
  • Dictionaries - key-value pairs, nesting, iteration
  • Sets - uniqueness, set operations, hands-on
  • Condition statements - if, elif, else
  • Looping statements - for loops, while loops, useful operators
  • Control flow and functions - defining, calling, scope
  • Methods - string methods, list methods, hands-on
  • Function arguments and parameters - positional, keyword, default, *args, **kwargs
  • Classes and objects - introduction to OOP basics
  • Errors and exception handling - try, except, finally
  • Slicing in Python - strings, lists, tuples
  • Pytest and testing frameworks - writing basic unit tests
  • API calls and JSON handling - requests library basics
  • Lab: Data structures - tuples, list, dictionary
  • Lab: Control statements
  • Lab: Function arguments and class objects
For everyone

Object-Oriented Programming (OOP)

OOP Part 1 - objects, classes, attributes, methods, __init__ - and 4 more topics & labs.

Modules covered
  • OOP Part 1 - objects, classes, attributes, methods, __init__
  • OOP Part 2 - inheritance, single and multi-level inheritance
  • OOP Part 3 - polymorphism, method overriding
  • OOP Part 4 - encapsulation, abstraction, dunder methods
  • Lab: OOP hands-on - build classes, implement inheritance and polymorphism
For everyone

Introducing Machine Learning in Detail

Machine Learning overview - types, workflow, real-world applications - and 12 more topics & labs.

Modules covered
  • Machine Learning overview - types, workflow, real-world applications
  • Data pre-processing prerequisites - missing values, encoding, scaling
  • Pandas - Series, DataFrames, indexing, filtering, groupby, merge, hands-on
  • NumPy - arrays, broadcasting, vectorised operations, hands-on
  • Matplotlib - plots, subplots, styling, visualisation hands-on
  • Scikit-learn (sklearn) - estimator API, pipelines, train-test split
  • Data visualisation - histograms, scatter plots, heatmaps, box plots
  • Central tendency of data - mean, median, mode, variance, standard deviation
  • Lab: Central tendency
  • Lab: Working with Pandas
  • Lab: Working with NumPy
  • Lab: sklearn - build an ML classification model
  • Lab: Data visualisation
For everyone

EDA & Feature Engineering

Feature engineering - encoding categorical variables, feature creation, binning, scaling - and 4 more topics & labs.

Modules covered
  • Feature engineering - encoding categorical variables, feature creation, binning, scaling
  • Dimensionality reduction - the curse of dimensionality, variance threshold, correlation analysis
  • Dimensionality reduction continued - PCA (Principal Component Analysis), interpretation
  • Lab: Feature engineering
  • Lab: Dimensionality reduction
For everyone

Supervised Machine Learning

Supervised ML overview - regression vs classification, labelled data - and 10 more topics & labs.

Modules covered
  • Supervised ML overview - regression vs classification, labelled data
  • Linear Regression - OLS, coefficients, R-squared, relative absolute error
  • Logistic Regression - sigmoid function, decision boundary, log loss
  • Stratification - why it matters in train-test splits
  • K-Nearest Neighbours (KNN) - distance metrics, choosing K
  • Support Vector Machines (SVM) - hyperplanes, kernels, margin
  • Naive Bayes - conditional probability, types of Naive Bayes
  • Model evaluation metrics - accuracy, precision, recall, F1, AUC-ROC, confusion matrix
  • Which metric to use - choosing the right evaluation approach per problem type
  • Lab: Supervised machine learning - end-to-end pipeline
  • Lab: Model evaluation
For everyone

Ensemble Learning

Decision Trees - splitting criteria, Gini impurity, information gain, pruning - and 5 more topics & labs.

Modules covered
  • Decision Trees - splitting criteria, Gini impurity, information gain, pruning
  • Random Forest - bagging, feature importance, out-of-bag error
  • XGBoost - gradient boosting, learning rate, regularisation
  • Support Vector Machines - deeper dive into kernel tricks
  • Classification measures and reports - classification report, macro vs weighted metrics
  • Lab: Ensemble learning - Decision Tree, Random Forest, XGBoost comparison
For everyone

Unsupervised Machine Learning

Unsupervised ML overview - clustering, dimensionality reduction, anomaly detection - and 9 more topics & labs.

