Get a $120K–$250K+ Job in AWS Cloud and AI (Gen AI and Agentic AI).

A hands-on program where you build, train, and deploy real AWS AI/ML systems Amazon SageMaker, Amazon Bedrock, and production MLOps built for engineers, data professionals, and developers who learn by building it, not by watching slides.

18 modules· 55+ hands-on labs· 17 real-world projects· 6 AWS certifications· Weekly live sessions· 1-year on-job support
AWS AI/ML, Gen AI & Agentic AI job-oriented program with 6 certifications
46,000+professionals trained
$120K–$250K+salary range
17real-world projects
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4.6/5 on Trustpilot · 267+ reviews

Our coaches and alumni work at these companies

JPMorgan
Microsoft
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Verified reviews

What our learners say

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4.6 Trustpilot & Google
The complete program at a glance

Everything you get, on one map.

Beginner courses, 17 modules and 70+ hands-on labs, 6 AWS certifications, 17 real-world projects, and full job support – the entire roadmap in a single view. Tap the image to zoom in.

AWS AI/ML, Gen AI & Agentic AI job-oriented program roadmap: beginner courses (Cloud, AWS, AI/ML/GenAI for beginners), 17 modules and 70+ hands-on labs (M0-M17), 6 certifications (AWS Cloud Practitioner, Solutions Architect, AI Practitioner, ML Engineer, Generative AI Developer, Python for AI/ML), 17 real-world projects (P1-P17), plus resume, mock interview, profile & authority, and on-job support. ⤢ Hover to zoom
AWS AI/ML Career Roadmap

4 layers. All for everyone.
Each one goes deeper.

Every learner completes the same progressive path from cloud and AWS foundations, through AI/ML foundations and Python for Machine Learning, and all the way into the AWS specializations - Solutions Architect, Generative AI Developer, and Machine Learning - all included for every learner.

