Claude Code for Agentic AI Mastery | K21 Academy
Everyone is using AI. Few can build agents that ship.

Become an Agentic AI builder with Claude Code.

Claude Code for Agentic AI Mastery is a live, hands-on program for IT professionals: architects, developers, DevOps & SRE, QA, business analysts, administrators and technical, project & program managers. Go from Claude fundamentals to production-grade agentic systems across 13 core modules and ship a real capstone.

46,000+ professionals trained by K21 Academy · Live interactive sessions · 1 year of access

13core modules, live
48+hours live training
37hands-on labs in the core
1real-world capstone
3bonus modules
Limited seats · Live cohort

Next batch starts in

Dates announced soon

00Days
00Hours
00Minutes
00Seconds
PST6:00 PM to 8:00 PM (16 Nov)
EST9:00 PM to 11:00 PM (16 Nov)
GMT2:00 AM to 4:00 AM (17 Nov)
IST7:30 AM to 9:30 AM (17 Nov)
Reserve your seat
Who this is for

Part builder, part architect, part AI lead.

Knowing how to prompt a chatbot is no longer enough. Teams want people who can design, build, test and govern agents that run inside real delivery work. No prior AI or ML experience needed.

🏛️

Architects & tech leads

Design agentic systems and defend every choice on the diagram.

💻

Developers & engineers

Build with Claude Code, the Agent SDK and MCP, faster and to your standards.

⚙️

DevOps, SRE & MLOps

Wire agents into GitHub, CI/CD, containers and Kubernetes.

🧪

QA & testing professionals

Generate tests, validate output and measure coverage with AI.

📋

Business analysts & admins

Turn BRDs, docs and diagrams into AI-assisted workflows.

🧭

Project & program managers

Understand what agents can do, what they cost and how to govern them.

Our coaches and alumni work at these companies

JPMorgan ChaseMicrosoftAccentureCapgeminiPwCAT&TBNY Mellon
Curriculum · Your Claude roadmap

13 modules, from Claude fundamentals to production-grade agents.

Live, instructor-led sessions with a concept-and-demo hour followed by labs you build yourself. Tap a module under the roadmap to open its lessons and labs.

Claude Code for Agentic AI Mastery roadmap: 12 modules from Foundations of Claude and Agentic AI to Applied Labs and Real-World Agentic Workflows
Scroll sideways to see the full roadmap →
FoundationsDesignBuildShip & govern

Claude Code for Agentic AI Mastery

13 core modules · open any module to see every lesson and lab

●M1

Foundations of Claude and Agentic AI

What Claude is, how agents fit into GenAI workflows and the SDLC.

5 topics
  • Module overview presentation
  • Overview of the Claude model family
  • What is Claude? LLMs explained for developers
  • Role of agents in GenAI workflows & the SDLC
  • Agentic loop lifecycle and correct termination
●M2

Setup Environment & Enterprise

Your first Claude API call and a working enterprise dev environment.

5 topics · 5 labs
  • Module overview presentation
  • Your first Claude API call
  • Claude Code: what it is
  • Setup for UI, backend & knowledge base
  • API / platform access, integrations & secrets
  • Lab 2.1: Create & set up a Claude account
  • Lab 2.2: Set up VS Code
  • Lab 2.3: Install Git, Node.js & Claude Code
  • Lab 2.4: Install the Chrome extension for Claude
  • Lab 2.5: Interactive workspace in a Claude Artifact
●M3

Prompt Engineering for Reliable Agents

Instructions, few-shot and escalation patterns that make agents predictable.

5 topics · 4 labs
  • Module overview presentation
  • Explicit criteria and instruction design
  • Few-shot prompting for consistency
  • Context optimization
  • Escalation patterns and reliable decision-making
  • Lab 3.1: Design and test basic prompts for Claude
  • Lab 3.2: Structure prompts for relevant answers
  • Lab 3.3: Implement context retention systems
  • Lab 3.4: Simulate edge cases & test robustness
●M4

Designing Agents for SDLC

Modular architecture, the Agent SDK, subagents and MCP patterns.

