Client Overview

A large AI/ML company focused on developing cutting-edge machine learning models for various applications.

Challenge

1. Slow Model Lifecycles

• 10-day median "dev-to-prod" time per model
• Engineers spent 40+ hours manually editing Helm charts per release

2. $750K/Month Cloud GPU Waste

• On-prem GPU utilization languished at 41%
• Teams defaulted to cloud bursts to avoid job queues

3. Unpredictable Performance

• Recommendation API breached 300ms SLO 28% of the time (peak p95: 430ms)
• No auto-scaling for traffic spikes

4. Siloed Troubleshooting

• 2.1-hour MTTR for model incidents
• Feature drift, infra metrics, and logs lived in separate systems

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Solution

1. Unified Model Serving

• KServe for canary/blue-green rollouts
• Knative Eventing enabled zero-to-scale concurrency

2. Intelligent GPU Orchestration

• Kueue + Slurm scheduler routed jobs by:
  • Cost (on-prem first)
  • SLA (cloud burst for latency-sensitive models)
• Vertical/Horizontal Pod Autoscaling tuned per model type

3. GitOps Automation

• Replaced Helm with Kustomize overlays
• GitHub Actions pipelines:
  • Built container images
  • Generated SBOMs
  • Triggered ArgoCD syncs

4. Observability Fabric

• OpenTelemetry traced full request lifecycle
• Grafana dashboards correlated:
  • Model metrics (feature drift, accuracy)
  • Infra metrics (GPU utilization, latency)
  • Business metrics (conversion rates)

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Implementation

As a smaller firm we had to get creative in order to implement this solution. We executed this transformation through focused, iterative phases designed to deliver quick wins while building toward the complete solution. We began with a streamlined 2-week discovery period, using automated tools to analyze logs and metrics rather than manual audits. For our pilot, we selected just 5 high-impact models (representing 20% of total prediction volume) and implemented the core KServe/Knative solution in 3 weeks - enough to prove the concept without overextending our team.

The global rollout was conducted in manageable waves over 10 weeks, prioritizing models by business criticality. We automated as much of the migration as possible through custom scripts that converted Helm charts to Kustomize configurations. Rather than attempting to train all 200 engineers upfront, we created self-service documentation and trained a core group of 10 "MLOps champions" who then trained others.

Key adaptations for our small team:
- Used managed services wherever possible (e.g., GitHub Actions instead of self-hosted CI/CD)
- Focused on the 20% of features that would deliver 80% of the value
- Scheduled rollouts during low-traffic periods to minimize need for 24/7 support
- Partnered with the client's IT team to handle basic operational tasks

Our weekly "show and tell" demos with stakeholders ensured alignment while minimizing meeting overhead. The entire implementation was completed in 4 months with no additional hires, proving that small teams can deliver enterprise-scale transformations through smart automation and phased execution.

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Results & Impact

Velocity

• 98% faster deployments: 10 days → 4 hours
• 80% less engineer toil: 40 → 8 hours/model

Efficiency

• 45% cloud cost reduction: 750K→750K→410K/month
• 36pp higher on-prem utilization: 41% → 77%

Reliability

• 56% lower latency: 430ms → 190ms p95
• 25pp fewer SLO breaches: 28% → <3%

Operational Clarity

• 80% faster incident resolution: 2.1h → 25min MTTR
• Unified dashboards eliminated 7+ troubleshooting tools

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This MLOps transformation was a game-changer. We went from 10-day manual deployments to 4-hour automated rollouts while cutting our cloud GPU costs by $340K monthly. The team's lean approach proved small firms can deliver enterprise-grade AI infrastructure. - VP of AI Engineering

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Key Takeaways

1. Kubernetes Native > Custom Tooling

KServe's built-in canary testing and Knative scaling reduced rollout risk without maintaining proprietary MLOps platforms.

2. GPU Efficiency = Cost Control

Kueue's quota system and Slurm integration turned $340K/month in cloud waste into productive on-prem capacity.

3. GitOps is Non-Negotiable for AI

Kustomize + ArgoCD eliminated:

• 100% of Helm chart drift incidents
• 90% of "works on my machine" deployment failures

4. Observability Must Span the Stack

Correlating model accuracy (W&B), infra metrics (Prometheus), and business KPIs reduced debugging hops by 70%.

Strategic Impact:
This transformation proved Kubernetes can deliver:
✔ Enterprise-grade AI serving
✔ Predictable cloud costs
✔ Real-time performance at 120M-user scale

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