MLOps Consulting Services
MLOps consulting from engineers who run models and LLM applications in production. BigData Boutique helps teams automate the machine learning lifecycle — training pipelines, deployment, monitoring, and evaluation — so models ship faster and stay reliable.
What Our MLOps Consulting Covers
ML Pipeline Automation
Reproducible training and data pipelines with experiment tracking, feature stores, and CI/CD for models — replacing notebooks-in-production with engineering discipline
Model Deployment & Serving
Scalable, cost-efficient model serving on AWS SageMaker, Bedrock, Kubernetes, or serverless — with autoscaling, A/B rollout, and rollback strategies built in
Monitoring & Evaluation
Drift detection, model quality dashboards, and LLM evaluation frameworks — so you know when models degrade before your users do
LLMOps for GenAI
MLOps for generative AI: prompt versioning, evaluation suites, guardrails, token cost management, and observability for RAG and agentic applications
Data Infrastructure for ML
The data engineering that MLOps depends on: streaming ingestion, lakehouse storage, and vector search — built by our data engineering consulting team
Cost Optimization
GPU utilization, right-sized inference, spot strategies, and caching — cutting ML infrastructure spend without sacrificing model quality or latency
Why Choose BigData Boutique for MLOps Consulting?
Data Engineering Roots
MLOps is mostly data engineering. Our team has 15+ years building production data platforms — pipelines, streaming, and analytics — which is exactly the foundation reliable ML operations are built on.
GenAI-Ready
We run MLOps for classic ML and for LLM applications alike — including evaluation, guardrails, and cost control for RAG pipelines and AI agents in production.
Frequently Asked Questions
What MLOps consulting services does BigData Boutique offer?
Our MLOps consulting services cover the full ML lifecycle: training pipeline automation, model deployment and serving, monitoring and drift detection, LLM evaluation and observability, and the underlying data infrastructure. We work hands-on with your team, from architecture review to implementation.
Which MLOps tools and platforms do you work with?
AWS SageMaker and Bedrock, Kubernetes and Kubeflow, MLflow, Airflow, dbt, feature stores, and modern LLMOps tooling for evaluation and observability. We are vendor-neutral and recommend the stack that fits your team and budget.
Do you cover MLOps for generative AI and LLM applications?
Yes. LLMOps is a core part of our practice: prompt and context versioning, evaluation suites, guardrails, token cost management, and production observability for RAG and agentic applications.
Can you work with our existing ML team?
That is our default mode. We embed with your data scientists and engineers, set up the MLOps foundations together, and hand off with documentation and knowledge transfer so your team owns the platform confidently.
Related Resources: AI Consulting Services · Machine Learning Consulting · Data Engineering Consulting · AI Agent Development
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