Layer 1: Common Foundation for Everyone
Cloud for Beginners
For Everyone
Cloud for Beginners
What is cloud computing and why it matters - AWS, Azure, Google & Oracle concepts and 8 more core concepts.
Modules Covered
M00 Cloud for Beginners
▼
  • What is Cloud Computing and why it matters
  • Public vs Private vs Hybrid Cloud
  • IaaS, PaaS, SaaS - what each is good for
  • Overview of AWS, Google Cloud, Oracle Cloud, Azure
  • Core cloud concepts: regions, availability zones, scalability
  • Cloud pricing models and cost basics
  • Cloud security fundamentals
  • ⚡ Lab: Explore free tiers across cloud providers
AWS for Beginners
For Everyone
AWS for Beginners
Core AWS services, pricing, free tier, billing setup - EC2, S3, RDS, IAM, VPC and 8+ core concepts.
Modules Covered
M01 AWS for Beginners
▼
  • What is Cloud Computing and why AWS
  • EC2: instance types, AMIs, pricing models
  • S3: buckets, storage classes, lifecycle
  • RDS Managed Databases: MySQL, PostgreSQL, Aurora basics
  • IAM - Users, groups, roles, and permission policies
  • VPC - Virtual Private Cloud, subnets, security groups
  • AWS Pricing Calculator & Cost Explorer overview
  • ⚡ Lab: Create an AWS Free Tier Account
  • ⚡ Lab: Set Up Billing Alerts & Budget Controls
AI, ML & GenAI for Beginners
For Everyone
AI, ML & GenAI for Beginners
What is AI, Machine Learning, Deep Learning, GenAI and LLMs and 10+ more core concepts.
Modules Covered
M02 AI/ML/GenAI Beginners
▼
  • What is AI, Machine Learning, and Deep Learning?
  • Generative AI and Large Language Models explained simply
  • Supervised vs unsupervised vs reinforcement learning
  • Real-world AI/ML use cases across industries
  • Introduction to foundation models: Claude, Titan, Llama
  • How models learn: training, inference, and evaluation
  • AI Safety and responsible AI basics
  • ⚡ Lab: Explore a foundation model in a playground
  • ⚡ Lab: Build your first simple AI chatbot
AWS AI/ML for Beginners
For Everyone
AWS AI/ML for Beginners
Get hands-on with the AWS AI/ML stack - SageMaker, Bedrock, building APIs, plus common labs & troubleshooting.
Modules Covered
M03 AWS AI/ML Beginners
▼
  • Overview of the AWS AI/ML stack - SageMaker, Bedrock & AI services
  • Getting started with Amazon SageMaker Studio & Domains
  • Amazon Bedrock foundation models, playground & chat
  • Building APIs for AI/ML services with boto3, Lambda & API Gateway
  • Common setup & troubleshooting issues (IAM, quotas, regions)
  • ⚡ Common Lab: Create a SageMaker Domain & Studio environment
  • ⚡ Common Lab: Chat with foundation models in Amazon Bedrock
  • ⚡ Common Lab: Build & deploy an inference API (boto3 + Lambda)
  • ⚡ Common Lab: Troubleshooting AWS AI/ML IAM, quotas & regions
↓
Layer 2: AWS Certifications for Everyone
AWS Cloud Practitioner (CLF-C02)
For Everyone
AWS Cloud Practitioner (CLF-C02)
Foundational AWS cloud concepts, core services, security, pricing and billing - full CLF-C02 exam prep included.
Modules Covered
CLF-C02 AWS Cloud Practitioner
▼
  • Cloud concepts, value proposition and the AWS Cloud
  • Core AWS services: compute, storage, database, networking
  • AWS security, shared responsibility model and IAM basics
  • Pricing, billing, cost management and support plans
  • AWS Well-Architected Framework overview
  • ⚡ Lab: CLF-C02 Full Mock Exam Practice Test 1
  • ⚡ Lab: CLF-C02 Full Mock Exam Practice Test 2
AWS AI Practitioner (AIF-C01)
For Everyone
AWS AI Practitioner (AIF-C01)
Foundational AI/ML on AWS, Amazon Bedrock, generative AI, responsible AI - full AIF-C01 exam prep included.
Modules Covered
AIF-C01 AWS AI Practitioner
▼
  • AI, ML, and Deep Learning fundamentals for AWS
  • AWS AI/ML services: SageMaker, Bedrock, Rekognition, Polly, Lex
