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* 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"],
},
];
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