AI Solution Architect
Installfest Guides
None
Pacing Guide
None
Content
| Module name | Type | Duration | About |
|---|---|---|---|
| 1.1 Quick Refresher to ML | Lecture | 90 min | Explore types of ML Models (Supervised, Unsupervised, Reinforcement Learning) and use cases. |
| 1.2 Evaluation Metrics for Supervised ML Models | Lecture | 90 min | Learn metrics to evaluate model performance across regression and classification tasks. |
| 1.3 ML Workflow and Best Practices | Lecture | 90 min | End-to-end ML workflow (EDA, feature engineering, bias-variance tradeoff, train/test split, cross validation) and ways to break down a problem to determine challenges with accuracy, latency, and cost. |
| 1.4 Lab: Hands-on Implementation of ML Workflow | Lab | 90 min | TBD |
| 2.1 Intro to Neural Networks + Overview of AI Architectures | Lecture | 90 min | Key DL architectures like feed-forward, CNN, RNN, transformer. AI architecture for different data modalities: picking the right model for the right modality, data size, etc. |
| 2.2 Unsupervised Learning & Metrics | Lecture | 90 min | Unsupervised learning techniques like clustering and dimensionality reduction; evaluate methods using appropriate metrics. |
| 2.3 Data Governance & Security in AI | Lecture | 90 min | Data security, privacy and bias considerations; Deloitte’s Trustworthy AI Framework. |
| 2.4 Lab: Training/Fine-Tuning Deep Learning Models | Lab | 90 min | TBD |
| 3.1 Data Management in AI Projects | Lecture | 90 min | TBD |
| 3.2 Data Pipelines and Workflow Orchestration | Lecture | 90 min | TBD |
| 3.3 Lab: Designing and Building a Data Pipeline using Apache Airflow | Lab | 90 min | TBD |
| 3.4 AI in Practice & Business Value Identification | Lecture | 90 min | TBD |
| 4.1 AI Model Deployment | Lecture | 90 min | TBD |
| 4.2 MLOps Fundamentals | Lecture | 90 min | TBD |
| 4.3 Lab: MLOps using MLFlow | Lab | 90 min | TBD |
| 5.1 GenAI: LLM Capabilities & Alignment | Lecture | 90 min | Explore different stages of building LLM apps; intro to RAG application and evaluations. |
| 5.2 Intro to AI Agentic Workflows | Lecture | 90 min | Intro to AI agents and building agents with LangGraph. |
| Total content | Total | ~25.5 total course hours |
Projects
| Module name | About |
|---|---|
| 4.4 Capstone Project | What do students do in this project? |
| 5.4 Capstone Project | What do students do in this project? |