Modules covered
  • Unsupervised ML overview - clustering, dimensionality reduction, anomaly detection
  • K-Means Clustering - centroid initialisation, elbow method, inertia
  • K-Modes - clustering for categorical data
  • Association Rule Mining - support, confidence, lift, Apriori algorithm
  • Frequent Pattern Growth (FP-Growth) - memory-efficient rule mining
  • Most common feature selection transforms for unsupervised learning
  • Lab: K-Means clustering analysis
  • Lab: Mall customer segmentation using K-Means
  • Lab: Association rule mining
  • Lab: Frequent pattern growth
Layer 4 - Azure AI/ML Job-Oriented Program

The core program: 12 modules and 70+ hands-on labs, taking you from AI foundations through agents, vision, MLOps and GenAIOps to governance. Three Microsoft certifications are covered across it.

M1

Foundations of AI, GenAI, Agents, NLP, Speech & Vision

AI & ML Fundamentals - and 13 more lessons & labs.

Lessons & labs
Lessons
  • AI & ML Fundamentals
  • Generative AI & LLMs
  • AI Agents & Agentic Workflows
  • Natural Language Processing
  • Speech & Vision AI
  • Information Extraction
Labs
  • Lab 01: Explore a simple AI agent
  • Lab 02: Explore generative AI
  • Lab 03: Get started with Foundry
  • Lab 04: GenAI & agents in Foundry
  • Lab 05: Explore text analytics
  • Lab 06: Explore AI speech
  • Lab 07: Explore computer vision
  • Lab 08: Explore info extraction
M2

Building, Planning & Deploying AI Apps on Microsoft Foundry

GenAI & LLMs in Foundry - and 11 more lessons & labs.

Lessons & labs
Lessons
  • GenAI & LLMs in Foundry
  • Prompting & Grounding
  • Foundry IQ & Knowledge Apps
  • Microsoft Foundry Deep Dive
  • Planning an AI Project
  • Model Selection & Deployment
Labs
  • Lab 09: Text analysis in Foundry
  • Lab 10: Speech in Foundry
  • Lab 11: Computer vision in Foundry
  • Lab 12–13: +2 more labs
  • Lab 14: Prepare an AI dev project
  • Lab 15: Explore and compare models
M3

GenAI Chat Apps, Tool Integration, RAG & Fine-Tuning

Chat App Architecture - and 9 more lessons & labs.

Lessons & labs
Lessons
  • Chat App Architecture
  • Prompt Flow & Context
  • Tool & API Integration
  • Retrieval-Augmented Generation
  • Fine-Tuning Language Models
  • Azure AI Search
Labs
  • Lab 16: Create a GenAI chat app
  • Lab 17: Chat app that uses tools
  • Lab 18: Fine-tune a language model
  • Lab 19: Knowledge mining solution
M4

Responsible AI Agents, MCP Tools & Foundry IQ

Agentic & Multimodal Systems - and 11 more lessons & labs.

Lessons & labs
Lessons
  • Agentic & Multimodal Systems
  • Responsible AI & Safety
  • Reliable & Secure Agents
  • Model Context Protocol (MCP)
  • Connecting Agents to Sources
  • Foundry IQ, Language & Speech
Labs
  • Lab 20: Apply safety guardrails
  • Lab 21: Build agents (portal / VS Code)
  • Lab 22: Custom function in an agent
  • Lab 23: AI agent with MCP tools
  • Lab 24: Analyze text
  • Lab 25: Build a text analysis agent
M5

Agent Workflows, Multi-Agent & A2A

Agentic Workflows in Foundry - and 5 more lessons & labs.

Lessons & labs
Lessons
  • Agentic Workflows in Foundry
  • Multi-Agent Solutions
  • A2A Protocol & Remote Agents
Labs
  • Lab 26: Build a workflow in Foundry
  • Lab 27: Build a multi-agent solution
  • Lab 28: Connect remote agents (A2A)
M6

Speech, Voice Agents & Translation

Speech-Capable GenAI - and 6 more lessons & labs.

