server/src/data.js

/**
 * Mock educational content catalogue and user profiles.
 *
 * In production this module would be replaced by a database adapter.
 */

// ---------------------------------------------------------------------------
// Content catalogue (10 items)
// ---------------------------------------------------------------------------
export const CONTENT_ITEMS = [
  {
    id: 1,
    title: "Introduction to Kubernetes for ML Engineers",
    description:
      "Hands-on deployment walkthrough using Docker and Kubernetes. Covers pod creation, service exposure, and scaling ML inference endpoints.",
    difficulty: "Intermediate",
    duration_minutes: 45,
    tags: ["kubernetes", "ml", "deployment", "docker"],
    format: "video",
  },
  {
    id: 2,
    title: "Python for Data Science \u2013 From Zero to Pandas",
    description:
      "Beginner-friendly course covering Python basics, NumPy arrays, and Pandas DataFrames for exploratory data analysis.",
    difficulty: "Beginner",
    duration_minutes: 60,
    tags: ["python", "data-science", "pandas", "numpy"],
    format: "lecture",
  },
  {
    id: 3,
    title: "Deep Learning Fundamentals with PyTorch",
    description:
      "Build neural networks from scratch using PyTorch. Covers tensors, autograd, CNNs, and training loops with real datasets.",
    difficulty: "Intermediate",
    duration_minutes: 90,
    tags: ["deep-learning", "pytorch", "neural-networks", "ml"],
    format: "video",
  },
  {
    id: 4,
    title: "MLOps Pipeline Design Patterns",
    description:
      "Slide deck covering CI/CD for ML models, feature stores, model registries, and monitoring in production.",
    difficulty: "Advanced",
    duration_minutes: 30,
    tags: ["mlops", "ci-cd", "deployment", "monitoring"],
    format: "slides",
  },
  {
    id: 5,
    title: "Natural Language Processing with Transformers",
    description:
      "Understand attention mechanisms, BERT, and GPT architectures. Includes fine-tuning a text classifier on custom data.",
    difficulty: "Advanced",
    duration_minutes: 75,
    tags: ["nlp", "transformers", "bert", "ml"],
    format: "lecture",
  },
  {
    id: 6,
    title: "Data Engineering with Apache Spark",
    description:
      "Process large-scale datasets using PySpark. Covers RDDs, DataFrames, Spark SQL, and integration with cloud storage.",
    difficulty: "Intermediate",
    duration_minutes: 50,
    tags: ["data-engineering", "spark", "python", "big-data"],
    format: "video",
  },
  {
    id: 7,
    title: "Git & GitHub for Collaborative Projects",
    description:
      "Learn branching strategies, pull requests, merge conflicts, and GitHub Actions for automating workflows.",
    difficulty: "Beginner",
    duration_minutes: 25,
    tags: ["git", "github", "collaboration", "ci-cd"],
    format: "slides",
  },
  {
    id: 8,
    title: "Building REST APIs with FastAPI",
    description:
      "Create production-ready REST APIs with FastAPI. Covers path parameters, Pydantic validation, async handlers, and OpenAPI docs.",
    difficulty: "Intermediate",
    duration_minutes: 40,
    tags: ["fastapi", "python", "api", "backend"],
    format: "video",
  },
  {
    id: 9,
    title: "AI Model Deployment on AWS SageMaker",
    description:
      "Step-by-step guide to packaging, deploying, and A/B testing ML models on AWS SageMaker with auto-scaling.",
    difficulty: "Advanced",
    duration_minutes: 55,
    tags: ["aws", "sagemaker", "deployment", "ml"],
    format: "lecture",
  },
  {
    id: 10,
    title: "Prompt Engineering for Large Language Models",
    description:
      "Master prompt design techniques: few-shot, chain-of-thought, and system prompts for ChatGPT, Claude, and open-source LLMs.",
    difficulty: "Beginner",
    duration_minutes: 35,
    tags: ["llm", "prompt-engineering", "ai", "nlp"],
    format: "slides",
  },
];

// ---------------------------------------------------------------------------
// User profiles (3 personas)
// ---------------------------------------------------------------------------
export const USER_PROFILES = [
  {
    user_id: "u1",
    name: "Alice",
    goal: "Learn to deploy ML models into production using Kubernetes and cloud platforms",
    learning_style: "visual",
    preferred_difficulty: "Intermediate",
    time_per_day: 60,
    viewed_content_ids: [1],
    interest_tags: ["ml", "deployment", "kubernetes", "docker"],
  },
  {
    user_id: "u2",
    name: "Bob",
    goal: "Transition from software engineering to data science and machine learning",
    learning_style: "hands-on",
    preferred_difficulty: "Beginner",
    time_per_day: 45,
    viewed_content_ids: [7],
    interest_tags: ["python", "data-science", "ml", "numpy"],
  },
  {
    user_id: "u3",
    name: "Carol",
    goal: "Master advanced NLP and LLM techniques for building AI-powered applications",
    learning_style: "reading",
    preferred_difficulty: "Advanced",
    time_per_day: 90,
    viewed_content_ids: [5],
    interest_tags: ["nlp", "transformers", "llm", "prompt-engineering"],
  },
];