====== 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.

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

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==== 📅 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 |

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==== 📅 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 |

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==== 📅 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 |

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==== 📅 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 |

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==== 📅 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 |

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==== ✅ Suggested Resources ====

  * [[https://aws.amazon.com/training/learning-paths/machine-learning/|AWS ML Specialty Learning Path]]
  * [[https://github.com/aws/amazon-sagemaker-examples|AWS Samples on GitHub]]
  * [[https://aws.amazon.com/bedrock/|Amazon Bedrock Labs]]
  * [[https://docs.aws.amazon.com/wellarchitected/latest/machine-learning-lens/|AWS Well-Architected ML Lens]]

[[wiki:ai:home-page|AI Cloud Managed Services Policies and Procedures]]