🚀 Recorded AI Bootcamp for DevOps and Cloud Professionals

🎯 Bootcamp Objective

Equip DevOps Cloud engineers with practical knowledge and hands-on experience in integrating AI/GenAI services into cloud-native workflows using AWS (Bedrock, SageMaker) and automation tools.

✅ Day 1: Foundations of AI, ML, and GenAI

Goal: Establish a clear understanding of core AI concepts and set the context for modern GenAI use cases.

📘 Topics Covered:

1. What is AI, ML, and GenAI?
2. Traditional AI vs Generative AI
3. Examples: ChatGPT, DALL·E, Claude
4. What is an LLM (Large Language Model)?
5. How LLMs work: training vs inference
6. Prompt & Completion structure
7. Hosting LLMs: Why it’s hard (cost, infra, compliance)

🛠 Outcome:

Participants understand the value and complexity of GenAI and LLMs and their role in cloud infrastructure.

✅ Day 2: Deep Dive into AWS Bedrock for GenAI

Goal: Explore how to deploy and consume LLMs using AWS Bedrock without managing infrastructure.

📘 Topics Covered:

1. What is Amazon Bedrock?
2. Access to models: Claude, Titan, Jurassic, Llama
3. No infra, no training, fully managed
4. Bedrock architecture (diagram)
5. IAM → API → Model → Output
6. Use Cases: Chatbots, summarization, code generation

👨‍💻 Hands-On Lab:

Project 1 – Bedrock Serverless Chatbot (Architecture + Bedrock Setup)

✅ Day 3: Building GenAI Apps with Lambda & API Gateway

Goal: Construct a real serverless GenAI chatbot using AWS services and best practices.

📘 Topics Covered:

1. Demo: Bedrock chatbot architecture
2. Serverless backend (Lambda + API Gateway)
3. Securing access with IAM roles
4. CloudWatch Logs & observability
5. DevOps relevance: Quotas, monitoring, security

👨‍💻 Hands-On Lab:

Project 2 – Complete and test chatbot in Postman/CURL, monitor logs, troubleshoot

✅ Day 4: MLOps Foundations & Amazon SageMaker

Goal: Introduce DevOps engineers to the ML model lifecycle and operationalizing it using SageMaker.

📘 Topics Covered:

1. What is Machine Learning?
2. Supervised, Unsupervised, Reinforcement Learning
3. ML Lifecycle: Train → Evaluate → Deploy → Monitor
4. MLOps 101: What & Why
5. DevOps-style practices for ML
6. Responsibilities: infra, monitoring, CI/CD
7. What is Amazon SageMaker?
8. Components: Notebook, Training Jobs, Model Registry, Endpoint

👨‍💻 Hands-On Lab:

Project 3 – Launch a basic training job in SageMaker using preloaded scripts

✅ Day 5: CI/CD for Machine Learning with GitHub Actions

Goal: Automate ML workflows using GitHub Actions and AWS services.

📘 Topics Covered:

1. CI/CD pipeline structure for ML
2. GitHub Actions for retraining, deployment
3. IAM setup for SageMaker and S3
4. Logging, rollback, endpoint testing

👨‍💻 Hands-On Lab:

Project 4 – Complete GitHub Actions pipeline to retrain and redeploy SageMaker model

✅ Day 6: Event-Driven AI Workflows with Bedrock

Goal: Use event-based serverless automation for real-world GenAI use cases.

📘 Topics Covered:

1. Why use EventBridge, Lambda, S3, SNS for AI
2. Workflow: S3 → EventBridge → Lambda → Bedrock → SNS
3. Full Architecture Diagram
4. DevOps role in event-driven AI: Infra as Code, monitoring, error handling

👨‍💻 Hands-On Lab:

Project 5 – AI-powered Document Summarizer

1. Upload to S3 → Trigger → Claude → Store output → SNS notify
2. Add error handling, CloudWatch alarms

🧰 Training Resources Provided

1. ✅ Architecture diagrams (PDF/PowerPoint)
2. ✅ Lambda & GitHub Actions templates
3. ✅ IAM policies for each scenario
4. ✅ CloudFormation / Terraform templates (optional)
5. ✅ Demo checklists + troubleshooting FAQs

📲 To Enrol use Topmate to pay and register to live program.

Sanjeev Kumar

📱 WhatsApp: +91-9888055959

Other Payment Options in India:

UPI to direct2sanjeev@icici

or pay UPI to 9888055959