Lessons & labs
Lessons
  • Speech-Capable GenAI
  • Designing Voice Agents
  • Text & Speech Translation
Labs
  • Lab 29: Speech-capable GenAI models
  • Lab 30: Recognize & synthesize speech
  • Lab 31: Azure Speech in an agent
  • Lab 32–33: +2 more labs
M7

Vision, Document Intelligence & Mining

Computer Vision Basics - and 6 more lessons & labs.

Lessons & labs
Lessons
  • Computer Vision Basics
  • Document Intelligence
  • Content Understanding
Labs
  • Lab 34: Vision-enabled chat app
  • Lab 35: Generate images with AI
  • Lab 36: Generate video with Sora
  • Lab 37–40: +4 more labs
M8

Agents in Microsoft Copilot Studio

Copilot Studio Design - and 5 more lessons & labs.

Lessons & labs
Lessons
  • Copilot Studio Design
  • Topics, Knowledge & Flows
  • Publishing Copilots
Labs
  • Lab 41: Deploy agents to Teams / Copilot
  • Lab 42: Work IQ for AI agents
  • Lab 43: Agent Framework SDK chat agent
M9

GitHub for MLOps & Experimentation with Azure ML

Git & GitHub for AI - and 11 more lessons & labs.

Lessons & labs
Lessons
  • Git & GitHub for AI
  • Branching & Code Review
  • GitHub Actions CI/CD
  • Azure ML Workspace & Data
  • Training & MLflow Tracking
  • Responsible AI in Model Dev
Labs
  • Lab 44: First GitHub Actions workflow
  • Lab 45: Automate training (GitHub Actions)
  • Lab 46: Best model with AutoML
  • Lab 47: Explore Azure ML workspace
  • Lab 48: Workspace developer tools
  • Lab 49–55: +7 more labs
M10

MLOps: Pipelines, Deployment & Monitoring

Azure ML Pipelines - and 12 more lessons & labs.

Lessons & labs
Lessons
  • Azure ML Pipelines
  • Preparing an MLOps Project
  • Automated Training (GitHub Actions)
  • Model Deployment on Azure
  • Batch vs. Online Endpoints
  • Registration & Validation
Labs
  • Lab 56: Run pipelines in Azure ML
  • Lab 57: Plan an MLOps solution
  • Lab 58: Optimize model training
  • Lab 59: Hyperparameter sweep job
  • Lab 60: Register models with MLflow
  • Lab 61: Deploy to batch endpoint
  • Lab 62–63: +2 more labs
M11

GenAIOps: Prompts, Versioning, Evaluation & Observability

GenAIOps & App Lifecycle - and 12 more lessons & labs.

Lessons & labs
Lessons
  • GenAIOps & App Lifecycle
  • Prompt Versioning
  • Automated Evaluation
  • GenAI Evaluation Metrics
  • Human vs. Auto Evaluation
  • Observability & Improvement
Labs
  • Lab 64: GenAIOps prerequisites
  • Lab 65: Set up a Foundry project
  • Lab 66: Prompt & agent versions
  • Lab 67: Design & optimize prompts
  • Lab 68: Cloud evaluators (auto eval)
  • Lab 69: Monitor & trace GenAI agent
  • Lab 70: Fine-tune to optimize agents
M12

AI Governance, Risk & Compliance

Governance & Compliance - and 2 more lessons & labs.

Lessons & labs
Lessons
  • Governance & Compliance
  • Responsible AI & Risk
  • Security & Privacy
Labs

Concepts and demos - no labs in this module.

Certifications you are eligible to sit for

  • AZ-305Azure Solutions Architect Expert
  • AI-103Azure AI Engineer Associate
  • AI-300MLOps Engineer Associate
Your portfolio, on GitHub

16 production-ready Azure AI/ML projects.

Not toy scripts. Each one is built, deployed, and documented the way enterprise teams actually ship.

What the program includes

Everything from skill-building to a job offer, in one system.

This is not a video library. It is a job-outcome system.