  • Generative AI on AWS - Amazon Bedrock, Titan & Claude models
  • Foundation Models, prompt engineering, and model customization
  • Responsible AI, bias detection, and governance on AWS
  • ⚡ Lab: AIF-C01 Full Mock Exam Practice Test 1
  • ⚡ Lab: AIF-C01 Full Mock Exam Practice Test 2
⚡ Bonus Module Value Add
MLOps on AWS
Included as an additional bonus for every learner - take ML models to production on AWS with SageMaker MLOps, containerization, Amazon EKS, CI/CD, and scalable model serving.
MLOps on AWS
Modules Covered
MLOps on AWS Bonus
▼
  • MLOps fundamentals - the ML lifecycle in production
  • Containerizing ML models with Docker
  • Amazon EKS (Elastic Kubernetes Service) essentials for ML
  • Deploying & serving models on EKS (pods, services, autoscaling)
  • CI/CD pipelines for ML on AWS
  • Monitoring, logging & rollout strategies on EKS
  • ⚡ Lab: Deploy an ML model to Amazon EKS
  • ⚡ Lab: Build a CI/CD pipeline for model serving on EKS
↓
Layer 3: Python for AI/ML, GenAI & Agentic AI
PythonPython 3
NumPyNumPy
PandasPandas
scikit-learnscikit-learn
MatplotlibMatplotlib
JupyterJupyter
For Everyone
Introduction to Python for Machine Learning
Overview of Python key features and benefits for AI/ML and 11 more topics & labs.
Topics & Labs
Introduction to Python for ML
▼
  • Overview of Python key features and benefits for AI/ML
  • Setting up the Python environment (Anaconda, Jupyter, VSCode)
  • 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
For Everyone
Python Data Structures, Control Flow & Functions
Tuples definition, use cases, hands-on and 16 more topics & labs.
Topics & Labs
Python Data Structures
▼
  • 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
For Everyone
Object-Oriented Programming (OOP)
OOP Part 1: objects, classes, attributes, methods, __init__ and 4 more topics & labs.
Topics & Labs
Object-Oriented Programming (OOP)
▼
  • OOP Part 1: 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)
For Everyone
Introducing Machine Learning in Detail
Machine Learning overview - types, workflow, real-world applications and 12 more topics & labs.
Topics & Labs
Introducing Machine Learning
▼
  • Machine Learning overview - types, workflow, real-world applications
  • NumPy arrays, operations, broadcasting
  • Pandas DataFrames, indexing, groupby, merge
  • Data cleaning - missing values, duplicates, outliers
  • Exploratory Data Analysis (EDA) with Matplotlib and Seaborn
  • Train/test split, cross-validation, and bias-variance tradeoff
  • ⚡ Lab: Introducing Machine Learning in Detail
⚡ Bonus Modules Value Add
⚡ Bonus Module
EDA & Feature Engineering
Topics & Labs
EDA & Feature Engineering
▼
  • Feature engineering - encoding categorical variables
  • Feature creation, binning, and scaling (MinMax, Standard)
  • Dimensionality reduction with PCA
  • Correlation analysis and feature selection techniques
  • Pipelines in scikit-learn for reproducible preprocessing
  • ⚡ Lab: EDA & Feature Engineering
⚡ Bonus Module
Supervised Machine Learning
Topics & Labs
Supervised Machine Learning
▼
  • Supervised ML overview - 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 Module
Ensemble Learning
Topics & Labs
Ensemble Learning
▼
  • 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 Module
Unsupervised Machine Learning
Topics & Labs
Unsupervised Machine Learning
▼
  • Unsupervised ML overview - clustering, dimensionality reduction
  • K-Means Clustering - algorithm, 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
↓
Layer 4: AWS Specializations for Everyone
All 3 included for everyone
Solutions Architect, Generative AI Developer & Machine Learning - one layer, all included.
Nothing to pick. Every learner completes all three specializations together and preps for all three AWS certifications.