7 topics · 2 labs
  • Module overview presentation
  • Modular architecture & the Agent SDK
  • Task decomposition and chaining
  • Multi-agent orchestration
  • Tool distribution across agents
  • Writing tool descriptions
  • MCP patterns for real workflows
  • Lab 4.1: Task decomposition, plan & implementation
  • Lab 4.2: Creating multiple subagents with Claude
●M5

AI-Powered Code Gen & Engineering Standards

Claude Code, CLAUDE.md, Skills, Plugins and full-stack generation.

7 topics · 7 labs
  • Backend and frontend standards
  • API design (REST / GraphQL)
  • Tool use for reliable structured output
  • CLAUDE.md configuration hierarchy
  • Custom slash commands and Skills
  • Plan mode and iterative refinement
  • Structured error responses
  • Lab 5.1: Working with Claude Skills
  • Lab 5.2: Working with Claude Plugins
  • Lab 5.3: Calorie Tracker app with Claude Code
  • Lab 5.4: Generate backend API code (CRUD, Auth)
  • Lab 5.5: Generate frontend components & UI
  • Lab 5.6: Generate and design REST / GraphQL APIs
  • Lab 5.7: Tune Claude for context assistance
●M6

UI and Experience Generation with AI

Design systems, Figma and HTML scaffolding with accessibility built in.

3 topics · 2 labs
  • Figma and HTML integration
  • Design systems and AI-driven scaffolding
  • Accessibility and responsiveness constraints
  • Lab 6.1: Generate a UI design system in Figma
  • Lab 6.2: Generate HTML/CSS from a design
●M7

Automated Testing and Quality Engineering

Unit and integration tests, validation-retry loops and coverage.

4 topics · 3 labs
  • Unit & integration test generation
  • Validation-retry loops
  • Multi-pass review strategies
  • Coverage and quality metrics
  • Lab 7.1: Generate unit tests & validate correctness
  • Lab 7.2: Integration tests across multiple systems
  • Lab 7.3: Assess coverage and generate reports
●M8

AI-Driven Documentation & Visualization

Mermaid diagrams, tech docs and BRD-to-requirement traceability.

3 topics · 3 labs
  • Mermaid and swimlane diagrams
  • Technical documentation generation
  • BRD to requirement traceability
  • Lab 8.1: Create flowcharts / Mermaid diagrams
  • Lab 8.2: Automatically generate tech docs
  • Lab 8.3: Generate a Business Requirements Document
●M9

Enterprise Integration & DevOps Alignment

Rally, GitHub, CI/CD and MCP server configuration.

4 topics · 3 labs
  • Rally integration & Excel export
  • GitHub structure, commit standards
  • CI/CD integration and session isolation
  • MCP server configuration
  • Lab 9.1: Integrate Rally with Claude
  • Lab 9.2: Integrate Claude with GitHub
  • Lab 9.3: Design a CI/CD pipeline using Claude
●M10

Debugging, Optimization & Improvement

Troubleshooting, context degradation, performance and feedback loops.

4 topics · 3 labs
  • Troubleshooting common issues
  • Context degradation in extended sessions
  • Performance tuning
  • Feedback loops
  • Lab 10.1: Introduce bugs to test Claude
  • Lab 10.2: Optimize code for memory & speed
  • Lab 10.3: Implement feedback loops
●M11

Governance, Security & Responsible AI

Hooks, human review, provenance and audit trails a security team will approve.

4 topics · 3 labs
  • Coding standards & ethical AI practices
  • Hooks and programmatic enforcement
  • Human review, provenance
  • Version control and audit trails
  • Lab 11.1: Implement a security / compliance checklist
  • Lab 11.2: Version control tracking for AI models
  • Lab 11.3: Test outputs for bias and add filters
●M12

Labs & Real-World Agentic Workflows

Custom agents for niche workflows, built on a real airline pilot.

