InnomiumInsights
Evidence for leaders building AI that has to work.
Practical guidance on AI strategy, agents, computer vision, software, data, cloud, GPU systems, evaluation, and the work between pilot and production.
All insights
10 insights matching filters
Data & Cloud
Data Engineering for Production AI: Build Inputs the Team Can Trust
Production AI needs governed data products with contracts, lineage, quality, access, freshness, replay, and owners—not one-off preparation notebooks.
Innomium Data EngineeringJuly 23, 20265 min readData & Cloud
LLM Model Routing: Control Cost Without Hiding Quality Regressions
Route tasks by evidence, capability, risk, latency, and cost—with fallback behavior and evaluations that prevent silent degradation.
Innomium AI EngineeringJuly 22, 20266 min readData & Cloud
Cloud Architecture for AI Workloads: Separate Experiments, Platforms, and Products
Design identity, networking, data, compute, deployment, observability, cost, and recovery around distinct AI workload classes.
Innomium Cloud EngineeringJuly 22, 20265 min readData & Cloud
AI Platform Observability with OpenTelemetry: Trace the Complete Workflow
Connect application, retrieval, model, tool, data, and infrastructure signals using vendor-neutral telemetry and outcome-oriented service indicators.
Innomium Platform EngineeringJuly 22, 20265 min readData & Cloud
AI Inference Cost Optimization: Measure Latency, Throughput, and Quality Together
Optimize model choice, context, batching, caching, quantization, routing, hardware, and concurrency without concealing quality loss.
Innomium ComputeJuly 22, 20265 min readData & Cloud
Kubernetes GPU Workloads in Production: Scheduling Is Only the Beginning
Plan drivers, device plugins, node pools, images, storage, topology, quotas, telemetry, upgrades, isolation, and failure recovery.
Innomium Platform EngineeringJuly 22, 20265 min readData & Cloud
GPU Cloud Cost Planning: Price the Useful Result, Not the Hour
Model GPU economics across utilization, queue time, data movement, engineering labor, failed runs, serving latency, and workload completion.
Innomium ComputeJuly 22, 20265 min readData & Cloud
MLOps vs. LLMOps: Keep the Proven Discipline, Extend the Behavior Model
LLM applications add prompts, retrieval, tools, graders, provider dependencies, and human review—but still need versioning, deployment, monitoring, and ownership.
Innomium Platform EngineeringJuly 22, 20265 min readData & Cloud
Vector Database Selection for Enterprise AI: Start with the Retrieval Workload
Compare retrieval quality, filters, updates, deletion, scale, operations, portability, and cost before choosing a vector store.
Innomium Data EngineeringJuly 22, 20265 min readData & Cloud
AI-Ready Data Platform Architecture: Capabilities Before Products
Design an AI data foundation around governed ingestion, reusable data products, evaluation, unstructured content, access, lineage, and observability.
Innomium Data EngineeringJuly 22, 20265 min read
Want the methods applied to your system?
Talk with Innomium about evaluation, adaptation, or a production engineering program.