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AI Studio

This module focuses on building and deploying AI-powered applications from research to production, covering LLMs, RAG, secure APIs, multi-platform UIs, and scalable DevOps. By the end, learners will launch a fully functional AI product with web, desktop, and mobile interfaces.

Day 1-15: AI Fundamentals & Project Setup​

Topics Covered​

  • Full‐Stack AI Overview
    • Architectural overview of AI-driven systems that include LLMs, smaller ML models, and classical AI pipelines.
    • Understanding how these integrate into frontends, backends, and DevOps for multi‐platform distribution.
  • LLM Research Foundations
    • Evolution from RNN/LSTM to Transformers.
    • Key papers: Attention is All You Need, BERT, GPT‐3.
  • Environment Setup
    • Configure Docker for AI development: GPU support, CUDA drivers containerization best practices.
    • Local environment with Python, Jupyter Notebooks, PyTorch/TensorFlow, HuggingFace Transformers, Overleaf for collaboration, Git for version control.

Hands‐on Tasks​

  • Create a project scaffold in GitHub with:
    • Basic Python environment (FastAPI or Flask for minimal testing).
    • Docker Compose to ensure consistent dev environments.
  • Annotate key LLM research papers in a shared Overleaf or Markdown doc.

Deliverables​

  • Summary Report detailing the architecture, challenges, and design considerations for an AI‐enabled system.
  • Public GitHub Repo with the initial project scaffold and a minimal CI/CD pipeline (GitHub Actions or Jenkins).
  • Research Notes (annotated paper summaries) in the repository.

Day 16-30: Deep Dive into Model Architectures & Tokenization​

Topics Covered​

  • Transformer Architectures
    • Multi‐head attention, positional encodings, feed‐forward layers.
  • Tokenization Techniques
    • BPE, SentencePiece, WordPiece.
    • Trade‐offs of vocabulary size vs. performance.
  • Challenges
    • Managing large model weights, memory constraints, and multi‐GPU setups.

Hands‐on Tasks​

  • Experiment with HuggingFace Tokenizers to compare performance and accuracy differences between BPE/SentencePiece.
  • Explore minimal WASM integrations (e.g., tokenizers that run in a browser/desktop environment for local inference).

Deliverables​

  • Detailed Research Document on transformer architectures and tokenization with code examples.
  • Public Blog Post explaining different tokenization strategies, linked to:
    • A GitHub repo containing tokenization scripts and evaluation notebooks.

Day 31–45: Fine‐Tuning, Prompt Engineering & Retrieval‐Augmented Generation​

Topics Covered​

  • Fine‐Tuning
    • Using HuggingFace Transformers to adapt GPT‐2, T5, or LLaMA to domain‐ specific data.
  • Prompt Engineering
    • Strategies for effective prompts, prompt sensitivity, few‐shot learning.
  • Retrieval‐Augmented Generation (RAG)
    • Integrating external knowledge bases with vector databases (FAISS, Pinecone) for efficient retrieval.
  • Challenges
    • Tuning hyperparameters to minimize catastrophic forgetting, ensuring stable training.

Hands‐on Tasks​

  • Fine‐tune a small GPT‐2 or T5 on a text dataset relevant to your business domain.
  • Build a minimal RAG prototype with FAISS to handle QA queries.

Deliverables​

  • GitHub Repository with your fine‐tuning code, including prompt templates.
  • Detailed Blog Tutorial describing prompt engineering, data pipeline, and RAG integration.

Day 46–60: Evaluation, Bias & Fairness, and Advanced AI Topics​

Topics Covered​

  • Evaluation Metrics & Tools
    • Perplexity, BLEU, ROUGE, HuggingFace Evaluate, OpenAI Evals, and custom human eval loops.
  • Bias, Fairness & Ethics
    • Identifying potential biases, using adversarial training, or bias filters.
  • Multimodal Extensions
    • Explore DALL‐E/CLIP style models or basic text‐to‐image.
  • Challenges
    • Standardizing benchmarks for varied LLM outputs, resources for large model training.

Hands‐on Tasks​

  • Evaluate your model using multiple metrics, including human feedback.
  • Attempt a small text‐to‐image generation experiment (e.g., using stable diffusion or DALL‐E mini).

Deliverables​

  • Comprehensive Evaluation Report with data on performance metrics.
  • Public Blog Post outlining best practices to mitigate bias and a short demo of any multimodal experiments.
  • Architecture Diagrams (Draw.io or similar) showing the AI pipeline end to end.

Day 61–75: Modern Web & Desktop UI for AI​

Topics Covered​

  • React, Next.js, Lynx.js
    • Building responsive and dynamic UIs.
    • SSR (Server‐Side Rendering) with Next.js for SEO and performance.
  • Desktop Packaging
    • Using Tauri to wrap React or Next.js apps as native desktop applications.
    • Considering WASM for partial AI inference on the desktop.
  • State Management & Asynchronous Flows
    • Redux, Zustand, or Context API (for React).
    • Data fetching from AI endpoints.

