AWS for Python Developers: Serverless, Containers & Bedrock
Lambda, Step Functions, Fargate, MediaConvert, Amazon Bedrock and Bedrock AgentCore — build serverless, event-driven and AI systems on AWS with Python.
Is this for you?
Join if…
- You’re a Python developer who wants to build and deploy on AWS
- Your company runs on AWS and you want to own serverless, container or AI workloads
- You want hands-on experience with Amazon Bedrock and AI agents on AWS
Pick another programme if…
- You’re new to Python — start with the Launchpad or the Backend programme
By the end, you’ll be able to
Curriculum
Every module, every topic and every lab — nothing hidden.
01AWS foundations5 topics · hands-on labWeek 1
Set up AWS safely and learn the services everything else builds on.
- Regions, Availability Zones and the shared-responsibility model
- IAM users, roles, policies and least privilege
- AWS CLI and boto3 for Python
- Budgets, billing alarms and the Free Tier
- Amazon S3: buckets, storage classes, presigned URLs, lifecycle rules and events
02Serverless with AWS Lambda7 topics · hands-on labWeek 2
Run Python without managing servers.
- Handlers, events and the context object
- Runtimes, layers and container-image functions
- Environment variables and AWS Secrets Manager
- Cold starts, concurrency and provisioned concurrency
- AWS Lambda Powertools for Python: logging, tracing, metrics, idempotency
- API Gateway (REST and HTTP APIs) and Lambda Function URLs
- Deploying with AWS SAM
03Event-driven architecture5 topics · hands-on labWeek 3
Connect services that react to events instead of polling.
- Amazon SQS: standard and FIFO queues, dead-letter queues
- Amazon SNS fan-out
- Amazon EventBridge rules and EventBridge Scheduler
- S3 and DynamoDB stream triggers
- Idempotency, retries and back-pressure
04Workflows with AWS Step Functions6 topics · hands-on labWeek 4
Orchestrate long, multi-step processes reliably.
- States: Task, Choice, Parallel, Map and Wait
- Standard vs Express workflows
- Retries, catch and error handling
- Direct service integrations: Lambda, ECS, SNS and Amazon Bedrock
- Human approval with task tokens
- Distributed Map for large-scale processing
05Containers on Amazon ECS Fargate6 topics · hands-on labWeek 5
Run containers without managing servers.
- Docker images and Amazon ECR
- ECS clusters, task definitions and services
- Fargate vs EC2 launch types
- Application Load Balancer and service autoscaling
- Scheduled and event-driven Fargate workers
- Logs and metrics in CloudWatch
06Data on AWS5 topics · hands-on labWeek 6
Pick and design the right data store.
- Amazon DynamoDB data modelling: keys, GSIs and single-table design
- Amazon RDS and Aurora PostgreSQL
- Amazon ElastiCache for Redis
- Choosing between stores
- Backups and point-in-time recovery
07Media pipelines6 topics · hands-on labWeek 7
Process and deliver video at scale.
- AWS Elemental MediaConvert jobs, presets and job templates
- Adaptive streaming (HLS/DASH), thumbnails and captions
- Automating S3 → EventBridge → MediaConvert
- Amazon CloudFront delivery and signed URLs
- Amazon Transcribe for automatic captions
- Amazon Rekognition for content moderation
08Generative AI with Amazon Bedrock8 topics · hands-on labAIWeeks 8–9
Build AI applications with managed foundation models.
- Amazon Bedrock models and model access (Anthropic Claude, Meta Llama, Amazon Nova and more)
- The Converse API and streaming
- Embeddings on Bedrock
- Knowledge Bases for Amazon Bedrock (managed RAG) with OpenSearch Serverless or Aurora pgvector
- Guardrails for Amazon Bedrock
- Amazon Bedrock Agents: action groups backed by Lambda
- Document AI with Amazon Textract and Amazon Comprehend
- Prompt management and cost control
09Amazon Bedrock AgentCore6 topics · hands-on labAIWeek 10
Deploy and operate AI agents securely at scale.
- AgentCore Runtime: deploying LangGraph and Strands agents
- AgentCore Memory: short-term and long-term memory
- AgentCore Gateway: turning APIs and Lambda functions into MCP tools
- AgentCore Identity: OAuth and secure access for agents
- Code Interpreter and Browser tools
- AgentCore Observability
10Security, observability & infrastructure as code7 topics · hands-on labWeek 11
Run AWS workloads like a professional team.
