MLOps services: senior AI pods that run models in production

Ryz provides MLOps through dedicated AI pod teams of senior engineers who work in your cloud and repos, alongside your team. The pod builds the training pipelines, model registry, deployment paths, monitoring and eval gates that take models from notebooks to production and keep them working there, for classic ML and LLM systems alike. Every engineer comes from the top 1% of the tens of thousands we interview, on US business hours.

What we build

Most models that never reach production are stuck on engineering, not data science: no reproducible training, no safe deployment path and no way to know when a model goes bad. Typical deliverables:

• Reproducible training pipelines. Orchestrated jobs that pull versioned data, train, evaluate and register a model, so any production model can be rebuilt from its inputs.
• Model registry and lineage. A registry that records the code commit, dataset version, parameters and metrics behind each model, with stage transitions your team approves.
• Feature pipelines. Shared feature definitions for training and serving, using a feature store where it pays off, to prevent training-serving skew.
• Serving infrastructure. Batch scoring, real-time endpoints and streaming inference on SageMaker, Azure ML, Databricks or Kubernetes, with autoscaling and latency targets.
• Safe deployment. Shadow deployments, canary releases and champion-challenger comparisons, with automatic rollback when metrics drop.
• Monitoring. Data quality checks, feature and prediction drift, delayed-label performance tracking and alerts routed to the team that owns the model.
• LLMOps. Prompt and model versioning, eval suites that gate releases in CI, tracing of LLM calls, and cost and latency dashboards per feature.
• Platform templates. Starter repos and CI/CD templates so your data scientists ship new models through the same path without filing tickets.

How an engagement works

1. Talk. We review the models you run or plan to run, how they are trained and deployed today, who owns them and where releases get stuck.
2. Match. We propose a pod scoped to your stack: typically a tech lead, MLOps and platform engineers, a data engineer and an ML engineer who has been on the receiving end of bad tooling, with names and a price.
3. Join. The pod works in your repos, CI and standups, with weekly demos of models moving through the new path.
4. Grow. You onboard more models and teams to the platform, or your platform team takes it over with templates and runbooks.

Week 1 is discovery and one model: tracing how a representative model gets from data to production today and picking it as the first to move. Month 1 usually brings that model on a reproducible pipeline with a registry, CI and a monitored deployment. Month 3 is a platform others use: templates, drift monitoring, eval gates for LLM features and several models running through the same path. Pace depends on scope, cloud access and your onboarding.

The stack our teams work in

How we keep models reliable after launch

A model usually fails quietly. The endpoint returns 200s while predictions get worse. Our pods build for these failure modes:

• Training-serving skew. A feature computed one way in a notebook and another way in production produces garbage without errors. Shared feature code, a feature store where needed and tests comparing offline and online values prevent it.
• Data drift and broken upstream data. A source system changes a unit or starts sending nulls. Data validation runs before training and scoring, and drift metrics on inputs and predictions alert before business metrics move.
• Concept drift with delayed labels. In fraud or credit, true outcomes arrive weeks later. We track proxy metrics in the meantime and backfill performance when labels land.
• Unreproducible models. If nobody can rebuild the production model, nobody can safely change it. Every registered model carries its commit, data version, environment and metrics.
• Risky releases. New models go out in shadow or canary first, compared against the current champion on live traffic, with automatic rollback thresholds.
• LLM regressions. A provider model update or prompt edit can change outputs overnight. We pin model versions, run eval suites in CI and on a schedule, and trace production calls so failures become new test cases.
• Runaway cost. GPU endpoints left idle and LLM calls with bloated context add up. Dashboards show cost per model and per feature, with autoscaling and budgets.
• Governance gaps. Regulated teams need to know which model made a prediction and how it was approved. The registry, approvals and lineage give your risk team that record, and our engineers have experience working within model risk and data protection standards.

This is the kind of production discipline our pods bring to client systems. A Ryz pod built AI fraud detection for a global fleet company that scores each item in under 30 seconds and has surfaced $5.94M in confirmed fraud, validated by the client's fraud team. See the case studies.

Team shapes and cost

Typical Ryz cost is $7,000 to $15,000 per engineer per month. Mid-level engineers run $7,000 to $10,000, seniors $10,000 to $15,000 and leads $15,000+, quoted per team.

• Foundation pod: tech lead + 2 senior MLOps engineers. $15,000+ plus $20,000 to $30,000 is roughly $35,000 to $45,000+ per month. Good for moving the first models onto a reproducible, monitored path.
• Platform pod: tech lead + 3 senior engineers + 1 mid-level engineer. $15,000+ plus $30,000 to $45,000 plus $7,000 to $10,000 is roughly $52,000 to $70,000+ per month. Good for a shared ML platform with templates used by several teams.
• One or two senior MLOps engineers on your team. $10,000 to $30,000 per month, when your platform team owns the roadmap.

Project cost is team size × duration × monthly rate. A foundation pod at about $40,000 per month for four months is roughly $160,000. Quotes are scoped per team, and you get a plan, a price and the names of the people before you start.

Dedicated team or staff augmentation?

Choose an AI pod team when you want an ML platform or a production path for specific models built and shipped by one group. Choose staff augmentation when your platform team owns the design and needs senior MLOps engineers or Databricks engineers working on your team.

When Ryz isn't the right fit

If you want a proprietary end-to-end ML platform product with vendor support, buy one and configure it. If you want hourly freelance help or a trial before talking to anyone, use a self-serve marketplace. If you need follow-the-sun on-call coverage across European and Asian hours, a global network fits better.

Related

• AI development services
• Machine learning development
• DevOps services
• Data engineering services
• Hire LLM evaluation engineers
• AWS Bedrock vs Azure OpenAI

FAQ

How much do MLOps services cost?

Typical Ryz cost is $7,000 to $15,000 per engineer per month. A foundation pod of a lead and two senior MLOps engineers is roughly $35,000 to $45,000+ per month. Total cost is team size × duration × monthly rate, and you get a scoped plan, price and names before you start.

How fast can MLOps work start?

After the scoping call we propose a team. Most of the timeline depends on scope and your onboarding, especially access to cloud accounts, data and existing pipelines.

What is the difference between MLOps and LLMOps?

MLOps covers training, deploying and monitoring models you build. LLMOps adds what matters when you call hosted LLMs: prompt and model versioning, eval suites, tracing and token cost. Most teams now need both, on one platform.

Do we need a feature store?

Not always. It pays off when several models share features or real-time serving needs the same values as training. For a few batch models, shared feature code and tests are often enough.

Which ML platform should we use?

Usually the one closest to your data and team: SageMaker on AWS, Azure Machine Learning on Azure, or Databricks if your data already lives there. Our pods build on your existing platform rather than adding another.

Questions we didn't answer? Email info@ryzlabs.com.