Get a $150K–$300K+ 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.

4 layers· 20+ modules· Weekly live sessions· 14 hands-on projects· AWS certifications· 1-year on-job support
46,000+ professionals trained
$150K–$300K+ salary range
14 production AWS projects
4.6/5 on Trustpilot · 267+ reviews

Our coaches and alumni work at these companies

JPMorgan
Microsoft
Accenture
Wipro
TCS
Infosys
Capgemini
PwC
Red Hat
AT&T
BNY
Spectrum
WTW
TD
Verified reviews

What our learners say

4.6 Trustpilot & Google
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. The depth is the point.

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 the common labs & troubleshooting everyone needs.
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
  • CLF-C02 exam structure, domains and question types
  • ⚡ 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
  • AIF-C01 exam structure, question types, and domain breakdown
  • ⚡ 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
Python logoPython 3
NumPy logoNumPy
Pandas logoPandas
scikit-learn logoscikit-learn
Matplotlib logoMatplotlib
Jupyter logoJupyter
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
Feature engineering encoding categorical variables, feature creation, binning, scaling and 4 more topics & labs.
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
Supervised ML overview regression vs classification, labelled data and 10 more topics & labs.
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
Decision Trees splitting criteria, Gini impurity, information gain, pruning and 5 more topics & labs.
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
Unsupervised ML overview clustering, dimensionality reduction, anomaly detection and 9 more topics & labs.
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 Certified Solutions Architect – Associate badge
For Everyone
AWS Solutions Architect
Design, govern, and scale enterprise solutions on AWS security, storage, compute, networking, databases, and the Well-Architected Framework. The full Solutions Architect path.
Certification
AWS Certified Solutions Architect – Associate (SAA-C03)
10 Modules Covered
INTROIntroduction to AWS & Cloud
  • Introduction to AWS Cloud (Theory & Labs)
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
  • ⚡ Amazon Athena
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 Solution Architect Exam Preparation
  • Full exam prep, domain review, practice questions & mock tests
AWS Certified 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 Certified 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. The full Machine Learning path.
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

14 production-grade AWS AI/ML projects.

Not toy scripts. Each one is built, deployed, and documented the way real teams ship on AWS across SageMaker, Bedrock, and MLOps.

Project 01

Predict University Admission SageMaker Canvas

Use Amazon SageMaker Canvas and Autopilot to predict university admission outcomes with no code. See how AutoML selects features, trains models, and scores new applicants.

SageMaker CanvasAutopilotNo-Code ML
Project 02

Predicting Customer Churn with ML

Train a classification model that flags customers likely to leave. Engineer features, evaluate metrics, and interpret the drivers behind each prediction.

SageMakerClassificationXGBoost
Project 03

Image Semantic Segmentation

Build a computer-vision model on SageMaker that labels every pixel in an image. Prepare the dataset, train the segmentation model, and visualize the output masks.

SageMakerComputer VisionSegmentation
Project 04

RAG with Bedrock & SageMaker

Build a Retrieval-Augmented Generation pipeline on Amazon Bedrock with a SageMaker notebook. Chunk documents, create embeddings, and ground answers in your own data.

BedrockRAGSageMaker
Project 05

Credit Card Fraud Detection

Detect fraudulent transactions on highly imbalanced data using Amazon SageMaker. Handle class imbalance, train the model, and tune it for precision and recall.

SageMakerAnomaly DetectionClassification
Project 06

MLOps Pipeline in SageMaker

Automate training and deployment with Amazon SageMaker Pipelines. Wire up the steps, register models, and promote them through a repeatable workflow.

SageMaker PipelinesMLOpsCI/CD
Project 07

End-to-End MLOps Pipeline

Ship a complete MLOps workflow on AWS SageMaker from raw data to a live, monitored endpoint. Cover build, deploy, and continuous monitoring in one project.

SageMakerMLOpsModel Registry
Project 08

Real-Time Stock Data Processing

Ingest streaming market data and run ML inference in near real time on AWS. Combine Kinesis streaming with SageMaker to score data as it arrives.

KinesisSageMakerStreaming
Project 09

Advanced Crypto AI Agents on Bedrock

Build advanced AI agents on Amazon Bedrock that reason over crypto market data. Give them tools and action groups so they can analyze and respond autonomously.

Bedrock AgentsGenAITool Use
Project 10

Deploy CloudMart on Lightsail

Deploy a containerized application to AWS Lightsail Containers end to end. Package the app, configure the container service, and ship it to a public endpoint.