3 topics · 2 labs
  • IAG Booking (Iberia pilot) overview
  • Custom agent creation for niche workflows
  • Collaborative development scenarios
  • Lab 12.1: Design real-world agentic workflows
  • Lab 12.2: Hands-on multi-agent workflow
●M13

Wrap-Up and Next Steps

Q&A, feedback and where to go next.

2 topics
  • Q&A and feedback collection
  • Next steps after the program

Bonus modules

Included for every learner

●B1Bonus 1

Git & GitHub Actions Training

Version control and CI workflows every agent builder needs.

8 topics · 3 labs
  • Git introduction, installation & setup
  • Working with Git: common terms & commands
  • Git workflows
  • Create CI workflows with GitHub Actions
  • Manage and debug workflows
  • Customize workflows with environment variables
  • Cache, share and debug workflows
  • Automate GitHub using GitHub Script
  • Lab: Git & GitHub installation & setup
  • Lab: Create a CI workflow on GitHub
  • Lab: Using GitHub Script in GitHub Actions
●B2Bonus 2

Claude + AWS Labs

Run Claude and Claude Code on Amazon Bedrock and the AWS platform.

0 topics · 4 labs
  • Lab: Automate AWS diagrams using Kiro AI
  • Lab: Claude Code on Amazon Bedrock
  • Lab: Claude Design Lab
  • Lab: Claude on the AWS Platform
●B3Bonus 3

Containers & Kubernetes for AI Deployment

From Docker basics to deploying containerized AI apps and agents on Kubernetes.

7 topics · 3 labs
  • Docker fundamentals: images, containers & Dockerfiles
  • Containerizing AI apps: package Claude apps & MCP servers
  • Environment variables & secrets
  • Container registries: push, pull, tag & version images
  • Kubernetes essentials: pods, deployments, services, ConfigMaps & secrets
  • Deploying AI apps on Kubernetes: autoscaling and rolling updates
  • Operations & monitoring: logs, metrics, health checks and cost awareness
  • Lab: Build and run your first container
  • Lab: Containerize a Claude-powered app
  • Lab: Deploy an AI app to Kubernetes

New to Claude and AI? Don't worry, start with the beginner courses

Self-paced beginner sessions, included for everyone. Take them before the first live session or alongside it.

For everyone

AI, ML, GenAI & Agentic AI for Beginners

AI, machine learning, deep learning and generative AI explained simply, before you build with Claude.

View lessons & labs7 topics · 1 lab
  • What is AI, Machine Learning & Deep Learning?
  • Generative AI & Large Language Models explained simply
  • Supervised, unsupervised & reinforcement learning
  • Real-world AI/ML use cases across industries
  • Foundation models & how they are trained
  • Prompts, tokens & inference basics
  • Responsible AI & safety fundamentals
  • Lab: Chat with a foundation model and explore its behaviour
For everyone

Cloud for Beginners

What cloud computing is and why it matters, before you build on any platform.

View lessons & labs6 topics · 1 lab
  • What is cloud computing and why it matters
  • Public, private & hybrid cloud
  • IaaS, PaaS, SaaS
  • Regions, availability zones & scalability
  • Cloud pricing models and cost basics
  • Cloud security fundamentals
  • Lab: Explore free tiers across major cloud providers
For everyone

Data for Beginners

The data literacy every AI builder needs: types, storage, pipelines and quality.

View lessons & labs7 topics · 1 lab
  • Structured, semi-structured & unstructured data
  • Databases vs data warehouses vs data lakes
  • How data is stored, moved & queried
  • Introduction to data pipelines (ETL / ELT)
  • Data quality, cleaning & validation basics
  • Data formats for AI: JSON, CSV, embeddings
  • Data privacy & governance basics
  • Lab: Load, clean & explore a simple dataset
For everyone

AWS for Beginners

A quick, practical tour of AWS.

View lessons & labs4 topics · 1 lab
  • What AWS is & the global infrastructure
  • Core services: EC2, S3, IAM, VPC, RDS
  • AWS pricing, free tier & billing basics
  • Where AI/ML fits: SageMaker & Bedrock overview
  • Lab: Create an AWS free-tier account & explore the console
For everyone

Azure for Beginners

A quick, practical tour of Azure.

