Technical Section
Deep technical dives into Generative AI architectures, patterns, and skills
GenAI
LLMs, Agents, Vector DBs, ML Ops
Google Cloud
Compute, Vertex AI, GKE, Security
AWS
EC2, Bedrock, Sagemaker, Lambda
Azure
OpenAI Service, AKS, Cosmic DB
Category Name
LLM Fundamentals
The building blocks behind language models — from tokens and attention to RAG and prompt engineering.
Agents & Orchestration
Autonomous systems that plan, reason, and use tools. From single-agent loops to multi-agent pipelines.
Computer Vision
Models that see and interpret images, video, and visual data using modern deep learning architectures.
Voice AI & Audio
Speech recognition, synthesis, and real-time voice pipelines powering the next generation of voice interfaces.
Frameworks & Ecosystem
The SDKs, platforms, and cloud services that make building GenAI applications faster and production-ready.
LLM Observability Tools
Monitor, trace, and evaluate your LLM applications in production. Essential for reliability and cost control.
Data & Representations
How models store, retrieve, and reason over structured and unstructured data at scale.
Vector Databases
Purpose-built databases for similarity search over high-dimensional embeddings — the backbone of RAG.
Text Embeddings
Dense vector representations of text that power semantic search, clustering, and retrieval.
Data Extraction
Techniques and tools for parsing, structuring, and extracting information from unstructured documents.
Security, Governance & Compliance
Protecting GenAI systems against adversarial attacks, data leakage, and policy violations.
Evaluation & Quality
Metrics, benchmarks, and automated frameworks for measuring LLM quality and reliability.
Models & Strategies
The frontier models shaping the GenAI landscape — capabilities, trade-offs, and use cases.
Open LLMs Access
Open-weight models you can run locally or on your own infrastructure, without API dependency.
Infrastructure & Cloud
The compute, storage, and networking layer for training, serving, and scaling AI workloads.
Multimodal & Interfaces
Models that process and generate across text, image, audio, and video modalities.
Machine Learning
Classical and modern ML techniques that underpin deep learning and GenAI model development.
Performance & Cost
Techniques for reducing latency, improving throughput, and cutting inference costs without sacrificing quality.
LLMOps
CI/CD, experiment tracking, and deployment pipelines adapted for the lifecycle of LLM applications.
Integration & Automation
Connecting LLMs to APIs, databases, and workflows through function calling and event-driven patterns.
DevOps & Professional Deploy
Container orchestration, scaling strategies, and production-grade deployment for AI services.
GenAI Architecture
System design patterns for reliable, scalable, and maintainable Generative AI applications.