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30 Day Plan to Deploy AI in AWS

Here's a 30-day (5 weeks, 5 days/week, 8 hours/day) hands-on training plan for deploying AI in AWS, focused on foundational knowledge, hands-on labs, deployment, and monitoring using key AWS AI/ML services.

🧠 Goal: Learn to develop, deploy, and manage AI/ML models using AWS tools such as SageMaker, Lambda, Step Functions, and associated data services.

📅 WEEK 1: AWS AI/ML Fundamentals & Setup (40 hrs)

Focus: AWS ecosystem, IAM, AI services overview

Day Topics Activities
1 AWS account setup & IAM Create AWS account, users, roles, permissions. Enable billing alerts.
2 Intro to AWS AI/ML tools Overview of SageMaker, Rekognition, Comprehend, Lex, Polly
3 S3 & Data Preprocessing Upload datasets, use S3 lifecycle, permissions, and Glue basics
4 Jupyter in SageMaker Launch notebook instances, experiment with basic Python/ML
5 Use Amazon Rekognition & Polly Analyze images and convert text to speech

📅 WEEK 2: ML Workflows & Model Training in SageMaker (40 hrs)

Focus: Training, tuning, evaluating ML models

Day Topics Activities
6 SageMaker built-in algorithms Use linear learner, XGBoost for regression/classification
7 Model training jobs Launch training jobs, use spot training to save cost
8 Hyperparameter tuning Use SageMaker HPO jobs, define search ranges, metrics
9 Model evaluation Evaluate precision, recall, confusion matrix
10 Bring your own model Train custom sklearn/Keras model and import to SageMaker

📅 WEEK 3: Model Deployment & Monitoring (40 hrs)

Focus: Deployment, endpoints, A/B testing, monitoring

Day Topics Activities
11 Real-time endpoint deployment Deploy trained model to SageMaker endpoint
12 Batch inference Perform batch predictions with Batch Transform
13 A/B testing & blue/green Use production variants for multiple model versions
14 CloudWatch & SageMaker Model Monitor Set up monitoring for data drift, alerts
15 Cost tracking Review and optimize training/deployment cost reports

📅 WEEK 4: MLOps, Automation, & Lambda Integration (40 hrs)

Focus: Automation, pipelines, serverless AI

Day Topics Activities
16 SageMaker Pipelines Build an end-to-end training + deployment pipeline
17 Step Functions for ML workflows Orchestrate multi-step ML workflows
18 Integrate with Lambda & API Gateway Create serverless API for model inference
19 CI/CD with CodePipeline & SageMaker Automate retraining and deployment pipeline
20 Local mode testing Use local SageMaker mode for dev/test on smaller models

📅 WEEK 5: GenAI, Use Cases & Capstone Project (40 hrs)

Focus: Generative AI, use cases, final project

Day Topics Activities
21 Amazon Bedrock overview Try Titan, Claude, and Jurassic models with prompts
22 Fine-tuning & custom GenAI Fine-tune foundation models (if eligible)
23 Common AI use cases Implement AI chatbot, fraud detection, or image tagging
24 Capstone project: build & deploy Plan and develop an end-to-end ML solution
25 Review, demo, and optimize Final testing, monitoring setup, and documentation

✅ Suggested Resources

AI Cloud Managed Services Policies and Procedures