View lessons & labs4 topics · 1 lab
  • What Azure is & core regions
  • Core services: VMs, Blob Storage, Entra ID, VNet
  • Azure pricing & free account basics
  • Where AI fits: Azure AI & Foundry overview
  • Lab: Create an Azure free account & explore the portal
For everyone

Google Cloud for Beginners

A quick, practical tour of Google Cloud.

View lessons & labs4 topics · 1 lab
  • What Google Cloud is & its infrastructure
  • Core services: Compute Engine, Cloud Storage, IAM, VPC
  • GCP pricing & free tier basics
  • Where AI fits: Vertex AI overview
  • Lab: Create a GCP free account & explore the console
For everyone

Oracle Cloud for Beginners

A quick, practical tour of Oracle Cloud.

View lessons & labs4 topics · 1 lab
  • What Oracle Cloud (OCI) is
  • Core services: Compute, Object Storage, IAM, VCN
  • OCI pricing & always-free resources
  • Where AI fits: OCI AI services overview
  • Lab: Create an OCI free account & explore the console

Going deep into development? Python is included

A full Python track, from your first script to the libraries behind AI, ML and GenAI.

Bonus

Introduction to Python for AI, ML & GenAI

Your first Python: environment, data types, loops and functions.

View lessons & labs7 topics · 1 lab
  • Overview of Python: key features and benefits for AI/ML
  • Setting up the Python environment (Anaconda, Jupyter, VS Code)
  • Variables, data types, and type conversion
  • Strings, lists, tuples: creation and manipulation
  • Conditional statements and loops (for / while)
  • Functions: defining, calling, arguments, return values
  • Importing libraries and using pip
  • Lab: Introduction to Python for Machine Learning
Bonus

Python Data Structures, Control Flow & Functions

Tuples, lists, dictionaries, sets, lambdas, exceptions and file I/O.

View lessons & labs7 topics · 1 lab
  • Tuples: definition, use cases, hands-on
  • Lists: indexing, slicing, list comprehensions
  • Dictionaries: keys, values, nested dicts
  • Sets: operations, membership testing
  • Lambda functions, map, filter, reduce
  • Exception handling: try / except / finally
  • File I/O: reading and writing files in Python
  • Lab: Python Data Structures, Control Flow & Functions
Bonus

Object-Oriented Programming (OOP)

Objects, classes, inheritance, encapsulation, polymorphism and decorators.

View lessons & labs5 topics · 1 lab
  • Objects, classes, attributes, methods, __init__
  • Inheritance, method overriding, super()
  • Encapsulation: private, protected attributes
  • Polymorphism: duck typing and method resolution order
  • Decorators and class / static methods
  • Lab: Object-Oriented Programming (OOP)
Bonus

Python Frameworks & AI Libraries

TensorFlow, PyTorch and the core Python AI libraries.

View lessons & labs6 topics · 2 labs
  • The Python AI/ML library ecosystem
  • TensorFlow: tensors, the Keras API, building & training models
  • PyTorch: tensors, autograd, nn.Module, training loops
  • Keras for rapid model prototyping
  • Hugging Face Transformers for GenAI & LLMs
  • Choosing the right framework
  • Lab: Build & train a model with TensorFlow
  • Lab: Build & train a model with PyTorch
Bonus

EDA & Feature Engineering

Encoding, feature creation, binning, scaling, PCA and preprocessing pipelines.

View lessons & labs5 topics · 1 lab
  • Encoding categorical variables
  • Feature creation, binning, and scaling (MinMax, Standard)
  • Dimensionality reduction with PCA
  • Correlation analysis and feature selection
  • Pipelines in scikit-learn for reproducible preprocessing
  • Lab: EDA & Feature Engineering
Bonus

Supervised Machine Learning

Regression vs classification, core algorithms and evaluation metrics.