60+ Hours Live Weekly SessionsInteractive cohort, not recorded videos. Real-time Q&A with Azure AI/ML experts. Weekend live sessions with 24-hour recording access.
16 Real-World Projects on GitHubBuild a production-grade Azure AI/ML portfolio: RAG pipelines, MLOps systems, GenAI apps, AI agents, fraud detection, and more - all on GitHub.
6 CertificationsAZ-900, AI-901, AI-103, AZ-305, AI-300, and Python for AI/ML. Full exam prep, mock tests, and guided revision included for every cert.
Resume + LinkedIn MakeoverYour profile optimized for Azure AI/ML roles: ATS-ready, keyword-optimized for Microsoft ecosystem jobs, and recruiter-tested.
Mock Technical InterviewsLive sessions covering Azure AI architecture, MLOps system design, GenAI solution design, and behavioral rounds.
On-Job SupportSupport continues after you are hired - through your first job and beyond.
Common questions

Before you enroll.

Free videos teach topics. They do not give you a structured path through 12 modules and four layers, live instructors, graded labs on real Azure subscriptions, a portfolio of 16 deployed projects, exam preparation for six certifications, or a team reviewing your resume and preparing you for interviews. This is a job-outcome system, not a video library.

Layers 1 and 2 will move quickly for you, and that is fine - they are there so nobody is left behind. The depth starts at Layer 3 and Layer 4: Python for AI/ML, then a full specialisation track with its own Microsoft certification. Most people who have “built a few things” have never shipped an MLOps pipeline or an agentic RAG system to production.

The program is designed around full-time work. Weekly live sessions run on weekends, every session is recorded with 24-hour access, and the labs are self-paced. You keep access to everything for a year, so a busy month does not cost you the program.

12 modules across four layers. The first three layers are common to everyone; Layer 4 is the specialisation track you pick. Most learners complete it alongside a full-time job, and access to materials, labs and recordings runs for a full year.

Azure AI Engineer, AI Solution Architect, Azure Solutions Architect, GenAI Engineer, Machine Learning Engineer, MLOps Engineer, Platform Engineer, AI Developer and Cloud AI Specialist. Which one depends on the Layer 4 track you choose.

Yes - six months, action-based. Complete the hands-on labs and projects, apply to at least 50 Azure AI/ML-relevant roles, get your resume reviewed by the K21 team, and ask for support when you need it. Do all that and still not satisfied? A full refund, no fine print.

Live and instructor-led, so you can ask questions and work through problems with the trainer. Every session is recorded and posted within 24 hours, so missing one is not a problem.

Yes. Layer 1 starts from what cloud computing is and why it matters. No cloud, AI or Python background is assumed - the four beginner courses exist precisely so that complete beginners and experienced engineers end Layer 1 at the same place.

Weekly live Q&A, WhatsApp query support, a ticketing system, and mentor access throughout. After placement, On-Job Support continues through your first job and beyond.

Yes. Upskill is $1,997 in full or three monthly payments of $797. Upskill + Job Prep is $2,997 in full or four monthly payments of $797. Paying in full is the lower total cost on either plan.

Azure: Microsoft Foundry, Azure OpenAI, Azure AI Search, Azure Machine Learning, Azure AI Vision, Document Intelligence, AKS, Azure DevOps, Azure Monitor and Entra ID. Beyond Azure: Python, NumPy, pandas, Matplotlib, scikit-learn, LangChain, MLflow, Docker, Kubernetes, Git and GitHub Actions.

16 production-ready Azure AI/ML projects, each one built, deployed and documented the way enterprise teams actually ship - RAG pipelines, MLOps systems, GenAI apps, AI agents, fraud detection and more, all on GitHub.

Yes. We run cohort-based team enrollments with a shared progress dashboard and a dedicated point of contact. Get in touch and we will put together a plan for your team size.

⚡ Limited time - only for current enrollees

2 Exclusive Bonuses Included Free

Enroll now and unlock these two bonuses at no extra cost - only available for this cohort.

AKS for AI Workloads on Azure

Deploy and scale AI/ML workloads on Azure Kubernetes Service - containerization, autoscaling, ingress, and production-grade orchestration for machine learning pipelines.

AKSDockerKubernetesAzure Container Registry

Azure DevOps & CI/CD for ML

Build automated ML pipelines using Azure DevOps - CI/CD for model training and deployment, GitHub Actions integration, and MLOps best practices at enterprise scale.

Azure DevOpsGitHub ActionsCI/CDMLflow

⚠ These bonuses are only available while seats remain in the current cohort.

Hear it directly from them

Video stories from K21 learners.

Real K21 Academy learners on how they moved into high-paying AI, Data & Cloud roles.

