Machine learning consulting is the work of turning your data into models that run in production and pay for themselves: identifying the use cases worth building, engineering the data pipelines that feed them, developing and evaluating the models, and operating them reliably at a sane cost. BigData Boutique delivers all of it — as a project team or embedded alongside yours, in your cloud or on-premises, backed by 15+ years of data engineering.
Most ML projects don't fail at the model. They fail at the data — pipelines that can't feed training reliably, features that drift, deployments nobody can reproduce. That's why our machine learning consultants are data engineers first: we fix the foundations the models stand on.
What Our Machine Learning Consulting Services Include
ML Strategy & Use-Case Discovery
Not every problem needs machine learning, and not every ML idea survives contact with production. We score your candidate use cases on feasibility, measurable ROI, and time to production — and tell you plainly which ones aren't worth building yet. If you want this as a standalone engagement, start with an AI readiness assessment.
ML Pipelines on Your Data
We build the pipelines that make ML possible: data preprocessing, feature engineering, training workflows, and the indexing and retrieval layers models depend on — each step tailored to your data and stack, on AWS, GCP, Azure, or on-premises.
Model Development, Evaluation & Deployment
Our machine learning consultants design, train, and evaluate models against business metrics — not just accuracy scores — then deploy them behind APIs your product can actually use. Every engagement ships with an evaluation harness, so you know when a model regresses before your customers do.
MLOps & Cost Optimization
Already running ML in production? We review architecture, reliability, and spend. Right-sizing training jobs, tuning inference, and automating deployment routinely cut ML cloud costs substantially — and our MLOps consulting team builds the operational backbone that keeps models monitored, versioned, and retrainable.
Why Teams Choose Our Machine Learning Consultants
- Production first. Evaluation, monitoring, and cost controls are part of the build, not an afterthought. Models that can't be operated aren't deliverables.
- Your data, your cloud. Everything runs in your environment. As an AWS Advanced Tier Services Partner with the AI Services Competency, we fast-track ML workloads on AWS — including GenAI on Bedrock.
- Data engineering roots. A decade-plus of Elasticsearch, OpenSearch, Spark, and ClickHouse work means the data layer — where ML projects are won or lost — is our home turf.
- Fixed-path delivery. Our AI Launchpad program takes a use case from idea to production in weeks, with clear milestones instead of open-ended research.
Frequently Asked Questions
What does a machine learning consultant do?
A machine learning consultant identifies where ML can deliver measurable business value, designs and builds the models and data pipelines to capture it, and gets the result running reliably in production. In practice most of the work is data engineering — preparing, moving, and serving data — with model development built on that foundation.
How much does machine learning consulting cost?
It depends on scope: a focused assessment is a short fixed-price engagement, while building and deploying a production ML system is a project priced by team size and duration. We scope every engagement up front with clear milestones — contact us for an estimate on your use case.
Do I need machine learning consulting or AI consulting?
Machine learning consulting covers predictive models trained on your data — forecasting, classification, recommendations, anomaly detection. If your use case involves large language models, RAG, or AI agents, you want our AI consulting services or LLM development services. Not sure? An AI readiness assessment answers that question in two weeks.
Do you work with our existing team and stack?
Yes — most engagements are alongside an in-house team, on your existing cloud and tooling. We transfer ownership as we go: your engineers should be able to retrain, redeploy, and extend everything we build without us.
Machine learning rarely stands alone. For generative AI, AI agents, and RAG systems, see our AI consulting services and LLM development services, or jump straight to building with our generative AI development services.