AI DevOps & Code Quality in Indonesia

Our programme covers the March 2026 compliance deadline, data localisation requirements, and progressive sanctions under GR 71/2019's implementing regulation. We help tech teams assess which of their systems qualify as Public Electronic Systems and build compliance plans.

Yes. If your company serves financial sector clients, we cover OJK's Banking AI Governance guidelines (April 2025) including human-in-the-loop requirements, AI Committee structures, and audit trail mandates that your products and services must support.

We focus on applied AI for business outcomes rather than data science theory. While Algoritma and Indonesia AI offer excellent technical training, our programmes build the governance, change management, and strategic capabilities that complement technical skills.

Yes. We help teams identify complementary pathways including the Digital Talent Scholarship (60,000 scholarships in 2025), elevAIte Indonesia (Microsoft-KOMDIGI partnership for 1 million participants), and Kartu Prakerja subsidies for individual team members.

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.