Learning and Publishing Roadmap
This roadmap shows how the site is growing and separates published material from ideas that are still being evaluated. It describes direction rather than promising completion dates.
Planned and exploratory subjects remain on this page until substantive notes exist. A new course or navigation section appears only when readers have useful material to open.
Status Guide
The status describes publication progress, not the value or difficulty of a subject.
| Status | Meaning |
|---|---|
| Maintained | Published material that remains part of the active site |
| In progress | Published material that is still being expanded |
| Planned | A committed direction that does not yet have enough material for its own section |
| Exploratory | A possible direction that is still being assessed |
| Archived | Older material preserved for reference at its original address |
Published Foundation
| Area | Status | Current scope |
|---|---|---|
| Networking | Maintained | Network architecture, design, routing, data center technologies, automation, and certification notes |
| Cybersecurity | In progress | Course hub reserved for complete curricula, with Kali Linux preserved in the archive |
| Machine Learning | Maintained | Supervised learning, unsupervised learning, model evaluation, anomaly detection, reinforcement learning, and federated learning |
| Deep Learning | In progress | Neural networks, optimization, project strategy, convolutional networks, and sequence models |
| Mathematics | Maintained | Linear algebra, calculus, probability, and statistics for machine learning and engineering |
Planned Areas
These subjects align with the focus of this site on networks, infrastructure, security, and practical AI architecture.
Kubernetes and Cloud Native
Kubernetes architecture, workloads, scheduling, networking, service discovery, storage, observability, platform operations, and security. This area will receive a navigation section only after the first substantive course material exists.
Agentic AI
Agent architecture, tools, memory, orchestration, evaluation, observability, identity, least privilege, approval boundaries, and safe automation.
AI Infrastructure and Networking
Networking for distributed AI systems, including compute and storage fabrics, east-west traffic, RDMA, RoCE, InfiniBand, congestion control, failure domains, segmentation, and GPU networking on Kubernetes.
AI for Networking and Security
Telemetry analysis, anomaly detection, incident correlation, configuration review, capacity forecasting, grounded assistants, and controlled operational agents.
AI Security and Governance
Threat modeling, adversarial machine learning, prompt injection, retrieval and tool security, model and data supply chains, evaluation, monitoring, risk ownership, and incident response.
Exploratory Areas
Exploratory subjects stay outside the navigation until their relevance and scope are clear.
Natural Language Processing
The Natural Language Processing Specialization is being assessed as supporting material rather than a committed course. Its most relevant topics are classification, embeddings, semantic retrieval, named entity recognition, attention, transformers, summarization, and question answering.
The first and fourth courses have the clearest connection to threat intelligence, retrieval-augmented generation, and security assistants. Completing the full specialization remains optional unless hands-on NLP model engineering becomes a priority.