View lessons & labs6 topics · 1 lab
  • Regression vs classification
  • Linear Regression: OLS, gradient descent, regularization
  • Logistic Regression: sigmoid, decision boundary, metrics
  • K-Nearest Neighbors (KNN)
  • Support Vector Machines (SVM): kernels, margin
  • Evaluation metrics: accuracy, F1, ROC-AUC, confusion matrix
  • Lab: Supervised Machine Learning
Bonus

Ensemble Learning

Decision trees, bagging, boosting, stacking and hyperparameter tuning.

View lessons & labs5 topics · 1 lab
  • Decision Trees: splitting criteria, Gini impurity, information gain
  • Bagging: Bootstrap aggregation, Random Forest
  • Boosting: AdaBoost, Gradient Boosting, XGBoost
  • Stacking and blending ensemble methods
  • Hyperparameter tuning: GridSearchCV, RandomizedSearch
  • Lab: Ensemble Learning
Bonus

Unsupervised Machine Learning

Clustering, dimensionality reduction and anomaly detection.

View lessons & labs5 topics · 1 lab
  • K-Means Clustering: elbow method, silhouette score
  • DBSCAN: density-based clustering
  • Hierarchical Clustering and dendrograms
  • Autoencoders for anomaly detection
  • t-SNE and UMAP for high-dimensional visualization
  • Lab: Unsupervised Machine Learning
Your portfolio

A real-world capstone, plus the builds that get you there.

Every card is something you build in the labs. Tap a card to flip it and see the architecture.

Certification prep kit · Self-study

Want to sit the Claude certification exam? The prep kit is yours.

The certification is not part of the live program. If you decide to sit the Claude Certified Architect, Foundations exam, you get a self-study prep kit: 8 modules, 25 labs and 200+ practice questions to work through at your own pace.

📘

Self-study, at your own pace. The kit is optional and is not covered in the live sessions. The exam and its fee are separate and are not included.

●M1Self-study

AI Foundations

What AI really is, LLMs, and the Claude product family.

7 topics
  • What AI actually is
  • Machine learning, deep learning & generative AI
  • Large language models
  • Claude 101
  • How companies are using Claude today
  • The Claude product family: Haiku, Sonnet, Opus
  • Anthropic's safety-first approach and what it means for architects
●M2Self-study

Claude API

The Claude API end to end: messages, streaming, tokens, caching and batching.

16 topics
  • Understanding the Claude API & API responses
  • Creating Claude API keys
  • The three message roles: system, user, assistant
  • Using .env to load the API key
  • Clean output (response JSON)
  • Using system prompts
  • Multi-turn conversations
  • Streaming responses
  • Temperature and max tokens
  • Image input
  • stop_reason: the field that drives everything agentic
  • Stop sequences
  • Tokens & context windows
  • Prompt caching
  • Message Batches API: processing at scale
  • Error handling, retries, and rate limits
●M3Self-study

Agentic Architecture & Orchestration

From a single API call to multi-agent systems: loops, hub-and-spoke, hooks and escalation.

10 topics · 5 labs
  • From API call to agentic loop
  • stop_reason as loop controller
  • The Agent SDK
  • Why single agents fail at scale
  • Hub-and-spoke architecture
  • Context isolation for subagents
  • Hooks: PreToolUse and PostToolUse
  • Hooks vs prompts: deterministic vs probabilistic
  • Valid escalation triggers
  • Prompt chaining vs dynamic adaptive decomposition
  • Lab: Build a perceive, reason, act, observe loop for a single tool-using agent
  • Lab: Decompose a goal into sequential and parallel (fan-out / fan-in) pipelines
  • Lab: Build a hub-and-spoke orchestrator coordinating three subagents
  • Lab: Add retry logic, fallback chains, and a human-escalation trigger
  • Lab: Design a programmatic validation layer that gates a high-stakes action
●M4Self-study

Tool Design & MCP Integration

Design tools Claude uses reliably and build MCP servers.

