Best MLOps
Consulting Services
Deploy, scale, and monitor ML and LLM models with confidence. CrunchOps delivers expert MLOps consulting, from ML pipelines to model serving.
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Fair prices
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Expert work
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Proven Track Record
Our MLOps Consulting Services
Expert-Led MLOps Solutions for Model Deployment, Monitoring & Scaling
ML Pipeline Design & Automation
Turn one-off notebook experiments into repeatable, production-grade pipelines. We design automated ML workflows that cover training, validation, and continuous retraining (CI/CD/CT) so your models stay accurate and your team stops shipping by hand.
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Building end-to-end pipelines with tools like Kubeflow Pipelines and Argo Workflows
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Automating data prep, training, evaluation, and deployment steps
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Adding continuous training (CT) so models retrain on fresh data
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Version-controlling data, code, and models for consistency
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Setting quality gates so only validated models get promoted
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Standardizing pipeline templates your data scientists can reuse
Model Deployment & Serving
Getting a model to production is where most teams get stuck. We deploy your models as scalable, secure services, real-time or batch, with autoscaling, rollout safety, and the performance your workloads demand.
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Deploying models with KServe, Seldon Core, or managed endpoints
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Supporting real-time, batch, and streaming inference patterns
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Enabling autoscaling (including scale-to-zero) to match traffic
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Rolling out safely with canary and blue-green strategies
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Optimizing latency and throughput with Triton, vLLM, or ONNX Runtime
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Serving models on Kubernetes across AWS
Model Monitoring, Drift Detection & Observability
A model that was accurate last week can silently decay today. We set up monitoring that watches for data drift, performance drops, and pipeline failures, so you catch problems before your customers do
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Tracking model accuracy, drift, and data quality in production
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Setting up drift and outlier detection with tools like Evidently AI
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Building dashboards with Prometheus and Grafana for real-time visibility
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Alerting your team the moment metrics move out of range
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Triggering automated retraining when performance degrades
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Giving you a clear audit trail of model behavior over time
LLMOps & Generative AI Infrastructure
Running LLMs and GenAI in production brings a new set of challenges, GPUs, vector databases, and inference cost. We help you build and scale the infrastructure behind RAG systems, fine-tuned models, and AI agents.
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Deploying RAG pipelines and vector databases for production use
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Optimizing GPU utilization on Kubernetes
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Reducing inference cost with batching, quantization, and efficient serving
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Setting up scalable serving for open and fine-tuned LLMs
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Adding evaluation, guardrails, and monitoring for GenAI outputs
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Managing GPU infrastructure across cloud and on-prem environments
ML Governance, Security & Reproducibility
For teams in regulated industries, the model works isn't enough, you need to prove it. We build governance into your ML lifecycle so every model is secure, and audit-ready.
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Tracking everything from raw data to deployed model
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Ensuring every model can be reproduced on demand
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Controlling access to data, features, and model endpoints
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Managing secrets, credentials, and model artifacts securely
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Aligning ML workflows with compliance and audit requirements
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Documenting model decisions for internal and external review
Why Choose CrunchOps for MLOps Consulting Services?
We help companies move machine learning out of notebooks and into dependable production systems. From the first pipeline to long-term scaling, we build MLOps that fits your team, your data, and your cloud, not a one-size-fits-all template. With deep, hands-on experience across Kubernetes, GPUs, and modern ML tooling, we've helped teams deploy models they can actually trust in production.
We build the tech systems that run AI every day handling the heavy hardware, the data setups, and the launch tools. Because we know how to connect the AI with the right technology, you get fewer unexpected problems and can launch your product much faster.
Deep ML Infrastructure Experience
We don't stop at a working demo. Our focus is models that stay accurate, scale under load, and deliver measurable business value.
Results-Focused, Production-First
From training to deployment to retraining, we automate the ML lifecycle so your team spends time on models, not manual work.
Automation at the Core
From day one, we make sure your ML workloads is automatically tracked, checked, and easy to recreate. This makes your systems reliable, safe, and ready for any official review.
Governance & Reproducibility Built-In
We hand over more than a system. Through training and documentation, we make sure your team can run and grow the platform on their own.
Empowerment Through Enablement
FAQs
Questions? We're glad you asked
Here's a little more about how we operate. Got a more specific question? Feel free to get in touch.
An MLOps consultant helps you take machine learning models from experiments to reliable production systems, designing pipelines, automating deployment, setting up monitoring, and putting governance in place so your models stay accurate and trustworthy.
We combine real infrastructure expertise (Kubernetes, GPUs, cloud) with ML workflow knowledge, and we keep it practical. Our focus is production outcomes and a business-first mindset, not theory.
Yes. We help teams deploy and scale LLMs, RAG systems, and AI agents, including GPU optimization, vector databases, inference cost control, and monitoring for GenAI workloads.
We work across the modern MLOps stack which includes Kubeflow, MLflow, KServe, Feast, Evidently AI, and more, on Kubernetes, and AWS. We recommend tools based on your needs, not a fixed template.
Absolutely. We work alongside your team, handle knowledge transfer, and make sure everyone is aligned so the platform is easy to own after we're done.
Yes. Through right-sizing, GPU sharing, autoscaling, and efficient serving, we help you cut waste and keep ML and GenAI infrastructure costs predictable.
We work with teams across SaaS, fintech, healthcare, eCommerce, education, and startups, whether you're deploying your first model or scaling ML across the organization.
Trusted by Teams Worldwide
Let’s make ML work for your business.
Deploy faster, keep your models reliable, and scale with confidence, with CrunchOps as your MLOps partner.