LightsailContainersDeployment
Project 11

Heart Disease Prediction Canvas

Predict heart-disease risk with no-code ML using Amazon SageMaker Canvas. Load clinical data, train a model, and interpret which factors drive the risk score.

SageMaker CanvasNo-Code MLHealthcare
Project 12

AWS Data Strategy with AI/ML Services

Design a data strategy that combines AWS data and AI/ML services for analytics. Map how data flows from ingestion through to insight across the AWS stack.

Data StrategyAI/ML ServicesAnalytics
Project 13

Evaluating AWS Bedrock Models

Compare and evaluate Amazon Bedrock foundation models for quality, latency, and cost. Run structured evaluations to choose the right model for each use case.

BedrockModel EvaluationGenAI
Project 14

AI Stylist Bedrock + Stable Diffusion

Generate personalized outfit recommendations using Amazon Bedrock, SageMaker, and Stable Diffusion. Combine text and image generation into one GenAI application.

BedrockStable DiffusionSageMaker
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

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

One of the best learning and professional growth experiences

I am 3 weeks into the AI/ML program. The support is extremely awesome for labs, doubts and even resume updates. Atul Sir is always available to address your concerns and provide career guidance. I am extremely impressed with all the live class teachers. The job and resume support is great. The team creates a custom game plan / learning path for everyone. The real world project experiences and hands-on lab experience is a key differentiator it sets this program apart from anything else I've tried.

GP
Gaj Paranjape
AI/ML Program · USA
Trustpilot

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
Trustpilot

K21 Experience AI/ML Track gave me direction

I recently joined the AI/ML track course at K21 Academy and it has been a really helpful experience. When I first started learning AI and AWS, I was quite confused about where to begin and what direction to take. The structured curriculum, supportive mentors, and proper guidance have given me a lot of clarity. I also had a very positive experience working with Supriya at K21 Academy. She took the time to truly listen to my concerns and challenges, and I really appreciated her patience and attention to detail. I now feel more confident in my learning journey.

RS
Rahul Syal
AI/ML Learner · India
Google

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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Poulami Beginner → Cloud Architect
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Meghana Career gap → working with BT
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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.

14 Hands-On Projects on GitHub

Build a production-grade AWS AI/ML portfolio: SageMaker training pipelines, Bedrock RAG apps, MLOps deployments, and more all on GitHub.

AWS AI/ML Certifications

AWS Cloud Practitioner (CLF-C02), AWS AI Practitioner (AIF-C01), plus Solutions Architect (SAA-C03), Generative AI Developer (AIP-C01), and Machine Learning Engineer (MLA-C01). Exam prep, practice tests, and guided revision included.

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.

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

Job Prep Program

One program.
Apply to get started.

Submit your application to get started.

Best Value
AWS AI/ML Job Prep Program
The full job system
  • Weekly LIVE sessions
  • 100+ hands-on AWS AI/ML labs
  • 14 real-world AWS AI/ML projects on GitHub
  • 500+ practice questions
  • Cert prep: AWS CLF-C02, AIF-C01, SAA-C03, AIP-C01 & MLA-C01
  • Resume + LinkedIn rebuild for AWS AI/ML roles
  • Mock technical interviews + 1-year on-job support
  • EKS Bonus Webinar included
  • Community access
APPLY NOW
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. I highly recommend K21 Academy to anyone preparing for AI/ML certifications.

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, 14 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.
What job roles can I apply for after completing the program?
Machine Learning Engineer, AI/ML Engineer, MLOps Engineer, Generative AI Developer, Amazon SageMaker Specialist, AWS AI/ML Solutions Architect, Data Scientist, and ML Platform Engineer roles.
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.
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.
Are installment options available?
Yes, flexible payment options may be available. Submit the application to see which option you qualify for.
What tools and services will I learn?
Amazon SageMaker (Studio, Canvas, Autopilot, Data Wrangler, Feature Store, Pipelines, Model Monitor, Clarify), Amazon Bedrock (Agents, Knowledge Bases, Guardrails), boto3, Lambda, API Gateway, S3, EC2, Amazon EKS, Step Functions, Kinesis, OpenSearch, CloudWatch, CloudTrail, and Docker plus the Python ML stack (scikit-learn, XGBoost, pandas, NumPy).
How many projects will I build?
14 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.

APPLY NOW

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

AWS AI/ML Job Program by K21 Academy