MLOps and AI Operations

Our MLOps services build the pipelines, deployment, and monitoring that turn a working model into a system your business can actually depend on every day.

Production Reality

The Gap Between a Model That Works and a System That Runs

A model that scores well in testing is maybe 30% of the job. The rest is everything that has to exist around it: somewhere it can run reliably under real load, data pipelines that feed it fresh clean inputs, and monitoring that notices when accuracy slips.

This is the work that decides whether AI becomes part of your operations or stays a project someone did once.

1Somewhere it can run reliably, under real load

Scalable hosting, failover, and responsive inference APIs under production traffic.

2Data pipelines that feed it fresh, clean inputs

3Monitoring that notices when accuracy slips

4A way to retrain and redeploy without breaking what's live

5Versioning, so you can roll back instantly

6Cost controls before the monthly bill arrives

Somewhere it can run reliably, under real load

Our Services

Our MLOps Services

01

MLOps Consulting and Assessment

We review how your models are built, deployed, and monitored today, then give you a plan with the gaps ranked by risk. You get an honest assessment even if you don't continue with us.

02

Model Deployment

Models served as reliable APIs or batch jobs, with autoscaling, versioning, and the ability to roll back instantly when something goes wrong.

03

Pipeline Automation

Automated pipelines for data preparation, training, evaluation, and deployment. Retraining becomes a routine that runs, not a task someone remembers to do.

04

Monitoring and Drift Detection

Tracking for accuracy, data drift, latency, and cost on live traffic. Alerts fire when performance slips, before your users are the ones who notice.

05

LLMOps

Operations built specifically for LLM systems: prompt versioning, evaluation sets, output quality tracking, token cost monitoring, caching, and safe migration when a provider deprecates a model.

06

Feature Stores and Data Infrastructure

Consistent feature definitions between training and production, so the model sees the same shape of data in both. This is the source of a surprising number of accuracy problems.

07

CI/CD for Machine Learning

Automated testing and deployment for models, the same discipline your software already has. Every model change gets evaluated before it reaches production.

08

Cost Optimization

Right-sizing infrastructure and models, caching repeated calls, batching where it helps, and routing simple requests to cheaper models. Most systems have real savings in them.

Architectural Comparison

MLOps or LLMOps?

Both put AI into production, but the failure modes are different.

Aspect

MLOps

LLMOps

01What you run

Models you trained

Models you call or fine-tuned

02Main risks

Data drift, stale training data

Output quality, cost, model deprecation

03You fix it by

Retraining on fresh data

Tuning prompts, retrieval, and evaluation sets

04Biggest cost

Compute for training

Tokens per request

05What to monitor

Accuracy, drift, latency

Output quality, hallucination rate, cost per call

Which does your system need?

Most businesses now run both. We cover both, and we'll tell you which one your system actually needs rather than selling you the larger package.

Eligibility & Scope

Who This Is For

Models unready for production

Deployment

You have models that work in testing but haven't reached production.

Untracked live accuracy

Monitoring

Models are live but nobody knows if they're still accurate or drifting.

Uncontrolled climbing AI costs

Cost Control

Your AI costs are climbing and you can't see which part is responsible.

Single-person manual retraining

Automation

Retraining is a manual job that only one person knows how to do.

Scaling beyond one model

Scalability

You're scaling from one model to several and the infrastructure is getting fragile.

Our Methodology

How We Work

01

1. Assess

We review your models, data pipelines, infrastructure, and deployment process. You get a written assessment with the gaps ranked by risk and effort.

02

2. Prioritize

We recommend where to start, usually whatever is most likely to break or cost the most. You see the reasoning.

03

3. Build

We build the pipelines, deployment, and monitoring, working inside your existing cloud and tooling rather than introducing a new platform.

04

4. Hand over

Documentation and a walkthrough so your team can run it. If you'd rather we ran it, our support plans cover that.

Why SolutionsLoft

Why Choose SolutionsLoft for MLOps

We build what you'll actually maintain

A pipeline your team can't operate is worse than the manual process it replaced.

Your stack, not ours

We work inside your cloud and tooling instead of pushing a preferred platform.

Cost is part of the design

We measure and control spend from day one, not after the first surprising invoice.

Full-stack under one roof

The same team handles the models, the infrastructure, and the applications around them.

Documentation and handover included

You should be able to run it without us.

Infrastructure & Tooling

Technologies We Work With

We build on whatever your team already runs. Adding a new platform to an already fragile setup rarely helps.

Airflow

Apache Spark

Frequently Asked Questions

Let's Build Something Worth Launching

Have an idea, product, or challenge? Let's bring it to life fast, smart, and beautifully executed.

MLOps services cover everything needed to run machine learning models in production: deployment infrastructure, automated pipelines for training and retraining, monitoring for accuracy and drift, versioning and rollback, and cost control. It's the engineering around the model rather than the model itself.

MLOps applies to models you train yourself, where the main risk is accuracy drifting as your data changes. LLMOps applies to systems built on large language models, where the risks are output quality, token cost, and providers deprecating models. Most businesses now need both.

Possibly not a full platform, but you still need deployment, monitoring, and a way to retrain. The right amount of MLOps scales with how much the business depends on the model. We'll tell you what's actually needed rather than building infrastructure for a scale you don't have.

We monitor the statistical properties of incoming data against what the model was trained on, and track accuracy on live traffic where ground truth is available. When either moves beyond an agreed threshold, an alert fires and retraining can be triggered.

Yes, and we prefer to. We build inside your current cloud and tooling wherever possible. Adding a new platform to an existing setup usually creates more problems than it solves.

It depends on how many models you run, the state of your current setup, and whether you need a build or an assessment. An assessment is a fixed price and gives you a plan before you commit to anything larger.

Usually, yes. Common savings come from right-sizing compute, caching repeated calls, batching, and routing simple requests to smaller models. We measure before and after so the savings are provable.

You do. It runs in your cloud accounts, uses open standards, and comes with documentation. There's no proprietary platform you'd have to keep paying us for.

Ready to Kick Off Your Project? Let's Get Started Together!

We'll schedule a call to discuss your idea. After discovery sessions, we'll send a proposal, and upon approval, we'll get started.

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Is Your Model Actually in Production?

Tell us what you've built and how it runs today. On a free call, we'll tell you what's missing between where you are and a system your business can rely on.

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