Get a $150K to $250K+ Job in AWS AI, ML, Gen AI & 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.
AWS AI/ML Career Roadmap
4 layers. All for everyone.
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 your chosen AWS specialization: Solutions Architect, Generative AI Developer, or Machine Learning. The depth is the point.
Layer 1 — Common Foundation for Everyone
For Everyone
For Everyone
For Everyone
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
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 — Compute: instance types, AMIs, pricing models
- S3 — Object Storage: 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
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
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
For Everyone
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)
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.
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 3
NumPy
Pandas
scikit-learn
Matplotlib
Jupyter
Python 3
NumPy
Pandas
scikit-learn
Matplotlib
JupyterFor 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
↓
Open to All
Track 1 — AWS Solution 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 – Part 1
- AWS Shared Responsibility Model – Part 2
- Identity and Access Management (IAM)
- IAM Components
- IAM Roles
- IAM Federation & SSO
- IAM Best Practices
- IAM Delegation, Audit, Identity & Credential Mgmt
- AWS Organizations
- AWS Cognito
- AWS WAF
- AWS Shield
- AWS GuardDuty & Firewall Manager
- AWS Inspector
- AWS KMS
- AWS Certificate Manager
- Accessing Billing
- ⚡ Working with AWS IAM
- ⚡ Enable Multi-Factor Authentication
- ⚡ AWS KMS Create & Use
M02Object Storage Options
- Object Storage Options
- Simple Storage Service (S3)
- S3 Bucket Policy
- Amazon S3 ACL
- S3 Object Versioning
- Cross Region Replication
- Transfer Acceleration in AWS S3
- Storage Classes
- Requester Pays, Object Locks
- Content Delivery Network
- AWS Snow Family
- AWS Storage Gateway
- S3 Glacier Vault & S3 Object Lock
- S3 Batch Operation
- AWS DataSync
- Bucket Level Security
- ⚡ Create S3 Bucket, Upload & Access Files
- ⚡ S3 Cross Region Replication
- ⚡ S3 Lifecycle Management on S3 Bucket
- ⚡ Deliver Content Faster (Amazon CloudFront)
M03Designing Computing Environment
- Amazon EC2, Tags
- Metadata & User Data
- Amazon Machine Image
- Key Pair in EC2
- AWS Security Groups
- EC2 Hardware Tenancy
- EC2 Purchasing Options
- Amazon EC2 Networking Layer
- Amazon EC2 Placement Groups
- AWS Storage Services
- EBS Snapshots
- Amazon Elastic File System
- SSM Session Manager
- Amazon FSx
- ⚡ Configure Webserver IIS on Windows EC2 Machine
- ⚡ AWS EC2 Instance Metadata
- ⚡ Create & Manage EBS Volumes & Snapshots
- ⚡ Attach & Mount EBS Volume to Linux EC2 Instance
- ⚡ Attach & Mount EBS Volume to Windows EC2 Instance
- ⚡ Create & Mount EFS on EC2 Instance
M04Load Balancer, Route 53 & Auto Scaling
- Elastic Load Balancer & Its Types
- Comparison Between the Load Balancers
- Autoscaling in AWS
- Types of Scaling
- Lifecycle of Autoscaling
- Limitations of Autoscaling
- Introduction to AWS Route 53
- Working of Route 53
- ⚡ Configure Network Load Balancer
- ⚡ Configure a Load Balancer & Autoscaling
- ⚡ Launch Template for Auto Scaling Group
- ⚡ Register a domain & Map using Route 53
M05Networking & Monitoring Services
- Introduction to VPC