Hands‐on Tasks​

  • Create a multi‐platform frontend app that calls your AI inference API:
  • Web version using Next.js. Desktop version packaged with Tauri.
  • Integrate real‐time updates from your AI service (chatbots, image generator progress, etc.).

Deliverables​

  • Tutorial Project with complete source code demonstrating integration of AI components across web and desktop.
  • Public Blog Post with code snippets, UI design best practices, and a live/demo build.

Day 76–90: Mobile Applications & Advanced Frontend Features​

Topics Covered​

  • Cross‐Platform Mobile Development
    • Flutter or Lynx.js mobile approach.
    • Handling AI calls from mobile devices, offline caching.
  • Performance Optimization
    • Caching responses, lazy loading, advanced bundling for
    • Next.js/Tauri/Flutter.
  • Integration with Third‐Party APIs
    • Payment gateways, social logins, analytics.

Hands‐on Tasks​

  • Develop a small mobile app that consumes your AI inference endpoints (e.g., text generation, image generation).
  • Integrate third‐party analytics to track usage and performance (e.g., Firebase, Mixpanel).

Deliverables​

  • Mobile App Demo (APK, iOS test flight, or a cross-platform build) integrated with your AI backend.
  • Detailed Documentation on the cross‐platform architecture, posted publicly.
  • Performance Benchmarks showing differences between web/desktop/mobile usage.

Day 91–105: Secure API Development & Advanced Auth​

Topics Covered​

  • API Design
    • FastAPI/Flask or Node.js (Express) endpoints for AI services.
  • Security & Authentication
    • OAuth2.0, JWT, HTTPS, token management, best practices.
  • Challenges
    • Protecting AI endpoints, data encryption at rest and in transit, rate limiting.

Hands‐on Tasks​

  • Build a secure API layer with advanced authentication.
  • Implement role‐based access control or token scoping for AI endpoints.

Deliverables​

  • Secure Demo Application with complete source code.
  • Blog Post documenting security protocols and best practices.

Day 106–120: DevOps, Deployment & Container Orchestration​

Topics Covered​

  • CI/CD Pipelines
    • Automating test, build, and deployment for AI backend and frontends.
    • Tools: GitHub Actions, Jenkins, CircleCI.
  • Container Orchestration
    • Docker, Kubernetes (K8s), Helm for AI model serving at scale.
  • Challenges
    • Ensuring consistent model versions, GPU scheduling, auto‐scaling.

Hands‐on Tasks​

  • Set up a Kubernetes cluster (on AWS, GCP, or Digital Ocean) to deploy your AI services.
  • Implement auto‐scaling for high‐load inference.

Deliverables​

  • Fully Automated CI/CD pipeline with test reports, container builds, and K8s deployment scripts.
  • Architecture Diagrams detailing your DevOps flow.

Day 121–135: Data Management & Real‐Time Analytics​

Topics Covered​

  • Databases & Data Pipelines
    • PostgreSQL, MongoDB, Redis for caching, real‐time analytics with Kafka or AWS Kinesis.
  • Performance Monitoring
    • Prometheus, Grafana dashboards for AI inference metrics.
  • Challenges
    • Handling structured vs. unstructured data, ensuring data consistency at scale.

Hands‐on Tasks​

  • Implement a data ingestion pipeline that stores logs and user interactions in a real‐time database.
  • Set up a Grafana dashboard to visualize inference latency, throughput, errors.

Deliverables​

  • Code Samples in a GitHub repo demonstrating ingestion & real‐time analytics.
  • Documentation & Blog Post on data architecture, real‐time dashboards, best practices.

Day 136–150: Scaling & Final Integrations​

Topics Covered​

  • Scaling Full‐Stack AI
    • Horizontal scaling, load balancing, caching, concurrency.
    • Using WASM for certain local inferences or logic.
  • Integration with External APIs
    • Payment, social media, advanced analytics.
  • Challenges
    • Handling large concurrency and data loads while keeping latency low.

Hands‐on Tasks​

  • Implement auto‐scaling with a load balancer, measure performance under stress tests.
  • Finalize end‐to‐end integration for a “Prodigal AI Suite” sub‐product (e.g., text generator or image social media automation).

Deliverables​

  • Research Report with architecture diagrams (Draw.io) on scaling strategies.
  • Public Case Study demonstrating results of load and concurrency tests.

Day 151–180: Final Project & Deployment​

Topics Covered​

  • Final Project Planning
    • Collaborative design, risk management, PRD finalization, architecture diagrams.
  • Implementation & Testing
    • End‐to‐end development, integration tests, security audits.
  • Live Deployment
    • CI/CD for production, multi‐platform distribution (web, desktop via Tauri, mobile via Flutter/Lynx.js).

Hands‐on Tasks​

  • Implement and launch the full Prodigal AI Studio or a sub‐product (e.g., “Prodigal Text Automation Tool”) in a production environment.
  • Perform final performance and security audits.

Deliverables​

  • Fully Functional AI Application with public access or easily installable desktop/mobile versions.
  • Comprehensive Testing Reports documenting performance, load, and security.
  • Final Presentation or webinar showcasing the product’s capabilities, architecture, and roadmap.