Case studies
Real infrastructure problems, documented properly.
- AI Infrastructure
- Platform Engineering
- Kubernetes
- MLOps
- SRE
- AIOps
- Cloud Modernization
- FinOps
- DevOps
Shared GPU platform for training and inference
A typical starting point: several ML teams each run their own GPU instances. Some sit idle while others wait for capacity, inference shares nodes with training, and nobody can see who is using what.
Golden paths for a growing engineering organization
A common pattern: as headcount grows, each team creates services differently. New services take a long time to reach production, and a small infrastructure team becomes a ticket queue.
Production readiness for a multi-tenant cluster estate
A typical scenario: clusters grew organically, upgrades are feared, configuration differs between clusters and tenants can affect each other.
Automated model delivery from registry to production
A frequent situation: models are trained in notebooks and handed over manually. Deployments are slow, training is hard to reproduce, and no one notices when a model degrades.
Introducing SLOs and burn-rate alerting
A common starting point: hundreds of alerts, frequent pages for non-issues, and no shared definition of 'reliable enough' between product and engineering.
Event correlation and noise reduction for a distributed platform
A typical situation: dozens of services each emit their own alerts, so one underlying failure produces a flood of pages across teams. Engineers spend the first part of every incident manually correlating symptoms.
Cost allocation across shared cloud infrastructure
A typical scenario: the cloud bill grows, but shared clusters and accounts make it impossible to tell which team or product drives the cost.
Structure
How every case study is written.
Each one follows the same engineering-first structure so you can see the reasoning, not just the result.
- 01
Challenge
- 02
Architecture
- 03
Approach
- 04
Technology
- 05
Implementation
- 06
Outcome
- 07
Lessons Learned
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