8 topics · 5 labs
  • Tool descriptions as documentation for Claude
  • Designing input_schema: types, required, enum, descriptions
  • Structured error responses
  • Access failure vs empty result
  • Model Context Protocol (MCP)
  • Tool count and tool distribution across agents
  • Claude Code's built-in tools: Read, Write, Bash, Grep, Glob
  • Built-in tool: Web Search
  • Lab: Write tool descriptions & input schemas that route correctly
  • Lab: Build your first MCP server in Python
  • Lab: Add MCP prompt templates and connect an MCP client
  • Lab: Deploy a remote MCP server over StreamableHTTP with auth
  • Lab: Decide built-in vs custom tools and set tool boundaries
●M5Self-study

Claude Code Configurations & Workflows

CLAUDE.md, slash commands, plan mode, hooks and CI/CD.

10 topics · 5 labs
  • Claude Code for IT professionals
  • Installing Claude Code
  • Code explanation using Claude Code
  • CLAUDE.md: your project's AI configuration file
  • CLAUDE.md hierarchy: global, project, subdirectory
  • Session operations: resume, fork, named sessions
  • Custom slash commands
  • Plan mode: approval before execution
  • TDD iteration workflow
  • Non-interactive mode and CI/CD integration
  • Lab: Onboard Claude Code to a codebase & author a 3-level CLAUDE.md hierarchy
  • Lab: Build custom slash commands and a reusable SKILL.md
  • Lab: Create custom subagents and drive them with plan mode
  • Lab: Implement PreToolUse and PostToolUse hooks
  • Lab: Run Claude Code headless in a CI/CD pipeline with the -p flag
●M6Self-study

Prompt Engineering & Structured Output

Production prompting, few-shot & chain-of-thought, and schema-locked JSON output.

7 topics · 5 labs
  • Production prompting vs casual prompting
  • Zero-shot, one-shot & few-shot
  • Chain-of-thought (CoT)
  • tool_use for guaranteed structured output
  • JSON schema design that prevents extraction failures
  • Validation-retry loops
  • Multi-pass review
  • Lab: Engineer a structured system prompt (role, XML tags, constraints)
  • Lab: Design few-shot examples and chain-of-thought for a reasoning task
  • Lab: Force schema-constrained JSON output using tool definitions
  • Lab: Build a validation loop with a retry strategy
  • Lab: Build a prompt evaluation workflow with model-based grading
●M7Self-study

Context Management & Reliability

Keep long-running agents accurate: context, provenance and escalation.

7 topics · 5 labs
  • Context positioning: instruction placement matters
  • Context degradation in long-running sessions
  • Progressive summarization
  • Information provenance
  • Error propagation
  • Per-type accuracy metrics
  • Escalation patterns: retry, reroute, human review, fail gracefully
  • Lab: Design a persistent fact block and trim strategy for long sessions
  • Lab: Build a handoff payload and propagation contract between two agents
  • Lab: Implement the scratchpad pattern as a durable memory slot
  • Lab: Route low-confidence output through a review queue
  • Lab: Add claim-to-source mappings and conflict annotation
●M8Self-study

Practice Questions & Simulator

200+ exam-style questions and a timed practice simulator.

5 topics
  • 200+ exam-style questions with explanations
  • Timed practice simulator
  • Domain-by-domain revision
  • Scenario walkthroughs
  • Readiness checkpoints
What the program includes

What you walk away with.

Not a video library. A working agentic system and the artifacts a hiring manager can open.

🎥

48+ hours of live training

Instructor-led sessions with real-time Q&A, and recordings after every session.

🔗

A working capstone

The Airline Booking System rewrite: real agentic architecture you can demo and walk through.

🧰

Agents, MCP & workflows

Subagents, MCP integrations, Claude Code Skills, hooks and CI/CD pipelines. Real artifacts, not toy snippets.

📐

Docs & diagrams

Mermaid diagrams, technical docs and BRDs generated and traced with Claude.

🛡️

Governance playbook

Security checklists, bias filters, audit trails and human-review patterns for responsible AI.

📘

Certification prep kit

Self-study modules, labs and 200+ practice questions, if you choose to sit the exam.