- Amazon Cognito user pools and authentication
- AWS KMS and Secrets Manager
- CloudWatch logs, metrics, alarms and dashboards; AWS X-Ray tracing
- VPC basics, subnets and security groups
- AWS CDK in Python and AWS SAM
- CI/CD with GitHub Actions and OIDC
- Well-Architected reviews and cost optimisation
11Capstone3 topics · hands-on labWeek 12
An end-to-end AWS system with an AI component.
- Architecture design and review with a team lead
- Build, deploy and monitor
- AWS interview and certification-path guidance
What you’ll build
Tools & technologies
Who mentors you
Working senior Python developers and team leads — people who build and ship AI and backend systems for clients every week. They run the live sessions, review your code on GitHub and conduct your mock interviews. You learn the way their own teams work.
Your certificate
EZB-AIE-2026-0001
Issued 2026
Certificate of Completion
This is to certify that
has successfully completed the mentor-led programme
Ezibell Tech
Issued by Ezibell Tech, an MSME (Udyam) and GST registered company in India, with a unique ID listed on our public verification page.
Questions learners ask
Still unsure? Ask the team on WhatsApp or call +91 93981 39619.
Will this cost me money on AWS?
Most labs fit within the AWS Free Tier. In week 1 we set up budgets and billing alarms, and every lab ends with a clean-up step. Some services, such as Bedrock and MediaConvert, charge per use — we keep those labs small and show you the cost before you run them.
Do I need a credit card for AWS?
AWS requires a card to open an account. If that’s a problem, talk to us and we’ll suggest options.
Does this prepare me for AWS certification?
It covers much of what the AWS Certified Developer – Associate exam tests, and we share a study path. The certification exam itself is taken separately with AWS.
What is Bedrock AgentCore?
Amazon Bedrock AgentCore is a set of AWS services for running AI agents in production — a runtime, memory, a gateway that exposes your tools via MCP, identity, and observability. You’ll deploy a real agent on it.
Is the AWS console or code used?
Both. You’ll learn in the console, then build everything with Python (boto3) and infrastructure as code (CDK and SAM), which is how teams work.
What laptop or setup do I need?
A laptop with 8 GB RAM and your own AWS account. Most labs fit within the AWS Free Tier; we set up budgets and billing alarms in week 1 so costs stay under control.
Are classes live or recorded?
Live and interactive, with mentors answering your questions in the session. Every session is also recorded so you can revise or catch up.
When are the classes?
Live online · weekends, built for working professionals. Exact timings are shared before each batch starts, and we plan them around working hours and college schedules.
What if I miss a class?
Watch the recording, finish the lab, and bring questions to the next doubt session or the mentor group. Falling behind is normal — just tell your mentor early.
Which language are classes taught in?
Simple, clear English. If you’d like help in Hindi during doubt sessions, ask us when you enrol.
How big is a batch?
We keep batches small so mentors can review everyone’s code. Ask us for the current batch size.
How do I get my doubts solved?
Ask in the live session, post in the private mentor group, or bring it to the weekly doubt-clearing session. Mentors also comment directly on your GitHub pull requests.
How do I earn the certificate?
Complete the hands-on labs and the capstone or final project. Your mentor reviews and signs it off, and your certificate is issued with a unique ID listed on our public verification page.
Is the certificate recognised?
It’s issued by Ezibell Tech, a Government of India–registered company (MSME/Udyam and GST registered), and carries a unique ID that anyone can verify online. It’s a professional training certificate — not a university degree or a government accreditation — and its real value comes from the projects you build and the mentors who can vouch for your work.
Do I keep access to the recordings and materials?
Yes, recordings and materials stay available after the programme ends. Ask us for the exact access period for your batch.
Do I own the projects I build?
Yes. Everything lives in your own GitHub account and is yours to show employers.
What are the fees? Is EMI available?
Fees depend on the programme and batch. Message us on WhatsApp or call, and we’ll share the current fee, instalment options and any student or early-bird discount within a few hours.
What is the refund and batch-transfer policy?
Refund and batch-transfer terms are shared in writing before you pay, so you know exactly where you stand.
Do you guarantee a job or placement?
No — and be careful of anyone who does. We give you real skills, a portfolio, interview practice and introductions within our network.
Can I talk to a mentor before enrolling?
Yes. Book a free counselling call on WhatsApp or phone. We’ll look at your background and recommend the right programme — honestly, even if it’s a different one.