Teams with great models often have brittle deployments. We've audited dozens of in-house ML platforms and the gaps repeat: no model registry (which version is in production?), no eval CI (regressions ship silently), no drift detection (the model rots and no one notices), no rollback path. NKKTech builds MLOps platforms with the same patterns used by AI-native companies — but right-sized for your scale, not Google's.
End-to-end MLOps platforms, not a Kubeflow installation. Six capabilities every engagement includes.
Versioned training pipelines (Metaflow, Kubeflow Pipelines, or AWS SageMaker Pipelines). Experiment tracking (MLflow or W&B). Reproducibility from data to weights.
Single source of truth for which model is in production where. MLflow Model Registry or SageMaker Model Registry. Promotion workflow (staging → production) with approvals.
Eval framework in CI. Every PR that touches model code or data runs the eval suite. Deploys blocked on regression. Same engineering discipline as software CI.
Input-distribution drift, prediction drift, label-feedback monitoring where ground truth is available. Alerts wired to PagerDuty or Slack. Dashboards in Grafana or Datadog.
Feast or Tecton for shared feature definitions between training and serving. Eliminates train/serve skew. Real-time + batch features unified.
OpenTelemetry spans for every prediction request: input, output, model version, latency, downstream consumers. Replay any production prediction in 5 minutes.
1–2 weeks. Walk your current ML deployment, identify the highest-risk gaps, prioritize remediation.
3–5 weeks. Set up model registry, eval framework, CI gates. The minimum viable MLOps platform.
3–5 weeks. Drift detection, performance monitoring, feature store rollout for top-priority models.
2–3 weeks. Migrate existing models to the new platform, train your team, hand off runbooks.
Multi-model ensembles, real-time scoring, regulatory model documentation (SR 11-7), drift monitoring for credit risk and AML.
Batch scoring pipelines, A/B harness for model variants, feature store shared with product engineering.
Online + offline eval harness, sequential model deployment with shadow traffic, feature pipelines for collaborative filtering and ranking.
HIPAA-compliant deployment, FDA-pathway-aware documentation, prediction-explanation tooling for clinician review.
Regulatory model documentation, fairness monitoring, prediction-explanation requirements.
Real-time scoring at high QPS, multi-objective optimization, online learning patterns.
The signal isn't team size; it's number-of-models-in-production. Once you have 3+ models running, you need a registry, CI, and monitoring or you'll ship regressions silently. Most teams underinvest in MLOps until something breaks publicly.
Usually no. Most clients have invested in MLflow, W&B, or a cloud-native stack. We integrate and harden rather than replace. About 70% of engagements are augmentation of existing platforms.
Same playbook, with extra steps. Quantization-aware deployment (vLLM, TGI, Triton), prompt-versioning as code, A/B harness on prompt+model combinations. LLMOps is MLOps with a few new failure modes.
USD 60K–150K for a typical 10–14 week engagement. Ongoing tooling cost varies widely (open-source MLflow on existing infra: ~$200/month; managed Tecton + Datadog: $5K–$20K/month at mid-market scale).
Yes. We typically operate as augmentation — senior engineers embedded with your team, transferring patterns and playbooks. Plan for 6–12 months of ongoing collaboration if you want full enablement.
NKKTech delivered our LLM document processing pipeline on time and exactly on budget. The tech lead was available on Slack daily. First offshore team that actually worked the way we expected.
Tony's team understood our legacy PHP system faster than our internal team. Zero downtime migration, exactly as promised. The bilingual PM made communication seamless.
We went from 15 hours/week of manual prospecting to fully automated lead gen in 8 weeks. ROI in 60 days as Tony promised.
NKKTech delivered our LLM document processing pipeline on time and exactly on budget. The tech lead was available on Slack daily. First offshore team that actually worked the way we expected.
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30-minute free discovery call with a senior NKKTech engineer (not a sales rep). We'll review your requirements, scope an engagement, and tell you honestly whether we're the right fit.
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