AI Model Wiki

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    9 courses 10 hours

    AI Engineering Path

    Deep technical AI skills — fine-tuning, embeddings, MLOps, and building production AI products.

    Start with Fine-Tuning AI Models: LoRA, QLoRA & Beyond

    Courses in this path

    1. 1
      advanced

      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.

      5 lessons 80 minutes
    2. 2
      advanced

      AI Embeddings & Vector Databases

      Master the fundamentals of text embeddings, vector similarity search, and production vector database deployments with Pinecone, ChromaDB, and more.

      5 lessons 75 minutes
    3. 3
      advanced

      Evaluating & Benchmarking AI Models

      Learn to rigorously evaluate AI models using standard benchmarks, custom evaluations, red teaming, and LLM-as-judge techniques.

      5 lessons 70 minutes
    4. 4
      advanced

      AI Infrastructure & MLOps

      Master the infrastructure behind production AI — model serving, GPU optimization, monitoring, CI/CD for ML, and cost management at scale.

      5 lessons 75 minutes
    5. 5
      advanced

      Multimodal AI Applications

      Build applications that process and generate across modalities — vision-language models, document AI, cross-modal retrieval, and real-world integration…

      5 lessons 70 minutes
    6. 6
      intermediate

      Building AI Products: From Idea to Launch

      Learn the end-to-end process of building products powered by AI.

      5 lessons 50 minutes
    7. 7
      intermediate

      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.

      5 lessons 50 minutes
    8. 8
      intermediate

      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.

      6 lessons 45 minutes
    9. 9
      intermediate

      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…

      5 lessons 40 minutes