AWS Certified Solutions Architect - Associate (SAA-C03)
50-hour program → 10+ hands-on labs → Exam practice questions →
Certification ready Syllabus
Technical Outcomes
- Architecting global VPCs with multi-tier security
- Implementing HA/DR with Multi-AZ & Cross-region
- Data migration & hybrid cloud connectivity
- Serverless & Event-driven design patterns
Ideal For
-
SysAdmins: Moving to cloud engineering
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Developers: Deploying cloud-native apps
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IT Leads: Driving infrastructure strategy
Career Goals
- AWS Solutions Architect Certification
- Cloud Architecture roles
- Enterprise cloud consulting
Comprehensive Syllabus (50 Hours)
VPC Networking (Subnets, IGW, NAT), IAM Roles, Policies, and
MFA security best practices.
EC2 Instance types, Auto Scaling Groups (ASG), and
Application/Network Load Balancers (ALB/NLB).
S3 Lifecycles, Glacier, EBS, EFS. Mastery of RDS (Aurora),
DynamoDB, and ElastiCache.
Route 53 DNS, CloudFront CDN, AWS Lambda, SQS/SNS decoupling,
and CloudWatch monitoring.
AWS Certified AI Practitioner (AIF-C01)
40-hour program → Theory & Demos → AWS practice exam questions →
Certification Ready
Technical Outcomes
- Mapping business needs to AWS AI services
- Building predictive models with SageMaker Canvas
- Applying NLP and Computer Vision via APIs
- Ensuring AI Ethics, Security, and Compliance
Ideal For
-
Product Managers: Leading AI integration
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Data Analysts: transitioning to Cloud ML
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Business Leads: Evaluating AI ROI on AWS
Career Goals
- AWS AI Practitioner Certification
- AI Product Management roles
- AI Business Strategy consulting
Comprehensive Syllabus (40 Hours)
Machine Learning terminology, lifecycle, and the AWS AI
service hierarchy (Bedrock to SageMaker).
Rekognition for vision, Polly/Lex for audio, and
Textract/Comprehend for document intelligence.
Working with AWS Bedrock foundation models, prompt engineering
basics, and playground testing.
Forecasting with SageMaker Canvas. Implementing AI Governance,
bias detection, and data privacy.
Master Generative AI: From LLM Fundamentals to Production-Grade Applications
40-hour program → from 100+ production projects → Capstone → Industry
Ready
Technical Outcomes
- Implementing RAG with Vector DBs (Pinecone/Weaviate)
- Fine-tuning LLMs with PEFT/LoRA on custom datasets
- Orchestrating multi-agent systems (LangChain/CrewAI)
- Optimizing LLM token costs and latency in production
Ideal For
-
Sr. Developers: Building GenAI features
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Tech Leads: Designing AI system
architectures
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Software Architects: Integrating LLMs into
SDLC
Career Goals
- Production GenAI Engineer
- Generative AI Engineer
- LLM Application Developer
- LLM GenAI Architec
- Agentic AI Engineer
Comprehensive Syllabus (40 Hours)
Attention mechanism, Transformers, Tokenization, and the
science of RLHF (Human Feedback).
CoT, ReAct, and Self-Consistency. Building RAG pipelines with
Embeddings and Vector Search.
Instruction tuning, PEFT (LoRA/QLoRA), and deploying
open-source models (Llama/Mistral).
Building autonomous agents with tool-calling. Deploying GenAI
apps with monitoring and guardrails.