AWS Solutions Architect badge
For Everyone
AWS Solutions Architect
Design, govern, and scale enterprise solutions on AWS - security, storage, compute, networking, databases, and the Well-Architected Framework.
Certification
AWS Certified Solutions Architect – Associate (SAA-C03)
10 Modules Covered
M01Security Management in AWS
  • AWS Shared Responsibility Model
  • Identity and Access Management (IAM)
  • IAM Roles, Federation & SSO
  • AWS Organizations, Cognito, WAF, Shield
  • AWS GuardDuty, Inspector, KMS, Certificate Manager
  • ⚡ Working with AWS IAM
  • ⚡ Enable Multi-Factor Authentication
  • ⚡ AWS KMS Create & Use
M02Object Storage Options
  • Simple Storage Service (S3) & Bucket Policy
  • S3 Object Versioning, Cross Region Replication
  • Storage Classes, AWS Snow Family, DataSync
  • ⚡ Create S3 Bucket, Upload & Access Files
  • ⚡ S3 Cross Region Replication
  • ⚡ Deliver Content Faster (Amazon CloudFront)
M03Designing Computing Environment
  • Amazon EC2, AMI, Key Pairs, Security Groups
  • EC2 Purchasing Options, Placement Groups
  • EBS Snapshots, Amazon EFS, Amazon FSx
  • ⚡ Configure Webserver on Windows EC2
  • ⚡ Create & Manage EBS Volumes & Snapshots
  • ⚡ Create & Mount EFS on EC2 Instance
M04Load Balancer, Route 53 & Auto Scaling
  • Elastic Load Balancer types & comparison
  • Autoscaling types and lifecycle
  • AWS Route 53 & working
  • ⚡ Configure Network Load Balancer
  • ⚡ Configure a Load Balancer & Autoscaling
  • ⚡ Register a domain & Map using Route 53
M05Networking & Monitoring Services
  • VPC components, subnets, Internet Gateway
  • VPN, Direct Connect, CloudWatch, CloudTrail
  • AWS Config, Global Accelerator
  • ⚡ Create Custom Virtual Private Cloud
  • ⚡ Configure Amazon CloudWatch
  • ⚡ Working with Transit Gateways
M06Database Server and Analytics
  • Amazon RDS, DynamoDB, ElastiCache, Redshift
  • Amazon Kinesis, Aurora, DynamoDB Global Table
  • DynamoDB DAX, Memcached & Redis
  • ⚡ Create & Query with Amazon DynamoDB
  • ⚡ Configure a MySQL DB Instance
M07Application & Messaging Services
  • AWS SES, SNS, SQS, Serverless Computing
  • Amazon AppFlow, Pinpoint, SWF
  • ⚡ Send an Email Through AWS SES
  • ⚡ Event-Driven Architectures
M08Configuration Management & Automation
  • Amazon CloudFormation, Elastic Beanstalk, OpsWorks
  • YAML Basics for Docker & Kubernetes
  • ⚡ Create & Update Stacks Using CloudFormation
  • ⚡ Deploy a Web Application (Elastic Beanstalk)
M09Architecting on AWS
  • Why We Need the Well-Architected Framework
  • Security, Reliability, Performance Efficiency Pillars
  • Cost Optimization & Operational Excellence Pillars
  • Resilience, Disaster Recovery, Decoupling Services
M10AWS Solutions Architect Exam Prep
  • Full exam prep, domain review, practice questions & mock tests
AWS Generative AI Developer badge
For Everyone
AWS Generative AI Developer
Build, integrate, and ship production generative AI apps on AWS - Amazon Bedrock, RAG, agents, fine-tuning, guardrails, and deployment with Python and AWS.
Certification
AWS Certified Generative AI Developer – Professional (AIP-C01)
11 Modules Covered
M01Generative AI Foundation & AWS Ecosystem
  • AI vs ML vs DL vs Generative AI
  • Introduction to Agentic AI (AI Agents)
  • AWS AI/ML Service Stack
  • SageMaker Studio & MLflow
  • ⚡ Invoke FMs to generate Text & Image @Console
M02Accessing Amazon Bedrock FM Using API
  • Foundation Models & LLMs, Tokenization, Transformers
  • Amazon Bedrock, Guardrails, Safety & Responsible AI
  • Building RAG with Amazon Bedrock
  • ⚡ Invoke FM for Text, Image & Code generation via API
  • ⚡ Build & Deploy an AI Chatbot with Bedrock & Lambda
M03Prompt Engineering Fundamentals
  • Configuring Access & Model Pricing
