AI Infrastructure

Package an ML workload as a reproducible OCI image with pinned system, framework, and accelerator dependencies.

Define versioning contracts that link source data, transformations, features, labels, and trained models.

Select storage formats and partitioning strategies for high-throughput ML data access.

Implement schema, distribution, and data-quality checks at pipeline boundaries.

Design an orchestrated ML pipeline with explicit task dependencies, artifacts, retries, and idempotency.

Diagnose data-loading bottlenecks using compute utilization, I/O throughput, and queue-depth measurements.

Explain how accelerator compute, high-bandwidth memory, and interconnect characteristics constrain AI workloads.

Calculate accelerator memory consumption for model parameters, activations, optimizer state, and runtime overhead.

Select an accelerator type by benchmarking workload performance, availability, and cost.

Configure Kubernetes accelerator discovery, resource requests, and workload isolation.

Design node pools and scheduling policies using labels, taints, affinities, quotas, and priority.

Plan cluster capacity while accounting for workload bursts, accelerator fragmentation, and provisioning lead time.

Compare data, tensor, pipeline, and optimizer-state parallelism by memory use and communication pattern.

Calculate the communication volume and expected scaling efficiency of a data-parallel training job.

Choose network topology and collective-communication settings for a distributed training workload.

Configure a multi-node training job with deterministic rank assignment and coordinated startup.

Design checkpoint placement, frequency, and retention around recovery-time and storage constraints.

Diagnose distributed-training stragglers using accelerator, network, collective, and input-pipeline telemetry.

Design failure recovery for worker loss, node loss, and interrupted cluster capacity.

Record the code, configuration, data, environment, and metrics needed to reproduce an experiment.

Organize experiment metadata so that training runs can be compared and traced to resulting artifacts.

Build a train-evaluate-register pipeline with parameterized, reusable components.

Design a resource-efficient hyperparameter search using early stopping and concurrency limits.

Implement distributed data ingestion that preserves shuffling quality without starving accelerators.

Define automated acceptance gates for model quality, robustness, latency, and policy compliance.

Configure retraining triggers based on schedules, new data, drift signals, or performance degradation.

Define model-registry states and metadata for lineage, ownership, approval, and deployment eligibility.

Create unit, data-contract, model-behavior, integration, and infrastructure tests for an AI release.

Design a promotion workflow that separates artifact creation from environment-specific deployment.

Apply canary, blue-green, and shadow deployment patterns to model releases.

Implement GitOps reconciliation for declarative model-serving and platform configurations.

Verify model and container provenance using signing, attestations, and software bills of materials.

Design an automated rollback policy using service-health and model-quality signals.

Trace request admission, preprocessing, model execution, postprocessing, and response handling in an inference server.

Use queueing, batching, concurrency, and service-time measurements to analyze latency-throughput trade-offs.

Deploy a hardware-accelerated inference service on Kubernetes with explicit runtime and resource configuration.

Configure autoscaling from concurrency, queue depth, and latency signals rather than CPU utilization alone.

Design model loading, warm-up, caching, and eviction for single-model and multi-model serving.

Build a load test that identifies saturation, tail-latency growth, and maximum sustainable throughput.

Select a request-routing strategy for model versions, hardware pools, and tenant priorities.

Distinguish the compute and memory behavior of LLM prefill and decode phases.

Estimate model-weight and KV-cache memory for a specified model, precision, context length, and concurrency.

Evaluate quantization choices by memory savings, hardware support, latency, and quality impact.

Explain how continuous batching and paged KV-cache management improve serving utilization.

Choose among data, tensor, and pipeline parallelism for a large-model inference deployment.

Design admission control and request scheduling for mixed prompt lengths and service tiers.

Benchmark an LLM serving engine using latency, time-to-first-token, inter-token latency, throughput, and cost.

Assess speculative decoding for a workload using acceptance rate, added compute, and latency impact.

Define infrastructure, service, data, and model signals for an AI observability specification.

Instrument an inference path with correlated metrics, logs, and distributed traces.

Distinguish data drift, concept drift, training-serving skew, and ordinary service degradation.

Build an offline evaluation suite using representative datasets, task metrics, and regression thresholds.

Design online evaluation using sampled feedback, shadow traffic, experiments, or human review.

Create alerts that combine service-level objectives with model-quality and data-health signals.

Diagnose a production model regression by correlating release, input, resource, and output changes.

Perform a failure-mode analysis for an AI training or inference architecture.

Design high availability for gateways, schedulers, metadata services, storage, and model replicas.

Implement backpressure, admission control, load shedding, and graceful degradation for overloaded inference systems.

Define backup and disaster-recovery procedures for datasets, metadata, configuration, and model artifacts.

Design chaos experiments for node loss, accelerator failure, network impairment, and dependency outages.

Forecast capacity from demand growth, workload shape, utilization targets, and provisioning lead time.

Create an incident runbook and post-incident review template for AI-specific failures.

Create a threat model covering data pipelines, training jobs, model artifacts, serving endpoints, and platform control planes.

Design workload identity, least-privilege access, and tenant isolation for a shared AI platform.

Enforce data classification, encryption, retention, and deletion requirements across the model lifecycle.

Apply policy-as-code controls to workload admission, artifact promotion, and deployment.

Design audit records that connect data, code, training runs, approvals, models, and production predictions.

Mitigate model theft, artifact tampering, dependency compromise, and unauthorized endpoint use.

Allocate accelerator, storage, and serving costs to teams, models, and workloads using consistent attribution tags.

Define self-service platform interfaces for data preparation, training, evaluation, registration, and serving.

Design multi-tenant boundaries and resource-governance policies for an internal AI platform.

Evaluate build-versus-buy choices with portability, operational burden, integration, and cost criteria.

Produce a vendor-neutral reference architecture spanning data, training, registry, delivery, inference, and observability.

Evaluate the reference architecture against reliability, performance, security, governance, and cost requirements.

Create a benchmark plan that validates critical training and inference capacity assumptions.

Sequence a migration roadmap from isolated ML workloads to a shared production AI platform.