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Hedronite

Hedronite Academy

How operators are made.

Hedronite Academy transitions DevOps engineers into AI and ML Engineers. We build upon your DevOps foundation: Linux, cloud, Kubernetes, Terraform — and from there, teach you how it maps onto Artificial Intelligence and Machine Learning Operations. The curriculum of the program is produced by the work we do within Hedronite Foundry and Hedronite Capital: distilling a corpus into training data, fine-tuning specialist models, proving them against real benchmarks, and deploying them in production.

From that core the knowledge branches into three applied domains that intersect at the engineer's discretion. Agent Operations teaches AI systems in real production environments. Trading Operations puts agents to work across crypto and traditional markets. Blockchain Operations engineers intelligence into on-chain infrastructure. Learn the ML engineering core along with one branch, or several. You will be learning practical application alongside theory.

AI/ML Engineering

Trains and deploys the models themselves. The full lifecycle we run in Hedronite Foundry: turning a corpus into training data, fine-tuning a specialist model, proving it on real benchmarks, and serving it in production. Where a frontier lab trains models from scratch, this is how a small team trains open models to compete in specialized domains with frontier models.

  • Data and distillation: dataset construction, corpus versioning, provenance tracking, retrieval-shape preservation
  • Fine-tuning open models: QLoRA, continuation pretraining, and supervised fine-tuning on Gemma-class variants
  • Distributed training across hybrid cloud environments, with version control discipline maintained throughout
  • 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
  • Cert prep: Google Professional ML Engineer, Google AI Professional, AWS Machine Learning Engineer Associate, AWS AI Practitioner, Azure Machine Learning Operations Associate

Agent Operations

Builds the system around the model. Tool-use and planning, memory and session learning, multi-agent orchestration, and the guardrails that give an agent capability without free rein. While AI/ML Engineering trains the model, Agent Operations turns it into a production worker.

  • 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: Anthropic Certified Claude Architect, Google Generative AI Developer, AWS Generative AI Developer Professional, Azure AI Engineer Associate, GitHub Agentic AI Developer

Trading Operations

Puts models and agents to work across crypto and traditional markets. Signal research, execution infrastructure, position sizing, and the risk plumbing that decides whether the desk survives its first ten-sigma move. Where AI/ML Engineering trains the model and Agent Operations gives it autonomy, Trading Operations gives it a market.

  • Machine-learning signal generation: feature engineering, multi-signal filtering, model-driven selectivity
  • Trading-strategy agents: autonomous research-to-execution loops, backtest-to-live promotion gates, paper-mode validation first
  • Market microstructure: orderbook dynamics, perpetual and options markets, MEV defense
  • Execution algorithms: smart routing, fill reconciliation, idempotency discipline
  • Position sizing using regime-conditional Kelly, catalyst-quality discounting, pre-trade gates
  • Risk management: circuit breakers, drawdown monitoring, kill-switch discipline
  • Venue integration across DEX and CEX APIs, on-chain settlement, tokenized RWA exposure

Blockchain Operations

Engineers the infrastructure that lives on chain, and the intelligence that runs on it. Protocol engineering, validator operations, smart-contract architecture, and on-chain data systems, joined to the agents that read the chain and act on it.

  • Smart contract patterns: Solidity for EVM chains, Rust and CosmWasm for Cosmos and Solana ecosystems
  • Validator operations: node setup, key management, slashing protection, upgrade coordination
  • On-chain data architecture: indexing pipelines, archive nodes, query optimization, and chain data as training and analytics input
  • On-chain intelligence agents: analytics over indexed chain data, DAO governance agents, anomaly and MEV-pattern detection
  • Protocol upgrades: pre-activation rehearsal, state migration discipline, fork coordination
  • IBC and cross-chain messaging: relayer operations, light client verification, packet lifecycle
  • Cert pathway: Interchain Foundation Developer, institutional courses on validator economics

Hedronite Academy goes beyond cert prep. We train engineers to build, evaluate, and operate AI systems with practitioner-level rigor, and continue the education arc after graduation through ongoing specialization material.