MLOps consulting services for production machine learning
Senior nearshore ML engineers who build the pipelines, monitoring, and governance that keep your models running reliably after launch.
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15+years delivering software
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100+senior engineers across LATAM
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6-8hdaily overlap with US teams
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6+Fortune 500 & global brands trust us
The engineering layer between a model and production
Most models fail after deployment: drift creeps in, pipelines break, and nobody notices until a metric slips.
FusionHit builds the MLOps layer that prevents it, deployment pipelines, monitoring, retraining, and governance, run by senior nearshore engineers in your time zone.
Our MLOps services
Engage at any point in your MLOps maturity, from a first audit to fully managed operations.
MLOps audit
A focused maturity assessment of your pipelines, deployment, and monitoring, with a prioritized roadmap.
MLOps platform implementation
Set up MLflow, Kubeflow, SageMaker, or Vertex AI as one operational backbone for your team.
ML pipeline development
Build the training, deployment, and CI/CD pipelines that get models to production repeatably.
Model monitoring
Drift, data quality, and performance monitoring wired into your alerting, so problems surface early.
MLOps managed services
We run your pipelines, retraining, and incident response on a monthly retainer with a named team.
LLMOps
Prompt versioning, evaluation harnesses, and inference-cost control for language models in production.
What we build into your ML platform
The operational disciplines that turn a trained model into a system you can trust.
Model deployment
Package and serve models over REST or gRPC, scaled up for demand and down for cost.
Drift detection
Catch data and concept drift before it reaches your users or your reporting.
Experiment tracking
Compare runs, reproduce results, and unify experimentation across teams.
Model registry
Version, stage, and promote models with a clear path from experiment to production.
Automated retraining
Trigger retraining on schedule or on drift, with validation gates before anything ships.
Model lineage
Trace any deployed model back to the data and code it came from, for audits and debugging.
Infrastructure as code
Terraform, Kubernetes, and GitOps, so your ML platform is reproducible like any other system.
Governance and compliance
Audit trails, sign-off workflows, and the evidence regulated teams need (SOC 2, HIPAA, GDPR).
Why teams run MLOps with us
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Senior ML engineers
Engineers who have operated machine learning in production, across cloud platforms and regulated environments.
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Platform-agnostic
We fit the platform you already use, or recommend the one that suits your scale and cloud.
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Nearshore economics
Senior MLOps capacity at 30-50% below US rates, on a retainer you can size to your fleet.
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Real-time collaboration
Nearshore engineers on US hours, live in your standups, reviews, and on-call rotation.
Models we keep running
Embedding AI Across the Full Engineering Lifecycle
Challenge A product engineering team was managing growing codebases with increasing complexity around quality control, documentation, and cloud infrastructure. Code reviews…
AI-Augmented Development and Operations in a Multi-Team Enterprise
Challenge Multiple engineering teams working across Supply Chain and Support Services were operating in silos, each dealing independently with challenges around…
Architecture Documentation Reverse Engineering
Challenge Two core services had evolved organically, and their architecture had never been formally documented. The team was preparing for an…
The MLOps stack we work with
Platform-agnostic by design, we fit your stack or recommend the right one.
MLOps platforms
- • MLflow
- • Kubeflow
- • SageMaker
- • Vertex AI
- • Seldon
- • Databricks
Cloud
- • AWS
- • Azure
- • Google Cloud
- • Kubernetes
- • On-prem
Data
- • Snowflake
- • BigQuery
- • Postgres
- • Kafka
- • Airflow
- • dbt
Monitoring
- • Evidently
- • Weights & Biases
- • Prometheus
- • Grafana
MLOps for regulated and data-heavy industries
Where model reliability and audit trails carry real consequences.
Fintech
Model governance and audit evidence for risk, credit, and fraud models.
Healthcare
HIPAA-ready pipelines and lineage for clinical and operational models.
Insurance
Reproducible pricing and claims models with sign-off workflows.
Retail
Demand and recommendation models retrained as your data shifts.
Logistics
Forecasting and routing models monitored across live operations.
SaaS
In-product models deployed and observed alongside your releases.
Engage an MLOps team the way that fits
Add senior ML engineers to your team, embed a dedicated team, or hand us the full build. Pick the model that fits how you work today.
Add ML engineers to your team
Senior nearshore ML engineers join your existing team, under your direction, in days, not months.
Embed a dedicated MLOps team
A stable, cohesive team that works only on your platform as an extension of yours. You set priorities and roadmap, the team self-organizes sprint after sprint.
Hand us the full build
Hand us the roadmap and we deliver end to end, under our direction: audit, platform, pipelines, and monitoring. You get an outcome, not a team to manage.
How an MLOps engagement runs
FAQs about our MLOps services
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What is the difference between MLOps consulting and MLOps development services?
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How much do MLOps services cost?
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Which MLOps platform should we use?
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Do you work with our existing platform?
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Can you fix models that degrade after deployment?
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What does an MLOps audit include?
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Do you offer managed MLOps?
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Do you handle LLMOps for language models?
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Can you meet our compliance requirements?
Ready to get your models into production, and keep them there?
Tell us where your ML pipeline breaks, and we'll show you the team that can fix it.
Trusted by leading teams
Rated by our clients
