How does AI intelligent test selection decide which tests to run without missing critical bugs?

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).

Can AI really predict infrastructure incidents before they happen?

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.

What infrastructure monitoring tools does this training integrate with?

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.

How do we avoid alert fatigue when AI detects anomalies constantly?

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.

Is this training relevant for teams using serverless architectures (AWS Lambda, etc.)?

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.

Training Solutions

TECH-DEVOPS

Cohort-basedSubsidy eligible

AI DevOps & Code Quality Automation

DevOps teams can deploy AI-powered CI/CD optimization, code quality automation, and predictive infrastructure monitoring — improving velocity by 40-60% and reducing incidents by 30-50%

Specialist training for DevOps engineers and engineering leads to deploy AI-powered CI/CD optimization, intelligent testing, code quality analysis, infrastructure monitoring, and incident prediction. Designed for tech companies seeking to reduce deployment risk, improve code quality, and accelerate release velocity through AI automation.

Duration3-4 days

InvestmentUSD $16,000 - $30,000

Best forDevOps engineers, SREs, platform teams, and engineering managers responsible for CI/CD pipelines, infrastructure reliability, and code quality in technology companies

Start a Conversation Request Solution Brief

THE CHALLENGE

Sound familiar?

“

Our CI/CD pipeline runs all tests on every commit — it takes 45 minutes and slows down deployments.

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Production incidents happen but we don't know why until we're paged at 2am investigating logs.

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Code quality varies wildly across teams — some PRs have 90% test coverage, others have 20%.

“

We're manually reviewing infrastructure metrics looking for anomalies when AI could flag them instantly.

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Deployment rollbacks happen 15% of the time because we can't predict which changes will break production.

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OUTCOMES

What you'll achieve

Problems you'll solve

CI/CD pipelines running inefficiently (all tests on every commit instead of intelligent test selection)
Production incidents detected reactively through alerts instead of predicted proactively with AI
Code quality inconsistencies with no automated enforcement of coverage, complexity, and security standards
Infrastructure monitoring requiring manual log analysis instead of AI anomaly detection
Deployment risk assessment relying on intuition instead of AI-powered impact prediction

Value you'll gain

Deployment Velocity: Reduce CI/CD runtime by 40-60% using AI intelligent test selection and parallel optimization
Reliability: Decrease production incidents by 30-50% through AI predictive monitoring and anomaly detection
Code Quality: Improve test coverage by 25-40% and reduce technical debt using AI-powered quality gates
MTTR Reduction: Cut mean-time-to-recovery by 50-70% using AI incident prediction and auto-remediation
Cost Savings: Reduce cloud infrastructure costs by 15-25% through AI-optimized resource allocation

OUR PROCESS

How we deliver results

Step 1

DevOps Pipeline Assessment

Audit CI/CD workflows, testing strategies, deployment processes, and infrastructure monitoring to identify AI optimization opportunities.

Step 2

Tool Integration Planning

Select AI DevOps tools (test optimization, code analysis, monitoring) and design integrations with your existing CI/CD and cloud infrastructure.

Step 3

Hands-On Delivery

Multi-day programme building AI-powered CI/CD pipelines, code quality analyzers, infrastructure anomaly detectors, and incident prediction models.

Step 4

Pipeline Automation Development

Teams build production-ready AI DevOps automations: intelligent test runners, deployment risk predictors, or infrastructure auto-scaling systems.

Step 5

Production Deployment

30-day coaching to deploy AI DevOps tools into production workflows, measure impact on velocity and reliability, and iterate based on performance data.

What you'll receive

DevOps AI Readiness Assessment with pipeline optimization analysis
Production-ready AI automations for CI/CD, code quality, and infrastructure monitoring
AI DevOps implementation playbooks and runbooks
Individual learning certificates and competency assessments
30-day post-programme coaching and production deployment support
DevOps metrics improvement tracking framework

Best for

DevOps engineers, SREs, platform teams, and engineering managers responsible for CI/CD pipelines, infrastructure reliability, and code quality in technology companies

IS THIS RIGHT FOR YOU?

Finding the right fit

This is ideal for you if...

DevOps teams struggling with slow CI/CD pipelines (30+ minute test runs)
SRE teams managing complex infrastructure with frequent incidents and alert fatigue
Platform engineering teams seeking to improve code quality and reduce technical debt
Tech companies with existing CI/CD and monitoring infrastructure ready for AI enhancement

Consider another option if...

Teams without established CI/CD pipelines (recommend DevOps maturity assessment first)
Organizations with zero infrastructure monitoring (set up basic monitoring before AI)
Teams seeking general DevOps knowledge rather than AI-specific optimization

See yourself in the list above?

Let's Talk

CURRICULUM

What you'll learn

2 days total

Implement intelligent test selection, parallel build optimization, and AI-driven deployment risk assessment to accelerate release velocity.

What you'll be able to do

Build AI models predicting which tests to run based on code changes (reduce test runtime by 50-70%)
Optimize CI/CD parallelization using AI dependency analysis and resource allocation
Implement AI deployment risk scoring to predict rollback probability before production release
Create AI-powered canary deployment strategies with automated rollback triggers
Integrate AI CI/CD tools with existing pipelines (GitHub Actions, GitLab CI, Jenkins, CircleCI)