- Default & Non-Default VPC
- Components of VPC
- IP Address
- Classless Inter-Domain Routing (CIDR)
- Subnet and Subnet Masks
- IPv4 vs IPv6
- Public & Private Subnet, Internet Gateway
- VPN and Direct Connect
- AWS CloudWatch
- CloudTrail
- AWS Trusted Advisor
- AWS Config
- Event-Driven Automated Actions
- CloudWatch & Its Working
- What is Monitoring Services
- Global Accelerator
- ⚡ Create Custom Virtual Private Cloud
- ⚡ Work with VPC Peering Connection
- ⚡ Configure Amazon CloudWatch
- ⚡ Enable CloudTrail and Store Logs in S3
- ⚡ Set AWS Config to Assess, Audit & Evaluate
- ⚡ Establish a Client-Side VPN (Parts 1–4)
- ⚡ Working with Transit Gateways
M06Database Server and Analytics
- Database and Its Types
- Amazon RDS and Its Benefits
- RDS Database Instance and Read Replica
- DB Instance Region and Availability Zones
- Amazon DynamoDB
- DynamoDB API and DynamoDB Streams
- Read Consistency in DynamoDB
- Amazon ElastiCache
- Amazon Redshift
- Amazon Kinesis
- Amazon Aurora
- RDS & Aurora Backups and Security
- Amazon DynamoDB Global Table
- DynamoDB Benefits & Use Cases
- Amazon DynamoDB Accelerator (DAX)
- Memcached & Redis
- ⚡ Create & Query with Amazon DynamoDB
- ⚡ Configure a MySQL DB Instance
- ⚡ Create a Redis Cache & Connect EC2 Instance
- ⚡ Amazon Athena
M07Application & Messaging Services
- AWS Application Services
- AWS SES
- AWS SNS
- AWS SQS
- Serverless Computing
- Amazon AppFlow
- Amazon Pinpoint
- Simple Workflow Service (SWF)
- Types of SQS
- Limitations of SES
- ⚡ Send an E-mail Through AWS SES
- ⚡ Event-Driven Architectures
M08Configuration Management & Automation
- Amazon CloudFormation
- Amazon Elastic Beanstalk
- Amazon OpsWorks
- Elastic Beanstalk vs OpsWorks vs CloudFormation
- YAML Basics for Docker
- YAML Basics for Kubernetes
- ⚡ Create & Update Stacks Using CloudFormation
- ⚡ Deploy a Web Application (Elastic Beanstalk)
M09Architecting on AWS
- Why Do We Need the Well-Architected Framework
- How to Build a Well-Architected Framework
- Pillars of the Well-Architected Framework
- Security Pillar
- Reliability Pillar
- Performance Efficiency Pillar
- Cost Optimization Pillar
- Operational Excellence Pillar
- Resilience & How to Calculate Availability
- Resources Handling During Failure
- Resilience and Databases
- Decoupling Services
- Multi-tier Architecture
- Disaster Recovery
- Design Performance Architecture
M10AWS Solution Architect Exam Preparation
- Full exam prep, domain review, practice questions & mock tests
Open to All
Track 2 — 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 Generative AI Developer – (AIP-C01)
11 Modules Covered
M01Generative AI Foundation & AWS Ecosystem
- ⚡ Prerequisite: Create an AWS Cloud Account
- ⚡ Prerequisite: Create a CloudWatch Billing Alert
- Introduction to Artificial Intelligence (AI)
- Machine Learning (ML) – Concepts & Types
- Deep Learning (DL) & Neural Networks
- Generative AI (GenAI)
- AI vs ML vs DL vs Generative AI
- Real-World Applications
- Introduction to Agentic AI (AI Agents)
- AWS AI/ML Service Stack
- SageMaker & the ML Lifecycle
- SageMaker Studio & MLflow
- ⚡ Invoke FMs to generate Text & Image @Console
M02Accessing Amazon Bedrock FM Using API
- Introduction to Foundation Models & LLMs
- Tokenization, Transformers & Embeddings
- FMs vs LLMs
- What is Amazon Bedrock?
