AI DevOps & Code Quality in Thailand

All training modules incorporate PDPA compliance frameworks, covering consent management, data minimisation, and breach notification obligations. With THB 21.5 million in fines already levied in 2025 — including against data processors — we ensure your team understands both controller and processor obligations when deploying AI systems that handle personal data.

Thai businesses can access multiple incentives: DEPA grants up to THB 200,000 for digital transformation, a 200% tax deduction on qualifying digital expenses for SMEs (effective June 2025 through December 2027), BOI corporate income tax exemptions up to 13 years for AI investments, and the new Qualified Refundable Tax Credit at 30-50% for advanced skills development.

We deliver using a hybrid English-Thai approach with high-energy, interactive workshops that match Thai learner preferences. Content respects hierarchical management structures and the kreng jai norm of maintaining group harmony, using consensus-building exercises rather than confrontational case studies.

The 2025 Draft Principles of Thailand's AI Law introduces risk-based classifications including 'Prohibited-risk' and 'High-risk' AI categories. Our training builds governance frameworks that anticipate these requirements, so your organisation is prepared when the law is formalized in 2026.

AI test selection uses code change analysis (which files/functions modified) plus historical test failure data to predict which tests are most likely to fail. It typically runs 100% of tests on main branch merges and release candidates, but only 30-50% of tests on feature branch commits (catching 95%+ of bugs). We'll train you to configure risk tolerance: aggressive (fast feedback, slight risk) vs. conservative (slower but comprehensive).

Yes, for many incident types. AI excels at detecting gradual degradation: memory leaks (predict crash 20-40 minutes ahead), disk fill-up (predict 2-6 hours ahead), traffic spikes (predict 10-20 minutes ahead). AI struggles with sudden failures (hardware crashes, network partitions). Module 3 teaches you to combine AI predictive signals with traditional threshold alerts for comprehensive incident coverage.

We support major monitoring platforms: Datadog, New Relic, Prometheus/Grafana, AWS CloudWatch, Azure Monitor, Google Cloud Monitoring. During Content Customisation, we configure labs using your specific monitoring stack. If you're using custom/in-house tools, we'll show you how to integrate AI via APIs or metrics exporters. The AI principles are platform-agnostic.

Critical challenge addressed in Module 3. AI anomaly detection can generate 10x more alerts than threshold-based systems if tuned incorrectly. We train you to: (1) Use AI to score anomaly severity (ignore low-impact deviations), (2) Cluster related anomalies into single incident, (3) Learn normal baselines dynamically (reduce false positives over time), (4) Combine multiple signals before alerting (avoid single-metric noise). Result: 60-80% reduction in alert volume with higher signal quality.

Absolutely. Serverless teams benefit from AI in different ways: (1) AI cold start optimization (predict and pre-warm functions), (2) AI cost analysis (identify expensive function invocations), (3) AI observability (trace distributed serverless workflows), (4) AI auto-scaling (predict traffic and adjust concurrency limits). Module 1 and 3 include serverless-specific examples alongside traditional container/VM infrastructure.