[{"data":1,"prerenderedAt":202},["ShallowReactive",2],{"svc-detail-mlops-ai-infrastructure":3,"svc-work-mlops-ai-infrastructure":147,"svc-blog-mlops-ai-infrastructure":185,"ill-services\u002Fmlops-ai-infrastructure":201},{"slug":4,"short":5,"title":6,"tagline":7,"answer":8,"summary":9,"icon":10,"subServices":11,"useCases":60,"benefits":76,"process":82,"tools":95,"industries":111,"faq":117,"relatedWork":136,"relatedServices":139,"seo":144},"mlops-ai-infrastructure","MLOps & AI Infrastructure","MLOps & AI Infrastructure services","Deploy, monitor, scale and secure AI systems, so they keep working after launch.","MLOps and AI infrastructure services take models and LLM applications from a notebook or prototype to reliable production: deployment, GPU infrastructure, CI\u002FCD for models and prompts, monitoring and observability, inference optimisation, Kubernetes, cost control, security and governance. For engineering and data teams that need AI systems to be as dependable as the rest of their platform.","Model deployment, GPU infrastructure, AI observability, CI\u002FCD for models and prompts, Kubernetes for AI, inference and cloud-cost optimisation, AI security and governance: the operations layer under every AI system we ship.","gear",[12,16,20,24,28,32,36,40,44,48,52,56],{"slug":13,"name":14,"text":15},"model-deployment","Model deployment","Serve ML and LLM models as scalable APIs or batch jobs with versioning, rollbacks and health checks.",{"slug":17,"name":18,"text":19},"gpu-infrastructure","GPU infrastructure","Design and run GPU clusters on cloud or on-prem, right-sized, scheduled and cost-tracked.",{"slug":21,"name":22,"text":23},"ai-monitoring-observability","AI monitoring & observability","Tracing, quality metrics, drift, latency and cost dashboards for models, prompts and agents.",{"slug":25,"name":26,"text":27},"ai-scaling","Scaling AI systems","Autoscaling, queues, caching and load management for spiky and high-volume AI workloads.",{"slug":29,"name":30,"text":31},"ai-ci-cd","AI CI\u002FCD","Pipelines that test, evaluate and promote models and prompts the way you ship code.",{"slug":33,"name":34,"text":35},"kubernetes-for-ai","Kubernetes for AI","GPU-aware Kubernetes platforms with serving frameworks, autoscaling and multi-tenancy.",{"slug":37,"name":38,"text":39},"cloud-cost-optimisation","AI cloud cost optimisation","Cut spend with spot GPUs, right-sizing, batching, caching and token-usage governance.",{"slug":41,"name":42,"text":43},"inference-optimisation","Inference optimisation","Quantisation, compilation, batching and caching to lower latency and cost per request.",{"slug":45,"name":46,"text":47},"ai-security","AI security","Prompt-injection defences, secrets, network isolation, data-leak prevention and red-teaming.",{"slug":49,"name":50,"text":51},"ai-governance","AI governance","Model registries, approvals, audit trails, documentation and policy controls for regulated environments.",{"slug":53,"name":54,"text":55},"feature-stores-data-versioning","Feature stores & data versioning","Reproducible features and datasets so training and serving agree and experiments can be rerun.",{"slug":57,"name":58,"text":59},"llm-observability","LLM & agent observability","Trace every prompt, tool call and token; evaluate outputs continuously; alert on regressions.",[61,64,67,70,73],{"title":62,"text":63},"From notebook to production","Package, deploy and monitor a model your data team built so it serves real traffic reliably.",{"title":65,"text":66},"Keep LLM apps healthy","See every prompt, cost and failure; catch quality regressions before users do.",{"title":68,"text":69},"Control GPU and token spend","Cut infrastructure and API costs without hurting quality or latency.",{"title":71,"text":72},"Pass security and compliance review","Governance, audit trails and controls that satisfy internal and regulatory scrutiny.",{"title":74,"text":75},"Ship models like software","CI\u002FCD that evaluates and promotes models and prompts automatically.",[77,78,79,80,81],"AI systems that stay accurate, fast and available","Lower infrastructure and API costs","Faster, safer releases of models and prompts","Security and governance designed in","Clear ownership and runbooks for your team",[83,86,89,92],{"step":84,"text":85},"Platform assessment","Review current models, pipelines, infrastructure, costs and risks; identify what breaks first at scale.",{"step":87,"text":88},"Target architecture","Serving, observability, CI\u002FCD, security and cost controls designed for your cloud and team.",{"step":90,"text":91},"Implementation","Build the platform incrementally, migrating existing models and apps with zero-downtime cutovers.",{"step":93,"text":94},"Runbooks and handover","Dashboards, alerts, documentation and training so your engineers operate it confidently.",[96,97,98,99,100,101,102,103,104,105,106,107,108,109,110],"Kubernetes","Docker","MLflow","Kubeflow","vLLM","Triton Inference Server","LangSmith","Langfuse","Prometheus","Grafana","Terraform","GitHub Actions","AWS SageMaker","Vertex AI","Azure ML",[112,113,114,115,116],"saas-technology","finance","healthcare","manufacturing","logistics",[118,121,124,127,130,133],{"q":119,"a":120},"We