  • Bedrock API Fundamentals
  • ⚡ Prompt Techniques using Bedrock FM @Console
  • ⚡ Invoke Zero-Shot Prompt for Text Gen via API
M04Retrieval-Augmented Generation (RAG)
  • Core & Advanced Prompting Techniques
  • RAG Lifecycle, Concepts & Architecture
  • Vector Embeddings & Search, AWS RAG Architecture
  • ⚡ Building a RAG using @Console
  • ⚡ Building an End-to-End RAG System with API
M05Agentic AI and Workflow Orchestration
  • Introduction to Agentic AI
  • Core Agent Architecture, Multi-Agent Systems
  • ⚡ Build a Bedrock Agent with Action Groups
  • ⚡ Bedrock model integration with LangChain Agents
M06Data Validation, Processing & Enhancement
  • Designing Complex Workflows
  • Data Quality Rules, Monitoring, and Profiling
  • ⚡ Design Scalable Pipelines: Glue & Step Functions
M07FMs Customization and Deployment
  • Fine-Tuning & Continued Pre-Training
  • Bedrock Guardrails, PII Protection & Compliance
  • ⚡ Create a Bedrock Custom Model with Fine-tuning
M08Security, Governance, and Responsible AI
  • Content Safety with Amazon Bedrock Guardrails
  • Security & Encryption in GenAI Systems
M09Operational Efficiency and Monitoring
  • Reducing Inference Latency
  • Logging & Monitoring FM Applications
  • RAISE Framework & Model Evaluation, LLM as Judge
M10AWS AI Managed Services
  • Amazon Lex, Personalize, Polly, Rekognition, Forecast
  • ⚡ Amazon Polly and Rekognition
  • ⚡ Smart AI-Powered Search & Recommendation System
M11Exam Preparation
  • Full exam prep, domain review, practice questions & mock tests
AWS Machine Learning Engineer badge
For Everyone
AWS Machine Learning
Ingest data, engineer features, train, tune, deploy, and operate ML models on Amazon SageMaker - plus MLOps, security, and governance.
Certification
AWS Certified Machine Learning Engineer – Associate (MLA-C01)
11 Modules Covered
M01AWS Data Ingestion
  • S3, Athena, AWS Glue, Lake Formation, Kinesis for ML
  • ⚡ AWS Glue & Athena: Analyze CSV Data in S3
  • ⚡ Data Pipeline using Kinesis, Spark, and S3
M02Amazon EBS and Kinesis Data Streams
  • EBS Snapshots, EFS, FSx, AWS Kinesis
  • ⚡ Amazon Kinesis Data Streams – Hands-On
M03Data Transformation & Integrity
  • AWS Glue, Feature Engineering, Data Wrangler
  • Data Quality & Validation, SageMaker Feature Store
  • ⚡ Data Preprocessing with DataBrew
  • ⚡ Streamlining Data Analysis with SageMaker
M04Amazon SageMaker and Built-In Algorithms
  • Introduction to SageMaker & Studio setup
  • SageMaker Features, Capabilities & Built-In Algorithms
  • ⚡ Build, Train, Deploy a Model Using No-Code
M05Model Training, Tuning, and Evaluation
  • Distributed Training, Hyperparameters for AWS
  • Model Evaluation Metrics, LLM as Judge
  • ⚡ Hyperparameter Optimization using SageMaker
  • ⚡ Build, Train & Deploy ML Model @SageMaker AI
M06Generative AI Model Fundamentals
  • Transformers, Tokenization, Embeddings & Vector Search
  • Fine-tuning vs RAG vs Training, Foundation Models
  • ⚡ Exploring Transformers, Tokenization & GPT-2
M07Developing Generative AI Applications
  • Amazon Bedrock, Prompt Engineering, RAG, Agents
  • ⚡ Building a RAG using @Console
  • ⚡ Build a Bedrock Agent with Action Groups
M08MLOps
  • MLOps on AWS, Containers & Docker, Amazon EKS
  • CloudFormation, SageMaker Model Registry & MLflow
  • ⚡ Install Docker, Create Image & Push Image
M09Security, Identity, and Compliance
  • IAM Best Practices, AWS KMS, Secrets Manager
  • ⚡ Working with AWS IAM
M10Management and Governance
  • CloudWatch Logs, CloudTrail, AWS Config
  • Data Drift & Model Drift, Cost Optimization for ML
  • ⚡ Get Started with AWS X-Ray
M11Machine Learning Best Practices
  • AWS Machine Learning Lifecycle
  • Responsible AI for ML, Adversarial Machine Learning
Your portfolio, on GitHub