- Foundation Models Available in Amazon Bedrock
- Strategy for Selecting Foundation Models
- Accessing Amazon Bedrock Using APIs
- Guardrails, Safety & Responsible AI
- Building RAG with Amazon Bedrock
- Amazon Q Developer & Q Business
- ⚡ Invoke FM for Text, Image & Code generation via API
- ⚡ Build an AI Solution with Bedrock Converse API
- ⚡ Exploring Transformers, Tokenization & GPT-2
- ⚡ Watermark Detection with Amazon Bedrock
- ⚡ 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
- ⚡ Mitigating Image Bias with Effective Prompts
M04Retrieval-Augmented Generation (RAG)
- Anatomy of a Prompt
- Core Prompting Techniques
- Advanced Prompting Techniques
- The RAG Lifecycle
- RAG Concepts & Architecture
- Vector Embeddings & Search
- AWS RAG Architecture
- Vector Stores & RAG Lifecycle
- ⚡ Setup Guardrails with Amazon Bedrock
- ⚡ Building a RAG using @Console
- ⚡ Text & Vector Embeddings Using Bedrock FM API
- ⚡ Build a QA-Based RAG App using Bedrock API
- ⚡ Building an End-to-End RAG System with API
M05Agentic AI and Workflow Orchestration
- Introduction to Agentic AI
- Workflow Orchestration in AI Systems
- Building a QA-Based RAG App Using Bedrock API
- Compound AI Systems
- Core Agent Architecture
- Agent AI vs Multi-Agent Systems
- Agent Architecture Summary
- ⚡ 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
- Data Preparation & Feature Engineering
- ⚡ Design Scalable Pipelines: Glue & Step Functions
M07FMs Customization and Deployment
- FM Customization & Deployment
- Fine-Tuning & Continued Pre-Training
- Bedrock Guardrails & Content Safety
- Security, PII Protection & Compliance
- Governance Frameworks for Generative AI
- ⚡ Create a Bedrock Custom Model with Fine-tuning
- ⚡ Bedrock Evaluation, Monitoring & Cost Optimization
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
- Automated Evaluation of Foundation Models
- RAISE Framework & Model Evaluation
- Automatic Evaluation Methods
- LLM as Judge & Human Evaluation
M10AWS AI Managed Services
- Amazon Lex
- Amazon Personalize
- Amazon Polly
- Amazon Rekognition
- Amazon Forecast & Enterprise ML
- ⚡ Amazon Polly and Rekognition
- ⚡ Smart AI-Powered Search & Recommendation System
M11Exam Preparation
- Full exam prep, domain review, practice questions & mock tests
Open to All
Track 3 — 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 – (MLA-C01)
11 Modules Covered
M01AWS Data Ingestion
- Object Storage Options
- Simple Storage Service (S3)
- S3 Bucket Policy
- Amazon S3 ACL
- S3 Object Versioning
- Cross Region Replication
- Amazon Athena
- AWS Glue
- AWS Lake Formation
- Amazon Kinesis for ML
- ⚡ Create S3 Bucket, Upload and Access Files
- ⚡ Introduction to AWS Glue
- ⚡ AWS Glue & Athena: Analyze CSV Data in S3
- ⚡ Running an ETL Job using Glue
- ⚡ Data Pipeline using Kinesis, Spark, and S3
M02Amazon EBS and Kinesis Data Streams
- EBS Snapshots
- Amazon Elastic File System
- Amazon FSx
- AWS Kinesis
- ⚡ Amazon Kinesis Data Streams – Hands-On
M03Data Transformation & Integrity
- Data Transformation
- AWS Glue
- Feature Engineering
- SageMaker Data Wrangler
- Data Quality & Validation
- SageMaker Feature Store
- ⚡ Building Data Pipelines with No-Code ETL
- ⚡ Preparing Data for TF-IDF
- ⚡ Data Preprocessing with DataBrew
- ⚡ Data Prep and Automated Model Training
- ⚡ Streamlining Data Analysis with SageMaker
M04Amazon SageMaker and Built-In Algorithms
- Introduction to SageMaker
- Setting up Studio
- SageMaker Features and Capabilities
- SageMaker and Built-In Algorithms
- ⚡ Setting Up a Jupyter Notebook
- ⚡ Create & Manage SageMaker Studio
- ⚡ Build a sample chatbot using Amazon Lex
- ⚡ Build a Lex Chatbot with Lambda & Third-Party API
- ⚡ Build, Train, Deploy a Model Using No-Code
M05Model Training, Tuning, and Evaluation
- Model Training
- Deep Dive into Model Training
- Distributed Training
- Hyperparameters for AWS
- Tuning Jobs in AWS
- Model Evaluation Metrics
- LLM as Judge & Human Evaluation
- ⚡ Hyperparameter Optimization using SageMaker
- ⚡ Tune, Deploy, and Predict with TensorFlow
- ⚡ Build, Train & Deploy ML Model @SageMaker AI
M06Generative AI Model Fundamentals