Verified reviews

What our learners say.

★ Trustpilot

★★★★★

K21 Academy: The Real Deal for Tech Leaders

As a VP of Software Engineering making a deliberate career transition into AI/ML and Cloud leadership, I've been extremely selective about where I invest my time and money. Enrolling in K21 Academy's program has been one of the best professional decisions I've made this year. I've completed four courses and passed three Microsoft certification exams: AI-900, AZ-900, and AB-731.

Marcos OliveiraVP of Software Engineering · 21 May

★ Trustpilot

★★★★★

Recommendation of K21 Academy

Very pleased with K21 Academy thus far. The instructor of the Agentic AI Mastery program is very knowledgeable and a very good communicator. The team in the Interview/Job Preparation area are extremely helpful in assisting with resume modification, LinkedIn profile optimization, and job application preparation. I highly recommend K21 Academy.

Dale McAnally5 Jul

★ Trustpilot

★★★★★

Best Learning Avenue

I have been part of a few training programs by K21 Academy and they are doing justice with what they are offering. They have a detailed program structure, professional trainers and their support system is excellent. The learning portal SKOOL is the real differentiator. I would recommend K21 Academy to all serious learners.

Chandan Raj8 Jun

★ Trustpilot

★★★★★

Excellent guidance and support throughout

I wanted to express my special appreciation to Atisha and the team for their constant support throughout my job search and interview preparation journey. The interview guides have been invaluable study material and remarkably accurate in reflecting the types of questions asked during my interviews. It has made a real difference to my preparation and confidence.

Reny4 Jul

★ Trustpilot

★★★★★

Great experience learning AI/ML and Cloud

I had a great experience learning AI/ML and Cloud Engineering concepts with K21 Academy. The training was very practical and helped me strengthen my skills in Python, cloud technologies, data pipelines, and real-world projects. Career support included resume building, LinkedIn optimization, interview preparation. Highly recommend.

Sadia14 May

★ Trustpilot

★★★★★

Mock interview gave me real clarity

Thank you for arranging the mock interview and for helping me better understand what the company is looking for. The discussion gave me much greater clarity about the role and its expectations. The mock interview was extremely valuable in identifying gaps in my knowledge and helping me understand where I need to improve.

Shanti Mandal20 Jul

★ Trustpilot

★★★★★

I was LOST and then was FOUND

I started my first program yesterday and was LOST on the first day. Sahid made sure that I regained my bearings and put me on the right track. I was surprised at the dedication and additional time he was willing to take. It is truly AH-MAZING to learn from instructors who are EXCITED about the content. So far, So GREAT!

Anissa Webb19 Jul

★ Trustpilot

★★★★★

Wonderful consultation with realistic timelines

I had a wonderful consultation session with Shahid. He took his time to explain my roadmap and proper structure to follow with realistic timelines. What I appreciated was how personalized the guidance felt - he understood exactly where I was in my journey and what I needed to do next. Keep the good work up!

Akash Tiwari18 Jul

★ Trustpilot

★★★★★

Patient, knowledgeable team with a clear roadmap

I had a great experience with Sahid, Gracy and the team. They were very patient, knowledgeable, and took the time to address all my questions. The session helped clarify many of my doubts and gave me a much clearer understanding of the learning roadmap. As a working professional, I especially appreciated their practical guidance.

Oves Khan14 Jul

Rated 4.6/5 on Trustpilot · 267+ reviews

Hear it directly from them

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

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✓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.

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SteveGenerative AI Engineer✓ GOOGLE REVIEW VERIFIED
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“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.

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“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.

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“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.

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“I got cracked that role.”

The Azure AI/ML trainee landed the job after completing the program and building out her portfolio.

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in

“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.

SS
Sanjeev SaksenaAI/ML Career Transition✓ GOOGLE REVIEW VERIFIED
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“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.

GP
Gaj ParanjapeAI/ML Program, USA✓ TRUSTPILOT REVIEW VERIFIED
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“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.

