Trend
From prompts to processes
The biggest productivity gains come from repeatable workflows. Learn how teams turn prompts into checklists, templates, and CI steps that scale.
Practical AI for real IT work — engineering, data, security, operations
Build AI fluency that ships to production, not just demos.
otevaxun is an AI-first training platform for software teams and ambitious professionals. We blend structured courses, hands-on labs, and analytics-driven feedback so learners can adopt modern tools without losing engineering discipline.
Hands-on labs
Team dashboards
AI safety & governance
Modern DevOps
Career-ready projects
Learning paths
Focused curricula built around job tasks: planning, building, reviewing, testing, deploying, and monitoring.
8–12 weeks
AI-assisted practice
Guided prompts, rubric-based feedback, and realistic constraints: cost, latency, privacy, reliability.
Labs
Measurable outcomes
Skill signals derived from artifacts: pull requests, runbooks, incident notes, data pipelines, threat models.
Evidence
Quality guardrails
We teach how to validate AI outputs: tests, checklists, review patterns, and safe deployment.
No magic
Course design
Curricula built from IT workflows
Each module maps to a real job-to-be-done: write a migration plan, refactor a service, design a monitoring strategy, or create a secure data contract. You learn the tool, then you learn the habit.
Team-ready
Enablement for squads and orgs
We support teams that want consistent patterns: prompt playbooks, coding standards for AI suggestions, and shared evaluation metrics that align engineering and compliance.
Research-backed
Analytics, not hype
Our editorial team publishes trend reports on AI tooling, software quality, and the economics of automation. We highlight tradeoffs and failure modes so you can decide what is worth shipping.
Choose a direction. Keep the engineering bar high.
AI can accelerate development, but it can also amplify complexity. Our tracks teach concrete techniques: constraint-first prompting, verification loops, observability, and risk-aware rollout.
AI for Engineers
Shipping faster without breaking prod
From code review and test generation to architecture notes and refactoring plans. You practice with guardrails.
Data & MLOps
Reliable pipelines, measurable models
Data quality checks, reproducible experiments, model monitoring, and incident response for ML systems.
Security Automation
Safer automation, smarter triage
Threat modeling with AI assistants, log summarization, policy-as-code prompts, and red-team aware workflows.
Featured analysis
A selection of recent editorial pieces. They are designed to connect: each article links to a practical lesson inside the platform.
Quality
AI code suggestions and hidden debt
Speed without review creates subtle debt. We propose a lightweight rubric for correctness, maintainability, and security that works even under time pressure.
Operations
Runbooks for AI systems
AI features fail differently: drift, tool outages, prompt regressions. We outline what to monitor and how to prepare incident response.
Start with one week. Prove value. Then scale.
If your team is evaluating AI tooling, begin with a pilot that produces artifacts you can review: PRs, tests, runbooks, and a small production change behind a feature flag.