17 real-world AI/ML, GenAI & Agentic AI projects.

Not toy scripts. Every project is built and deployed on AWS - across SageMaker, Bedrock, Agentic AI and MLOps - exactly how real teams ship in production.

P01 · Gen AI & Agents

AI Loan Advisory System on AWS

Build a generative AI assistant to analyze user profiles and provide personalized loan recommendations using Bedrock and DynamoDB.

BedrockDynamoDBLambda
P02 · Machine Learning

Predict University Admission using SageMaker Canvas

Use no-code ML to predict university admission chances based on academic and test scores with SageMaker Canvas & Autopilot.

SageMaker CanvasAutopilotNo-Code ML
P03 · Computer Vision

Image Semantic Segmentation using AWS SageMaker

Build a deep learning model for pixel-level image segmentation - applications include autonomous driving and medical imaging.

SageMakerComputer VisionLambda
P04 · Machine Learning

Credit Card Fraud Detection using SageMaker

Detect fraudulent transactions using ML models (Random Cut Forest, XGBoost) with real-time API deployment on SageMaker.

SageMakerAPI GatewayLambda
P05 · Gen AI & RAG

Build RAG using AWS Bedrock & SageMaker

Create a Retrieval-Augmented Generation application using Bedrock and SageMaker Notebooks. Ground answers in your own documents with Kendra.

BedrockKendraSageMaker
P06 · MLOps & Deployment

End-to-End MLOps Pipeline using SageMaker

Automate ML model training, validation and deployment using SageMaker Pipelines, CodePipeline, and ECR.

SageMaker PipelinesCodePipelineECR
P07 · Gen AI & Agents

Advanced Crypto AI Agents on Amazon Bedrock

Build real-time AI agents to analyze crypto market data, generate insights, and automate trading strategies using Bedrock and DynamoDB.

Bedrock AgentsLambdaDynamoDB
P08 · Machine Learning

Heart Disease Prediction using SageMaker Canvas

Use no-code ML to predict heart disease risk based on patient health data and medical history with SageMaker Canvas.

SageMaker CanvasS3QuickSight
P09 · Gen AI & Agents

Evaluating AWS Bedrock Models

Compare and evaluate different foundation models on metrics like accuracy, cost and latency using CloudWatch and Bedrock.

BedrockCloudWatchS3
P10 · Gen AI & Agents

AI Stylist – Personalized Outfit Recommendations

Build a generative AI application to recommend personalized outfits using Amazon Bedrock and SageMaker image generation.

BedrockS3SageMaker
P11 · Data & Analytics

AWS Data Strategy using Data & AI/ML Services

Build a modern data pipeline and analytics solution using AWS data and AI/ML services across S3, Glue, and Redshift.

S3AWS GlueRedshift
P12 · Data & Analytics

Real-Time Stock Data Processing

Ingest, process and analyze real-time stock market data using AWS streaming and analytics services with Kinesis and Lambda.

KinesisLambdaQuickSight
P13 · Machine Learning

Customer Churn Prediction using ML Model

Predict customer churn using ML models and identify key churn factors using SageMaker and QuickSight for visualization.

SageMakerS3QuickSight
P14 · Cloud & Infrastructure

Deploy CloudMart on AWS Lightsail

Deploy a containerized e-commerce application (CloudMart) using AWS Lightsail with ECR and RDS backend.

LightsailECRRDS
P15 · MLOps & Deployment

Building ML Pipeline using AWS SageMaker

Create a complete ML pipeline with data processing, training, model registry and deployment using SageMaker and Step Functions.

SageMakerStep FunctionsS3
P16 · Gen AI & Agents

Build Prod-Ready AI Agents with AgentCore

Develop production-ready multi-agent systems using Amazon Bedrock AgentCore - deployed, monitored, and enterprise-ready.

Bedrock AgentCoreLambdaDynamoDB
P17 · Responsible AI

Secure Multi-Agent Patient Support Assistant

Build a secure healthcare multi-agent AI assistant with guardrails, privacy, and compliance considerations using Comprehend Medical.

BedrockComprehend MedicalIAM
Real transitions. Real offers.

They got the job.
Here's how.

Public reviews from K21 Academy learners who landed AWS AI/ML roles - real names, verified on Google and Trustpilot.

Trustpilot
★
★
★
★
★

K21 Academy: The Real Deal for Tech Leaders Transitioning into AI/ML and Cloud

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 AI/ML/GenAI/Agentic AI Track Program has been one of the best professional decisions I've made this year. Since joining, I've completed four courses - Python, Mastery Gen AI, Agentic AI, and MLOps in AWS - and successfully passed three Microsoft certification exams: AI-900, AZ-900, and AB-731. What sets K21 apart is the depth of knowledge their trainers bring. The hands-on projects on both Azure and AWS gave me practical experience that actually shows up on a resume and in interviews.