- Transformers
- Transformers Advantages and Limitations
- Tokenization
- Embeddings & Vector Search
- Fine-tuning vs RAG vs Training
- Foundation Models
- ⚡ Exploring Transformers, Tokenization & GPT-2
M07Developing Generative AI Applications
- Introduction to Amazon Bedrock
- Accessing Bedrock using Console
- Prompt Engineering
- RAG Concepts & Architecture
- Agent Architecture
- Guardrails
- ⚡ Invoke FMs to generate Text & Image @Console
- ⚡ Invoke FM for Text Generation using API
- ⚡ Mitigating Image Bias with Effective Prompts
- ⚡ Setup Guardrails with Amazon Bedrock
- ⚡ Building a RAG using @Console
- ⚡ Build a Bedrock Agent with Action Groups
- ⚡ Protect Transcriptions via Amazon Transcribe
- ⚡ Perform Sentiment Analysis with Amazon Comprehend
M08MLOps
- MLOps on AWS
- AWS Containers & Docker
- Amazon EKS
- CloudFormation
- SageMaker Model Registry & MLflow
- ⚡ Install Docker, Create Image & Push Image
M09Security, Identity, and Compliance
- Identity and Access Management (IAM)
- IAM Best Practices
- AWS KMS
- AWS Secrets Manager
- ⚡ Working with AWS IAM
- ⚡ Enable Multi-Factor Authentication
M10Management and Governance
- What is Monitoring Services
- AWS X-Ray
- CloudWatch Logs
- AWS CloudTrail
- AWS Config
- Data Drift & Model Drift
- Cost Optimization for ML
- ⚡ Get Started with AWS X-Ray
- ⚡ Enable CloudTrail and Store Logs in S3
- ⚡ Setting Up AWS Config
M11Machine Learning Best Practices
- Machine Learning
- AWS Machine Learning Lifecycle
- Responsible AI for ML
- Adversarial Machine Learning
Bonus Module
AI Agent Security: 8 Pillars That Get You Past the Security Team.
The #1 technical question from 500+ enterprise practitioners at our live sessions. Eight pillars that separate a demo agent from one your security team will actually approve and deploy in production.
⏰ Free Bonus — Enroll by July 3, 2026 to unlock this module at no extra cost
1
Prompt Injection Protection
Direct attacks + indirect XPIA (cross-prompt injection)
- Direct prompt-injection attacks and how to detect them
- Indirect / cross-prompt injection (XPIA) via retrieved content
- Input sanitization and instruction-defense patterns
- Testing agents against known injection payloads
2
IAM & Least Privilege
Scoped IAM policies per agent, not per user
- Scoping IAM policies to each agent's exact needs
- Separating agent roles from user roles
- Deny-by-default and permission boundaries
- Tightening over-permissive policies
3
IAM Roles, No Static Keys
No hardcoded credentials — roles + Secrets Manager
- Using IAM roles instead of hardcoded access keys
- AWS Secrets Manager for API keys and secrets
- Short-lived credentials and rotation
- Removing long-lived keys from code and config
4
Guardrails + Content Filtering
Amazon Bedrock Guardrails on inputs and outputs
- Amazon Bedrock Guardrails on inputs and outputs
- Blocking unsafe topics, profanity and PII
- Grounding responses to reduce hallucination
- Validating both what goes in and what comes out
5
Network Security
VPC isolation, PrivateLink, no public exposure
- Private endpoints and VPC isolation for agents
- AWS PrivateLink to keep traffic off the public internet
- Security groups and least-exposure networking
- No public endpoints for sensitive workloads
6
Audit Logging + Monitoring
Trace every agent action with CloudTrail + CloudWatch
- Tracing every agent action with AWS CloudTrail
- CloudWatch logs, metrics and alarms
- Capturing prompts, tool calls and outputs
- Building an audit trail for compliance
7
Human-in-the-Loop Approval Gates
Pause before high-impact, irreversible actions
- Pausing before high-impact or irreversible actions
- Approval workflows for sensitive operations
- Confidence thresholds that trigger human review
- Safe defaults when the agent is unsure
8
Red-Teaming & Adversarial Testing
Attack your agent before attackers do
- Attacking your own agent before attackers do
- Building an adversarial test suite
- Jailbreak and injection stress tests
- Fixing findings and re-testing
Enroll before July 3, 2026 — Bonus Module Included. Free for all enrollments before July 3.
