Fine-Tuning AI Models: LoRA, QLoRA & Beyond
Learn how to customize large language models for your specific use case using parameter-efficient fine-tuning techniques like LoRA and QLoRA.
AI Embeddings & Vector Databases
Master the fundamentals of text embeddings, vector similarity search, and production vector database deployments with Pinecone, ChromaDB, and more.
Evaluating & Benchmarking AI Models
Learn to rigorously evaluate AI models using standard benchmarks, custom evaluations, red teaming, and LLM-as-judge techniques.
AI Infrastructure & MLOps
Master the infrastructure behind production AI — model serving, GPU optimization, monitoring, CI/CD for ML, and cost management at scale.
Multimodal AI Applications
Build applications that process and generate across modalities — vision-language models, document AI, cross-modal retrieval, and real-world integration patterns.
Building AI Products: From Idea to Launch
Learn the end-to-end process of building products powered by AI. Covers ideation, prototyping, model selection, UX design, evaluation, deployment, and go-to-market strategy.
AI for Data Visualization: Charts, Dashboards & Insights
Turn raw data into compelling visuals with AI. Learn to generate charts, build dashboards, create automated reports, and extract insights — no coding required.
AI for Data Science: From Cleaning to Storytelling
Leverage AI assistants to accelerate every stage of data science — cleaning, exploration, SQL generation, dashboards, machine learning, and data storytelling.
Running AI Locally: Privacy, Control & Offline Access
Learn to run powerful AI models on your own hardware. Cover Ollama, LM Studio, llama.cpp, GPU requirements, quantization, fine-tuning with LoRA, and building local AI applications.