K21 Academy student testimonial
K21 Academy student testimonial
K21 Academy student testimonial
K21 Academy student testimonial
K21 Academy student testimonial
K21 Academy student testimonial
K21 Academy student testimonial
K21 Academy student testimonial
More learners who landed roles through this program
✓Devesh Azure Solutions Architect
✓Poulami Beginner to Cloud Architect
✓Kennedy Senior Technical Advisor (UK)
✓SJ Salary doubled, husband hired too
✓Shouvik Senior Enterprise Architect
✓Meghana Career gap to working with BT
✓Kwame Job offer, relocated to USA
✓Rajalakshmi Career gap to Job at Infosys
✓Winifred Job offer in Canada
✓Bezzel Cloud Ops Engineer
In their own words

From our learners who landed the role.

Verified reviews from Google and Trustpilot. Every profile links to the real person on LinkedIn.

“I'm thrilled to share that I've recently landed a role as a Generative AI Engineer.”The hands-on projects let me confidently describe an end-to-end AI solution in the interview. The interviewer was genuinely impressed.
SteveGenerative AI Engineer✓ Google review verifiedin
“I've successfully landed a job as an AI Engineer.”Worked hands-on with Azure AI services, Azure AI Foundry, and OpenAI. Later received a second offer as a Generative AI Engineer. K21 gave me the skills and confidence to choose.
Debasish DashAI Engineer to Generative AI Engineer✓ Google review verifiedin
“Enrolling in K21 Academy's AI/ML program has been one of the best professional decisions I've made this year.”As a VP of Software Engineering transitioning into AI/ML, I completed four courses and passed three Microsoft certification exams: AI-900, AZ-900, and AB-731. The hands-on projects on Azure gave me practical experience that actually shows up in interviews.
Marcos OliveiraVP of Software Engineering✓ Trustpilot verifiedin
“I have finally secured a contract-to-hire role as a Generative AI Engineer.”I was deploying a RAG pipeline to a web app during the actual hiring process. The projects I built at K21 were exactly what the company needed.
Ike ImalaGenerative AI Engineer (contract-to-hire)✓ Google review verifiedin
“I got cracked that role.”The Azure AI/ML trainee landed the job after completing the program and building out her portfolio.
SemantiAzure AI/ML Engineer✓ Google review verifiedin
“K21 Academy is a great professional institution to meet your AI/ML career aspirations.”The right training, courseware, job application support, and placement guidance to help students achieve their goals. I particularly like the quality and depth of the faculty's knowledge.
Sanjeev SaksenaAI/ML Career Transition✓ Google review verifiedin
“The support is extremely awesome for labs, doubts and even resume updates.”I am 3 weeks into the AI/ML program. The team creates a custom game plan and learning path for everyone. The real-world project experiences and hands-on lab experience is a key differentiator.
Gaj ParanjapeAI/ML Program, USA✓ Trustpilot verifiedin
“I joined K21 Academy at a time when I felt stuck and unsure about my career direction.”Connecting with Shahid was a turning point. He gave me clarity, practical guidance, and renewed confidence. The AI curriculum is exhaustive and serves as a one-stop shop for AI and ML.
Gunita BajajFull Stack AI Program, USA✓ Trustpilot verifiedin
“Excited to share that I have successfully passed the Microsoft AI-900 certification!”Credits K21 Academy guidance for the preparation and is now moving on to the next Azure AI certification.
Adnan AhmedAI Engineer - AI-900 Certified✓ Trustpilot verifiedin
“I decided to explore AI as part of a career change, but coming from a non-technical background made me hesitant.”I joined K21 Academy and I'm really glad I did. The course is well-structured, catering to both beginners and advanced learners. The emphasis on hands-on experience truly enhances the learning process.
Monica VarryAI Career Changer✓ Google review verifiedin
“I've secured a position as an OpenShift Architect.”Credits the structured training and support system for making the transition into a cloud architecture role.
Stephen AgborOpenShift Architect✓ Trustpilot verifiedin
“The structured curriculum, supportive mentors, and proper guidance have given me a lot of clarity.”When I first started learning AI and Azure, I was quite confused about where to begin. K21's structured approach gave me direction and I now feel more confident in my learning journey.
Rahul SyalAI/ML Learner, India✓ Trustpilot verifiedin
Choose your path

Choose the support that fits your goals.