MO✓
Marcos Oliveira
VP of Software Engineering · California, USA
Google
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A one-stop-shop for a great AI/ML career transition experience

K21 Academy is a great professional institution to meet your AI/ML career aspirations, and has the right training, courseware and job application, interview and placement support to help students achieve their goals. I particularly like the quality and depth of the faculty's knowledge, so conducive to learning. With AI/ML growing at such a rapid pace, it is imperative that we get connected with the right industry professionals who bring their experience and expertise to help students transition into a meaningful AI/ML career.

SS✓
Sanjeev Saksena
AI/ML Career Transition
Trustpilot
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K21 Academy gave me clarity and confidence in my AI journey

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. One of K21 Academy's biggest strengths is its AI curriculum. The content is exhaustive and serves as a one-stop shop for AI and ML. I enrolled in the Full Stack AI program across multiple cloud platforms, and despite the significant time commitment, I haven't regretted it for a single moment.

GB✓
Gunita Bajaj
Full Stack AI Program · USA
Google
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From non-technical background to confident AI learner

I decided to explore AI as part of a career change, but coming from a non-technical background made me hesitant to enroll in any course. With the encouragement and support of my husband, I joined K21 Academy run by Atul Kumar and I'm really glad I did. The course is well-structured, catering to both beginners and advanced learners. What stands out the most is the thoughtful design of the program and the strong support system provided by the team. The emphasis on hands-on experience truly enhances the learning process.

MV✓
Monica Varry
AI Career Changer
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. Tap any video to play.

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More learners who landed roles through this program
SteveSteve AI Engineer
✓SJ Salary doubled, husband hired too
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✓Devesh Solutions Architect
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GopinathGopinath Cloud Architect
✓Srikar AWS Architect
AartiAarti Informatica & AWS Developer ToluTolu Daramola No IT background → 2 job offers
✓Poulami Beginner → Cloud Architect
✓Kennedy Senior Technical Advisor (UK)
✓Meghana Career gap → working with BT
✓Kwame Job offer, relocated to USA
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.

▷

Live Weekly Sessions

Interactive cohort, not recorded videos. Real-time Q&A with AWS AI/ML experts. Weekend live sessions with 24-hour recording access.

⊞

17 Real-World Projects on GitHub

Build a verified AI/ML, GenAI & Agentic AI portfolio - 17 projects covering SageMaker, Bedrock, Agentic AI, MLOps, and more, all pushed to GitHub and ready to show employers.

☆

6 AWS Certifications Covered

CLF-C02 AWS Cloud Practitioner · AIF-C01 AWS AI Practitioner · SAA-C03 Solutions Architect · MLA-C01 ML Engineer · AIP-C01 Generative AI Developer · Python for AI/ML. Full exam prep, mock tests, and guided revision included for every cert.

≡

Resume + LinkedIn Makeover

Your profile optimized for AWS AI/ML roles: ATS-ready, keyword-optimized for SageMaker, Bedrock and MLOps jobs, and recruiter-tested across US, UK, Canada, and UAE markets.

☰

Mock Technical Interviews

Live simulation covering ML system design, SageMaker & MLOps workflows, Bedrock / GenAI architecture questions, and behavioral rounds.

⊕

1-Year On-Job Support

Support continues after you're hired through your first 90 days and beyond. We're invested in your outcome, not just your enrollment.

⚡ Limited Time Bonus - 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.

1
Docker & Kubernetes on AWS
Master containerization and orchestration fundamentals - package ML models with Docker, deploy and scale them on Kubernetes (EKS), and build production-grade container pipelines on AWS.
DockerKubernetesAmazon EKSECR
2
MLOps Engineering on AWS
Go beyond training - build automated ML pipelines on AWS using SageMaker Pipelines, CI/CD with CodePipeline, model monitoring, drift detection, and rollout strategies at scale.
SageMaker PipelinesCI/CDCodePipelineModel Monitor

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

⚡ Next cohort filling fast - limited seats available. Early applicants get priority placement.

Job Prep Program

One program.
Two ways to pay.

Pay once or split it into 4 payments - same program, same projects, same support.