GB
Gunita BajajFull Stack AI Program, USA✓ TRUSTPILOT REVIEW VERIFIED
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“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.

AA
Adnan AhmedAI Engineer – AI-900 Certified✓ TRUSTPILOT REVIEW VERIFIED
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“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.

MV
Monica VarryAI Career Changer✓ GOOGLE REVIEW VERIFIED
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“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.

SA
Stephen AgborOpenShift Architect✓ TRUSTPILOT REVIEW VERIFIED
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“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.

RS
Rahul SyalAI/ML Learner, India✓ TRUSTPILOT REVIEW VERIFIED
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Choose your path

Choose the support that fits your goals.

Both options include the full Claude Code for Agentic AI Mastery program. Add Job Prep for dedicated career support.

UPSKILL

PAY IN FULL

$2,997$1,997
You save $1,000

Build production-grade agentic systems with Claude and Claude Code, and finish with a capstone you can demo.

  • All 13 Claude Code for Agentic AI Mastery modules, live
  • 48+ hours of instructor-led training
  • 37 hands-on labs with Claude & Claude Code
  • Real-world capstone: Airline Booking System rewrite
  • Beginner courses: AI/ML, Cloud, Data, AWS, Azure, GCP & OCI
  • Bonus: Python for AI, ML, Data, GenAI & Claude
  • Bonus: Git & GitHub Actions, Claude + AWS Labs
  • Bonus: Containers & Kubernetes for AI Deployment
  • Certification prep kit (self-study)
  • 1-year access to recordings, labs and community
Enroll in Upskill →$1,997

Secure your place in the next cohort.

6-month money-back guarantee

Six months. Love it or leave it.

Complete the program the way it's designed, and if you're not satisfied within six months, we'll refund you. Applies to both plans.

  • Attend the live sessions and complete the labs
  • Build and present the capstone project
  • Ask for support when you need it, don't go silent

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

Common questions

Before you enroll.

Who is this program for?

IT professionals who want to lead with AI: architects, developers, DevOps & SRE, MLOps, DBAs, QA and testing, business analysts, administrators, and technical, project and program managers.

Do I need prior AI, ML or cloud experience?

No. The beginner courses (AI/ML, Cloud, Data and the AWS, Azure and Google Cloud tours) and the bonus Python track get you up to speed before the first live session.

Is a certification included?

The program itself does not include an exam or exam voucher. You get a certification prep kit for the Claude Certified Architect, Foundations exam: 8 self-study modules, labs and 200+ practice questions you can work through on your own if you decide to sit the exam. The exam fee is paid separately.

How long is the program?

The core program is 13 modules of live, instructor-led training. The beginner courses, Python track, bonus modules and certification prep kit are self-paced, with one year of access to all recordings and labs.

Is the training live or recorded?

Live and instructor-led, so you can ask questions and build alongside the trainer. Recordings are available after each session.

Is it hands-on or just theory?

Very hands-on. The core program has 37 labs where you build agent loops, subagents, MCP integrations, Claude Code workflows, tests, docs and CI/CD pipelines, plus a capstone that combines 11 lab modules into one build.

What is the difference between the two plans?

Both include the full program, beginner courses, bonus modules, the certification prep kit and the capstone. Upskill + Job Prep adds resume and LinkedIn rebuilds, a targeted job search, mock interviews, negotiation guidance and on-job support.

Do you guarantee a job?

No one can honestly guarantee a job. Job Prep gives you the resume, visibility and interview practice that turn skills into offers. Outcomes still depend on your effort, prior experience and the market.

Can I pay in installments?

Yes. Upskill is 3 monthly payments of $797, and Upskill + Job Prep is 4 monthly payments of $1,097. Switch to Pay in Installments in the pricing section to see the details.

Can my employer sponsor a team?

Yes. Use the Contact Us button and our team will share team pricing and invoicing options.

Ready to build agents that ship?

Join the next cohort and build real agentic systems with Claude and Claude Code.

See pricing & enroll →
Claude Code for Agentic AI Mastery, by K21 Academy
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