Both options include the full Azure AI/ML, GenAI & Agentic AI program. Add Job Prep for dedicated career support.

Upskill
Pay in full

$2,997$1,997

One payment. Full program access.

Build production-ready Azure AI/ML skills with hands-on projects and certification preparation.

  • Complete Azure AI/ML, GenAI & Agentic AI curriculum - 12 modules across 4 layers
  • 70+ hands-on Azure labs
  • 16 real-world Azure projects on GitHub
  • 500+ practice questions
  • 6 certification tracks: AZ-900, AI-901, AI-103, AZ-305, AI-300 and Python for AI/ML
  • Weekly live sessions, mentor access and community
  • 1-year access to recordings, labs and materials
  • AKS + Azure DevOps CI/CD bonuses included
Enroll in Upskill →$2,997 $1,997
Upskill
Pay in installments

$1,097 × 3$797 × 3

3 monthly payments of $797. Total: $2,391.

Build production-ready Azure AI/ML skills with hands-on projects and certification preparation.

  • Complete Azure AI/ML, GenAI & Agentic AI curriculum - 12 modules across 4 layers
  • 70+ hands-on Azure labs
  • 16 real-world Azure projects on GitHub
  • 500+ practice questions
  • 6 certification tracks: AZ-900, AI-901, AI-103, AZ-305, AI-300 and Python for AI/ML
  • Weekly live sessions, mentor access and community
  • 1-year access to recordings, labs and materials
  • AKS + Azure DevOps CI/CD bonuses included
Choose Upskill Installments →$1,097 × 3 $797 × 3
Upskill + Job Prep
Pay in full

$3,997$2,997

One payment. Full program access plus Job Prep.

Everything in Upskill, plus the resume, interviews and on-job support that turn skills into an offer.

  • Complete Azure AI/ML, GenAI & Agentic AI curriculum - 12 modules across 4 layers
  • 70+ hands-on Azure labs
  • 16 real-world Azure projects on GitHub
  • 500+ practice questions
  • 6 certification tracks: AZ-900, AI-901, AI-103, AZ-305, AI-300 and Python for AI/ML
  • Weekly live sessions, mentor access and community
  • 1-year access to recordings, labs and materials
  • AKS + Azure DevOps CI/CD bonuses included
  • Everything above, plus
  • Resume + LinkedIn rebuilt for Azure AI roles
  • Mock technical interviews
  • Job-search strategy and application tracking
  • On-Job Support after you are hired
Enroll in Upskill + Job Prep →$3,997 $2,997
Upskill + Job Prep
Pay in installments

$1,097 × 4$797 × 4

4 monthly payments of $797. Total: $3,188.

Everything in Upskill, plus the resume, interviews and on-job support that turn skills into an offer.

  • Complete Azure AI/ML, GenAI & Agentic AI curriculum - 12 modules across 4 layers
  • 70+ hands-on Azure labs
  • 16 real-world Azure projects on GitHub
  • 500+ practice questions
  • 6 certification tracks: AZ-900, AI-901, AI-103, AZ-305, AI-300 and Python for AI/ML
  • Weekly live sessions, mentor access and community
  • 1-year access to recordings, labs and materials
  • AKS + Azure DevOps CI/CD bonuses included
  • Everything above, plus
  • Resume + LinkedIn rebuilt for Azure AI roles
  • Mock technical interviews
  • Job-search strategy and application tracking
  • On-Job Support after you are hired
Choose Job Prep Installments →$1,097 × 4 $797 × 4

Secure your place in the next cohort.

The guarantee - 6 months. Love it or leave it.

Your decision is protected. Do the work, and if it does not deliver, you get your money back.

  • Complete all hands-on labs and projects
  • Apply to a minimum of 50 Azure AI/ML-relevant roles
  • Get your resume reviewed by K21's team
  • Ask for support when you need it - do not go silent

Did all that and still not satisfied? Full refund. Action-based, six months, no fine print.

Ready to land your Azure AI/ML role?

Join the next cohort and build the skills, portfolio, and support system that gets you hired.

Enroll now →
Azure AI/ML & Gen AI Job-Oriented Program - by K21 Academy