have a model in a notebook. How do we get it live?","We package it with its dependencies and preprocessing, wrap it in a versioned API or batch job, add tests and monitoring, and deploy it through a pipeline so the next version ships the same way. Most single-model deployments take two to four weeks including monitoring.",{"q":122,"a":123},"What should we monitor for an LLM application?","Latency, error rates and cost per request as with any service, plus AI-specific signals: output quality against an evaluation set, hallucination and refusal rates, prompt-injection attempts, drift in inputs, and per-tenant token usage. Traces of prompts and tool calls make debugging possible.",{"q":125,"a":126},"How much can we cut our AI cloud costs?","It varies, but common wins are large: caching repeated prompts, routing simple requests to smaller models, batching offline work, spot or reserved GPUs and right-sizing instances. We baseline spend first and report savings against it rather than promising a percentage.",{"q":128,"a":129},"Do you work with our existing cloud and tooling?","Yes. We build on AWS, Azure and GCP with the tools you already run (Kubernetes, Terraform, GitHub Actions, MLflow, SageMaker, Vertex) and only introduce new components when there is a clear gap. Where you have no platform yet, we recommend a small, standard stack (Kubernetes or managed serving, MLflow, an observability tool) that your team can hire for.",{"q":131,"a":132},"How do you secure AI systems against prompt injection?","Least-privilege tools, input and output filtering, separating instructions from untrusted content, human approval for sensitive actions, and regular red-teaming. No single control is sufficient, so we layer them and log everything. We test defences with realistic attack prompts as part of release, and re-test whenever tools or models change, since new capabilities open new paths.",{"q":134,"a":135},"What does AI governance involve in practice?","A registry of models and prompts with owners and versions, documented evaluations, approval steps for production changes, audit logs of decisions and data use, and policies for acceptable use. We keep it lightweight enough that teams actually follow it. The registry doubles as documentation, so when an auditor or customer asks how a model was built and tested, the answer already exists.",[137,138],"stock-market-deep-learning","enterprise-rag-knowledge-base",[140,141,142,143],"open-source-ai","ai-integration","data-analytics-predictive-ai","ai-fine-tuning",{"title":145,"description":146},"MLOps & AI Infrastructure Services | AIYatch","MLOps and AI infrastructure: model deployment, GPU infrastructure, AI observability, CI\u002FCD, Kubernetes for AI, cost optimisation, security and governance.",[148,167],{"path":149,"title":150,"description":151,"industry":152,"services":153,"results":155,"cover":165,"coverAlt":166},"\u002Fwork\u002Fmanufacturing-defect-detection","Computer-vision defect detection on a packaging line","Camera-based inspection that catches print, seal and label defects in real time on a high-speed packaging line, with results fed to the plant's quality system.","Manufacturing",[154,4,142],"computer-vision",[156,159,162],{"value":157,"label":158},"~96%","defect recall on the held-out test set",{"value":160,"label":161},"-60%","defective units reaching final inspection",{"value":163,"label":164},"\u003C40 ms","inference per frame at line speed","\u002Fcovers\u002Fwork\u002Fmanufacturing-defect-detection.svg","Abstract navy cover with a rising cyan course line",{"path":168,"title":169,"description":170,"industry":171,"services":172,"results":174,"cover":184,"coverAlt":166},"\u002Fwork\u002Fon-prem-llm-regulated-insurer","On-premises open-source LLM platform for a regulated insurer","A private LLM stack (open-weight models, GPU serving, guardrails and observability) deployed inside the client's data centre so no policy data leaves their network.","Finance & Fintech",[140,4,173],"rag-systems",[175,178,181],{"value":176,"label":177},"0","documents or prompts leaving the network",{"value":179,"label":180},"~1,200","internal users onboarded in the first quarter",{"value":182,"label":183},"~70%","lower cost per 1M tokens vs. the API quote","\u002Fcovers\u002Fwork\u002Fon-prem-llm-regulated-insurer.svg",[186,191,196],{"path":187,"title":188,"cover":189,"coverAlt":190},"\u002Fblog\u002Fmeasuring-ai-roi-honestly","Measuring AI ROI honestly: a method that survives the finance review","\u002Fcovers\u002Fblog\u002Fmeasuring-ai-roi-honestly.svg","Line-art illustration of a ruler measuring a stack of coins beside a rising bar.",{"path":192,"title":193,"cover":194,"coverAlt":195},"\u002Fblog\u002Fmlops-basics-first-model","MLOps basics for teams shipping their first model","\u002Fcovers\u002Fblog\u002Fmlops-basics-first-model.svg","Line-art illustration of a looping pipeline with gauges, from data to model to monitoring and back.",{"path":197,"title":198,"cover":199,"coverAlt":200},"\u002Fblog\u002Fopen-source-llms-on-prem","Open-source LLMs on-prem: when private AI actually makes 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