/** * 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"], }, ];