AI & Machine Learning Roadmap: From Basics to Responsible AI

Build It. Train It. Secure It.

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From AI basics to building and securing real-world AI systems. Learn machine learning, generative AI, foundation models, responsible AI, and security — step by step.
Phase 1 Fundamentals of AI and ML No experience needed ▶
Concepts & Terminologies of AI
Introduction to Artificial Intelligence Machine learning (ML) Deep learning Generative AI overview
Practical Use Cases of AI
AI for business efficiency Limitations of AI Types of ML problems AWS AI services
ML Development Lifecycle
Machine learning pipeline Amazon SageMaker services
🎯 Goal: Understand what AI and ML are, how they work, and where they are used in the real world
Phase 2 Fundamentals of Generative AI How Gen AI actually works ▶
Concepts of Generative AI
Overview of Gen AI Core components of Gen AI Transformer architecture In-context learning Multimodal & diffusion models Gen AI use cases Gen AI architectures Key techniques & challenges
Capabilities & Limitations for Solving Business Problems
Capabilities of Gen AI Limitations of Gen AI
AWS for Building Gen AI
Advantages of AWS Gen AI services AWS infra for Gen AI applications Pricing models for LLMs AWS services for Gen AI apps
🎯 Goal: Understand how generative AI systems work and how to build them on AWS
Phase 3 Applications of Foundation Models Build with pre-trained models ▶
Design Considerations for Applications
Selection criteria for pre-trained models Biases in training data Availability & compatibility Customisation & explainability Inference parameters Vector databases & retrieval Retrieval-Augmented Generation (RAG)
Effective Prompt Engineering Techniques
Introduction to prompts Prompting techniques Best practices & risk of prompt engineering
Training & Fine-Tuning Foundation Models
Key elements of training a foundation model Fine-tuning techniques Steps in data preparation for fine-tuning AWS data preparation tools
Evaluating Foundation Model Performance
Deployment considerations Evaluation metrics Human evaluation Integration into applications
🎯 Goal: Use, fine-tune, and evaluate foundation models to build real AI applications
Phase 4 Responsible AI Build fair & ethical AI ▶
Development of Responsible AI Systems
Intro to responsible AI Addressing bias in AI models Ethical dataset characteristics Ethical model selection AWS services for monitoring & bias detection
Transparency & Explainability
Recognising the importance of transparent & explainable models Transparency & explainability concepts Tools & techniques for transparency
🎯 Goal: Build AI systems that are fair, transparent, explainable, and ethically sound
Phase 5 Security, Compliance & Governance Protect and govern AI systems ▶
Secure AI Systems
Shared responsibility model Identity & access management (IAM) Identity federation Logging & monitoring S3 block public access SageMaker role manager Data encryption Security configuration & network management AI model security
Governance & Compliance Regulations for AI
Compliance standards & regulations Compliance tools & AWS services
🎯 Goal: Secure, govern, and ensure compliance of AI systems in real-world deployments
Career Roles
AI/ML Engineer ML Ops Engineer AI Solutions Architect AI Ethics Specialist AI Security Engineer Gen AI Developer Data Scientist
Dive deep into how generative AI works — from transformer architecture to building real Gen AI applications on AWS.
Core Concepts How Generative AI Works The engine behind LLMs ▶
Overview of Gen AI Core components Transformer architecture In-context learning Multimodal models Diffusion models Gen AI architectures Key techniques & challenges
🎯 Goal: Understand the inner mechanics of how generative AI systems produce content
Business Use Gen AI for Business Problems Apply Gen AI practically ▶
Gen AI use cases Capabilities of Gen AI Limitations of Gen AI AI for business efficiency
🎯 Goal: Know when and how to apply Gen AI to solve real business problems
AWS Platform Building Gen AI on AWS Deploy with AWS services ▶
Advantages of AWS Gen AI services AWS infra for Gen AI applications Pricing models for LLMs AWS services for Gen AI apps Amazon SageMaker
🎯 Goal: Use AWS to build, deploy, and scale generative AI applications
Learn to select, prompt, fine-tune, and evaluate pre-trained foundation models to build powerful AI applications.
Step 1 Selecting & Designing with Foundation Models Choose the right model ▶
Selection criteria for pre-trained models Biases in training data Availability & compatibility Customisation & explainability Inference parameters Vector databases & retrieval RAG (Retrieval-Augmented Generation)
🎯 Goal: Pick and design with the right foundation model for your use case
Step 2 Prompt Engineering Get the best from LLMs ▶
Introduction to prompts Prompting techniques Best practices of prompt engineering Risks & limitations of prompt engineering
🎯 Goal: Write effective prompts that get accurate, reliable results from AI models
Step 3 Training & Fine-Tuning Customise models for your needs ▶
Key elements of training a foundation model Fine-tuning techniques Steps in data preparation for fine-tuning AWS data preparation tools
🎯 Goal: Fine-tune a foundation model on your own data for specialised tasks
Step 4 Evaluation & Deployment Measure and ship your model ▶
Deployment considerations Evaluation metrics Human evaluation Integration into applications
🎯 Goal: Measure model performance accurately and deploy it into production applications
Build AI systems that are fair, transparent, and ethical. Learn to detect bias, ensure explainability, and follow responsible AI principles.
Part 1 Developing Responsible AI Systems Fairness & ethics in AI ▶
Intro to responsible AI Addressing bias in AI models Ethical dataset characteristics Ethical model selection AWS services for monitoring & bias detection
🎯 Goal: Identify and reduce bias in AI systems from data to deployment
Part 2 Transparency & Explainability Make AI decisions understandable ▶
Why transparency matters in AI Explainable AI (XAI) concepts Transparency & explainability tools Techniques for transparent models
🎯 Goal: Make AI decisions interpretable and explainable to users and stakeholders
Secure, govern, and ensure compliance of AI systems. Learn IAM, encryption, monitoring, and regulatory compliance for AI on AWS.
Part 1 Securing AI Systems Protect your AI from threats ▶
Access & Identity
Shared responsibility model Identity & access management (IAM) Identity federation SageMaker role manager
Data & Network Security
Data encryption S3 block public access Security configuration & network management AI model security
Monitoring
Logging & monitoring
🎯 Goal: Implement end-to-end security for AI systems and data on AWS
Part 2 Governance & Compliance for AI Meet regulations & standards ▶
Compliance standards & regulations for AI systems Compliance tools & AWS services Governance frameworks Audit & accountability
🎯 Goal: Ensure AI systems meet legal, regulatory, and organisational governance requirements
Career Roles
AI Security Engineer AI Compliance Analyst Cloud AI Architect AI Ethics & Governance Lead
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