Enroll now →
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
From our learners
Real people. Real roles. Real results.
Verified outcomes from K21 Academy learners across the globe.
"
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.
S
Steve
Generative AI Engineer
✓ Google review verified
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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.
D
Debasish Dash
AI Engineer → Generative AI Engineer
✓ Google review verified
"
I've secured a new cloud architect position. Credits the structured training and support system for making the transition possible.
S
Stephen Agbor
Cloud Solutions Architect
✓ Trustpilot 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.
I
Ike Imala
Generative AI Engineer
✓ Google review verified
"
I got that role. The AI/ML trainee landed the job after completing the program — the SageMaker projects made all the difference in the technical interview.
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Semanti
AI/ML Engineer
✓ Google review verified
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Excited to share that I have successfully passed the AWS AI Practitioner (AIF-C01) certification! Credits K21 Academy guidance, and is moving on to the next AWS AI/ML certification.
A
Adnan Ahmed
AI/ML Engineer
✓ Trustpilot verified
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 your specialization cert — Solutions Architect (SAA-C03), Generative AI Developer, or Machine Learning (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.
AI Agent Security Module — 8 Production Pillars
How do you actually secure a production AI agent on AWS? Eight pillars: prompt injection defense, IAM & least privilege, IAM roles (no static keys), Bedrock Guardrails, VPC isolation, CloudTrail/CloudWatch audit logging, HITL gates, and red-teaming. Free for enrollments before July 3, 2026.
Choose your path
Three levels. Pick how far and
Three levels. Pick how far and
how fast you want to go.
🔒
AI Agent Security Bonus Module included free — enroll by July 3, 2026. All three tiers qualify. Enroll before July 3 and the 8-pillar security module is added to your program at no extra cost.
Upskill
$1,997
Get certified
- ✓ Weekly LIVE sessions
- ✓ 100+ hands-on AWS AI/ML labs
- ✓ 1 capstone AWS AI/ML project
- ✓ 500+ practice questions
- ✓ Cert prep: AWS CLF-C02, AIF-C01, MLA-C01 + specialization
- ✓ Community access
- 🔒 AI Agent Security Bonus — 8 pillars (free until July 3)
Most Popular
Job Prep
$5,997
The full job system
- ✓ Everything in Upskill
- ✓ 14 real-world AWS AI/ML projects on GitHub
- ✓ Resume + LinkedIn rebuild for AWS AI/ML roles
- ✓ Mock technical interviews + 1-year on-job support
- ✓ EKS Bonus Webinar included
- ✓ Installments available
- 🔒 AI Agent Security Bonus — 8 pillars (free until July 3)
Mastermind
$9,997
Application only
- ✓ Everything in Job Prep
- ✓ Weekly 1:1 mentoring with AWS AI/ML experts
- ✓ Priority interview access
- ✓ Guaranteed interview calls, or we keep working
- 🔒 AI Agent Security Bonus — 8 pillars (free until July 3)
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.
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.
Can I pay in installments?
Yes — installment plans are available for the Job Prep tier ($1,297 x 4 payments) and Upskill tier ($597 x 4 payments). Switch to the "Installments" tab above to see the split payment options.
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.
What is the AI Agent Security Bonus Module?
An 8-pillar module covering how to actually secure a production AI agent on AWS: prompt injection defense, IAM & least privilege, IAM roles with no static keys, Amazon Bedrock Guardrails, VPC isolation and PrivateLink, CloudTrail/CloudWatch audit logging, human-in-the-loop gates, and red-teaming. Free for all enrollments before July 3, 2026.
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 →
