HedroniteAcademy

Learn the system. Enter the lab. Prove the skill.

Lesson to lab
Evidence over completion
Production-minded practice

Why Academy

Knowledge becomes capability only when a learner can make the system work.

Hedronite Academy is an interactive AI education platform for engineers moving from DevOps into AI and machine learning operations. Every lesson can lead into a guided sandbox lab: build the system, run the task, inspect the evidence, and receive immediate validation against production-minded checks.

The curriculum is forged from the same infrastructure, evaluation discipline, and operating practice used by Hedronite Foundry and Solutions. Learning paths begin with Linux, cloud, Kubernetes, and Terraform, then branch into DevSecOps Engineering, Agentic AI Operations, Generative AI Engineering, and Machine Learning Operations. Theory explains the mechanism; the lab proves you can operate it.

Learning paths

Read the mechanism. Enter the lab. Prove the skill.

Open each field to inspect the operating detail, standards, and intended outcomes.

DevSecOps Engineering

Security engineered into every stage instead of bolted on at the end. Hardened CI/CD, infrastructure, and Kubernetes operations, with AI integration sharpening monitoring, alerting, and threat detection across the stack.

Inspect scope

Secure CI/CD: pipeline gates, artifact signing, SBOM generation, and dependency scanning on every build
Infrastructure security: Terraform scanning, IAM least-privilege review, network policy, and secrets hygiene
Kubernetes hardening: admission control, runtime policy, and audit logging as the default configuration
AI-integrated monitoring: anomaly detection, alert correlation, and incident summarization across the observability stack
Threat detection where models watch the stream and an analyst validates every finding before it pages
Cert prep: CompTIA Security+ and CySA+, plus the cloud security specialties across AWS, Azure, and GCP

Agentic AI Operations

Build the system around the model. Guided scenarios exercise tool use, planning, memory, multi-agent orchestration, and the guardrails that give an agent capability without free rein.

Inspect scope

Agent architecture patterns: hooks, MCPs, tool-use design, planning loops, memory layers, session learning
Context engineering: skills, retrieval, memory management, prompt engineering, runbooks, schema design
Multi-agent orchestration: governance gates, role boundaries, handoff protocols between agents, deterministic workflows
Agent evaluation in production: task-completion suites, regression testing, behavioral telemetry and drift detection
Guardrails and runtime safety: pre-tool risk gates, permission boundaries, auditing, protocol enforcement
Cert prep: Google Generative AI Developer, AWS Generative AI Developer Professional, Azure AI Engineer Associate, GitHub Agentic AI Developer

Generative AI Engineering

Applied generative AI across the whole stack. Use models on frontend, backend, and database work to write, test, and debug code faster—and use retrieval, guardrails, and alignment to make the outputs trustworthy.

Inspect scope

AI-accelerated development across frontend, backend, and database layers: generation, refactor, and review loops
Test synthesis and debugging with models: regression coverage, reproduction cases, and root-cause drafts
RAG pipelines: retrieval design, chunking strategy, grounding, and evaluation that measurably improves output quality
Guardrails and alignment: output constraints, refusal correctness, and evaluation gates before anything ships
Agentic coding harnesses—Cursor, Codex, and Cline-class tools—operated with review discipline
Prompt and context engineering treated as operational craft, captured in reusable runbooks

Machine Learning Operations

Post-training as an operating discipline rather than a research exercise. Fine-tune, evaluate, deploy, and watch models in production—MLOps covers everything after the base model exists.

Inspect scope

Fine-tuning open-weight models: LoRA/QLoRA, supervised fine-tuning, and continuation passes on open bases
Evaluation that gates release: domain-accuracy and reasoning-fidelity benchmarks, adversarial validation, A/B before promotion
Model serving and runtime: harness engineering, production deployment, memory architecture, tool-call routing, drift detection
Post-training data operations: corpus versioning, provenance tracking, and quality stamps before any run
Regression suites and drift monitoring for models already live, with rollback paths defined in advance
Cert prep: AWS Machine Learning Engineer Associate, Google Professional ML Engineer, Azure Machine Learning Operations Associate

The library

Three ways in.

Technical lessons from the Founder’s continuing education.

The lesson library covers cloud engineering, AI/ML engineering, crypto and blockchain fundamentals, and quant studies. Topics include cert prep for Kubernetes, Terraform, AWS, and Google Cloud, alongside polyglot development across Python, Rust, Go, and the web stack (JavaScript, TypeScript, HTML).

Each lesson is authored at production speed alongside the work itself: the patterns that hold under load, the failure modes that surface at scale, and the discipline that keeps a production system running.

The lesson library is opening. New lessons publish daily, paired with the work that produces them.

The standard