BEST VALUE
AI/ML/GenAI/Agentic AI Job-Oriented Program
$3,997 $2,997
Save $1,000 – one payment
  • ✓ Weekly LIVE interactive sessions
  • ✓ 55+ hands-on labs & projects for practice
  • ✓ 17 real-world AI/ML, GenAI & Agentic AI projects on GitHub
  • ✓ 500+ practice questions
  • ✓ 6 certifications: CLF-C02, AIF-C01, SAA-C03, MLA-C01, AIP-C01 & Python for AI/ML
  • ✓ Resume + LinkedIn rebuild for AWS AI/ML roles
  • ✓ Mock technical interviews + 1-year on-job support
  • ✓ EKS Bonus Webinar included
  • ✓ Query support via WhatsApp, ticketing & live Q&A
  • ★ 100% job placement support
Enroll now – $3,997 $2,997 →
6-Month Money-Back Guarantee

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

Your decision is protected. Do the work, and if it doesn't deliver, you get your money back.

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

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

Job outcomes

They got hired.
Here's what they said.

Every one of these learners came to K21 Academy with a goal. Every one of them shipped real projects, landed interviews, and got hired.

"

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.

S
Steve
Generative AI Engineer
✓ Google review verified
"

I've successfully landed a job as an AI Engineer. Worked hands-on with cloud AI services and foundation models. Later received a second offer as a Generative AI Engineer. K21 gave me the skills and confidence to choose.

DD
Debasish Dash
AI Engineer → Generative AI Engineer
✓ Google review verified
"

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.

II
Ike Imala
Generative AI Engineer (contract-to-hire)
✓ Google review verified
"

Excited to share that I have successfully passed the AWS AI Practitioner (AIF-C01) certification! A big thank you to the K21 Academy team for their excellent training, well-structured course content, and continuous support.

AA
Adnan Ahmed
AI/ML Engineer AWS AIF-C01 Certified
✓ Trustpilot verified
Common questions

Before you enroll.

YouTube is free. Why pay for this?
YouTube gives you fragments. This program gives you a structured, progressive system - modules that build on each other, live weekly sessions where you actually build alongside AWS AI/ML experts, 17 hands-on projects you can show employers, AWS certification prep, resume optimization, mock interviews, and 12 months of support after you're hired.
I already know some ML and have built a few models.
Good - that means you can move faster through the foundations. But building models in a notebook is not the same as shipping production ML on AWS. This program covers Amazon SageMaker end to end, MLOps and SageMaker Pipelines, Amazon Bedrock and RAG, Bedrock Agents, guardrails, and deployment on EKS - the parts that actually get you hired.
I don't have time. I'm working full-time.
The live sessions run on weekends, with 24-hour recording access. Most learners complete the labs in focused 2–3 hour blocks during evenings or weekends. The curriculum is structured specifically for working professionals.
How long is the program?
The core program is delivered over weekly live sessions across approximately 12–16 weeks. Most learners complete projects and job-prep activities over 4–6 months. On-job support continues for 12 months from your enrollment date.
Do you offer a money-back guarantee?
Yes - a full 6-month action-based guarantee. Complete the labs and projects, apply to 50+ relevant roles, get your resume reviewed, and ask for support when needed. If you've done all of that and you're still not satisfied, you get a full refund. No fine print.
Can I pay in installments?
Yes - you can split the Job Prep program into 4 payments of $797 (total $3,188). Both the pay-in-full and installment options are shown via the toggle in the pricing section above.
Is your training live or recorded?
Live. Every session is a real-time interactive cohort. You can ask questions, debug together, and get feedback on your work. Recordings are available within 24 hours.
How many projects will I build?
17 hands-on projects across ML and generative AI - each built, deployed on AWS, and pushed to GitHub. You'll also build smaller lab projects throughout each module.

Ready to land your AWS AI/ML role?

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

View pricing & enroll →

★ 4.6/5 on Trustpilot · 267+ reviews · 6-month money-back guarantee

AWS AI/ML Job Program by K21 Academy