Operationalizing AI on AWS: A Roadmap for SREs and Platform Engineers

Phase 0: Prerequisites (You may already have most of this)

Goal: Be fluent enough in AWS + software fundamentals so AI becomes additive, not confusing.

Core Skills

• Linux, networking, TCP/IP, DNS

• Python (must-have for AI & automation)

• Git, CI/CD

• APIs & JSON

AWS Fundamentals

• IAM (roles, policies, least privilege)

• VPC (subnets, routing, security groups)

• EC2, Auto Scaling

• S3 (storage classes, lifecycle)

• RDS & DynamoDB

• CloudWatch, CloudTrail

✅ Outcome: You can deploy and operate AWS workloads confidently.

Phase 1: AI Fundamentals (Conceptual, Cloud-agnostic)

Goal: Understand what AI/ML actually is before touching AWS AI services.

Learn These Concepts

• AI vs ML vs Deep Learning

• Supervised vs Unsupervised vs Reinforcement Learning

• Model training vs inference

• Overfitting, bias, drift

• LLMs (tokens, embeddings, context windows)

Hands-on (Local)

• Python + Jupyter

• NumPy, Pandas

• scikit-learn

• Basic NLP with Hugging Face

🎯 Outcome: You understand how models work, not just how to call APIs.

Phase 2: AWS AI/ML Landscape (Big Picture)

Goal: Know which AWS service does what.

AWS AI Stack (Mental Model)

AI Applications │ ├─ Amazon Bedrock (LLMs, GenAI) │ ├─ SageMaker (Build/Train/Deploy models) │ ├─ Prebuilt AI APIs │ ├─ Rekognition (vision) │ ├─ Comprehend (NLP) │ ├─ Textract (OCR) │ └─ Transcribe / Polly │ └─ Data Layer ├─ S3 ├─ Glue ├─ Athena └─ Redshift

🎯 Outcome: You can choose the right tool instead of defaulting to SageMaker for everything.

Phase 3: Generative AI on AWS (High-Impact)

Goal: Leverage LLMs without training models.

Amazon Bedrock (Critical)

Learn:

• Foundation models (Claude, Titan, Llama)

• Prompt engineering

• Temperature, max tokens

• Embeddings & vector search

• Guardrails & content filters

Hands-on Projects:

• AI chatbot for internal ops

• Log analysis using LLMs

• Incident RCA summarization

• Documentation generator

Tools:

• Bedrock + Lambda

• Bedrock + API Gateway

• Bedrock + OpenSearch (vector DB)

🎯 Outcome: You can build GenAI apps without managing models.

Phase 4: Data + AI Pipelines (Where SREs Shine)

Goal: Feed AI with reliable, scalable data.

Data Engineering on AWS

• S3 as data lake

• Glue ETL jobs

• Athena queries

• Kinesis (streaming logs)

• OpenSearch (search + vectors)

AI Use Cases

• Anomaly detection on metrics

• Log clustering & root cause detection

• Predictive scaling

• Cost anomaly detection

🎯 Outcome: AI becomes part of observability & reliability.

Phase 5: MLOps & Production AI (Advanced)

Goal: Operate AI systems like production services.

MLOps Concepts

• Model versioning

• CI/CD for models

• Feature stores

• Model monitoring

• Drift detection

• Rollbacks

AWS Tools

• SageMaker Pipelines

• SageMaker Model Registry

• CloudWatch + custom metrics

• Canary deployments for models

🎯 Outcome: You can run AI safely at scale.

Phase 6: AI for SRE & Platform Engineering (High Leverage)

Goal: Use AI to reduce toil and incidents.

Practical Use Case

• AI-assisted incident response

• LLM-based runbook execution

• Automated postmortems

• ChatOps bots (Slack + Bedrock)

• Capacity forecasting

• Security threat summarization

Architecture Example

CloudWatch Logs → Lambda → Bedrock → OpenSearch ↓ Slack / PagerDuty

🎯 Outcome: AI directly improves uptime & efficiency.

Phase 7: Security, Cost, and Governance

Goal: Prevent AI from becoming a liability.

Learn

• IAM for AI services

• Data privacy & PII handling

• Prompt injection risks

• Model access control

• Cost controls (token usage!)

Tools

• AWS GuardDuty

• Bedrock Guardrails

• Budget alerts

• VPC endpoints for AI services

🎯 Outcome: Secure, compliant AI systems.

Suggested Learning Order (12–16 Weeks)

Week

Focus

1–2

AWS core + Python

3–4

AI/ML fundamentals

5–6

Bedrock + GenAI

7–8

Data pipelines

9–10

Observability AI

11–12

MLOps

13–16

Real-world projects

Certifications (Optional but Helpful)

• AWS Certified Solutions Architect

• AWS Certified Machine Learning – Specialty

• AWS Certified AI Practitioner

Final Advice (Important)

Don’t start with SageMaker. Start with Bedrock.

90% of business AI value comes from using models, not training them.

If you want, I can:

• Create a personalized roadmap for SREs

• Propose real AWS AI project ideas

• Design a reference architecture

• Suggest hands-on labs & resources