[ models that survive production ]

MLOps services: MLOps consulting, model deployment and monitoring for production ML

Describe what is stuck in plain language: a model that works in a notebook but has never shipped, a model already live that nobody is watching, or a training process that runs by hand every quarter. Botgigs matches you to a vetted US MLOps engineer who has kept real models running, so your work reaches production and stays accurate after launch.

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the short answer

MLOps services take a machine learning model from a notebook to production and keep it reliable once it is there: deployment pipelines, versioning, monitoring, drift detection, automated retraining and rollback. They matter because the commonly cited industry figure is that over 85 percent of machine learning projects never reach production, and of the ones that do, fewer than 40 percent still deliver value a year later. In the US in 2026, deploying a first model properly runs about $15,000 to $45,000, adding monitoring and retraining runs $25,000 to $70,000, and a shared MLOps platform runs $90,000 to $300,000+. Last updated July 2026.

01 / what it covers

What MLOps services actually cover

MLOps applies DevOps discipline to machine learning, then adds the parts DevOps never had to handle: data that changes, models that decay silently, and results that have to be reproducible months later. Two different decay patterns hide behind that word, and the monitoring you need differs for each: model drift vs data drift explains which alert catches which. Demand for MLOps engineers rose more than 35 percent year over year into 2026 for exactly this reason. Botgigs vets for the specific work below.

[ deployment ]

Model deployment and serving

Packaging a model as a versioned service with a real rollout path: staging gates, canary or shadow releases, autoscaling inference, and a rollback that takes seconds rather than a weekend.

[ pipelines ]

Training and CI/CD pipelines

Turning a manual notebook workflow into an automated, repeatable pipeline: data validation, feature engineering, training, evaluation and promotion, so the same inputs always produce the same model.

[ registry ]

Model registry and versioning

One place that records every model version with its metrics, training data and code commit, so you can answer which model scored a given customer in March and prove it.

[ monitoring ]

Monitoring and drift detection

Watching prediction quality, input distributions and latency in production, with alerts that fire when data drift or accuracy decay starts instead of when a customer complains.

[ retraining ]

Automated retraining loops

A pipeline that assembles fresh training data, retrains, compares the candidate against the incumbent on held-out data, and only promotes it if it actually wins.

[ governance ]

Reproducibility and governance

Lineage, audit trails, access control and documented approval gates, which is table stakes if you operate in a regulated industry and have to explain a decision after the fact.

02 / mlops vs devops

MLOps vs DevOps: what actually changes

MLOps does not replace DevOps, it sits on top of it. The reason it needs its own practice is in the first row: normal software fails loudly, and a model fails quietly.

Dimension	DevOps	MLOps
Failure mode	Loud. The build breaks or the service returns errors.	Silent. The model keeps responding while accuracy decays.
What is versioned	Code and configuration.	Code, data, features, model weights and hyperparameters.
Definition of tested	Unit and integration tests pass.	Tests pass and the model beats the incumbent on held-out data.
Trigger to redeploy	A developer merges a change.	A developer merges, or drift crosses a threshold, or data refreshes.
What monitoring watches	Uptime, errors, latency, saturation.	All of that, plus input distributions, prediction mix and accuracy.

If your stack is mostly language models called through an API rather than models you train yourself, the operational problems shift toward prompts, retrieval quality and evaluation. That work is covered on LLM development and RAG development services.

03 / what it costs

What MLOps services cost in 2026

These are effort bands derived from published US contract rates and typical scope durations, not quotes. The biggest swing factor is what already exists: deploying into a team that has CI/CD and a warehouse is a fraction of the work of deploying into a team where the model lives on a data scientist's laptop.

Scope	Timeline	Typical US cost	Best for
First model to production	3 to 6 weeks	$15,000 to $45,000	A working model that has never shipped
Monitoring and retraining loop	4 to 8 weeks	$25,000 to $70,000	Models already live that nobody is watching
Shared MLOps platform	3 to 6 months	$90,000 to $300,000+	Several models and teams, governance requirements
Managed MLOps (ongoing)	Monthly	$6,000 to $25,000 per month	Keeping it running after handover

US MLOps rates in 2026

Useful for sanity-checking any proposal you receive. Published ranges vary widely because MLOps covers everything from wiring up a deployment pipeline to GPU orchestration for distributed training.

Engagement	US rate, 2026	Note
Full-time MLOps engineer	About $130,600 per year	Roughly $63 per hour before benefits and overhead
Contract MLOps engineer	$85 to $130 per hour	The usual band for shipping a deployment and monitoring stack
Senior MLOps consultant	$150 to $250 per hour	Architecture, platform design, regulated environments
Scarce specialist	$275 to $450 per hour	GPU orchestration, distributed training, inference optimization

Rates published as of July 2026, re-check before you budget. If the model itself does not exist yet, start with hiring a machine learning engineer; if the blocker is upstream data rather than deployment, start with AI data engineering services.

04 / why models stall

Why machine learning models never reach production

The commonly cited figure is that more than 85 percent of machine learning projects fail to reach production. Almost none of those failures are modeling failures. Four patterns account for most of them, and all four are operational.

01

The model only runs on one laptop

Training depends on a local file, a hand-edited config and a package set nobody wrote down. Reproducing it takes a week, so it never gets promoted. An automated training pipeline and a model registry fix this permanently.

02

Nobody owns it after handover

The data scientist moves to the next project and the model becomes an orphan. Without alerting and a named owner, decay goes unnoticed until a business metric moves and somebody goes looking.

03

Production data does not match training data

The model was trained on a clean export and production sends nulls, new categories and a schema that changed last quarter. This is the single most common launch failure, and it is a data engineering problem before it is a model problem.

04

No path from a good result to a deployed one

There is no staging environment, no approval gate and no rollback, so shipping feels dangerous and gets deferred. Teams end up with a great offline metric and nothing in front of a customer. It is the same pattern that stalls AI POCs.

05 / how it works

From a notebook to a monitored production model

step_01

Describe what is stuck

Where the model lives today, what it predicts, how often it needs to change, and what your stack already has. The AI turns that into scope: deployment approach, monitoring plan and the MLOps skills to screen for.

step_02

Get scope and matches

A vetted US MLOps engineer who has shipped and operated models on a stack like yours. No proposal spam, no bidding war, no agency retainer on top of their rate.

step_03

Ship, monitor, hand over

Agree milestones from the brief, get the model deployed behind proper gates with monitoring and retraining wired in, and take handover with runbooks so your team can operate it.

06 / questions

MLOps questions, answered

What is MLOps in simple terms?

MLOps is the practice of getting a machine learning model out of a notebook and into production, then keeping it working. It covers automated deployment, versioning, monitoring, drift detection and retraining. The short version: DevOps keeps a deployment running, MLOps keeps a model behaving correctly as the world it predicts on changes underneath it.

What is MLOps and how is it different from DevOps?

MLOps builds on top of DevOps rather than replacing it. DevOps automates building, testing and deploying code, which behaves deterministically: it either works or it fails. MLOps adds the machine learning lifecycle on top, because a model can keep running perfectly while quietly getting less accurate as real-world data shifts. That is why MLOps adds data versioning, model registries, evaluation gates, drift monitoring and retraining triggers.

How much do MLOps consulting services cost?

In the US in 2026, getting a first model deployed properly runs roughly $15,000 to $45,000 over 3 to 6 weeks. Adding monitoring, drift detection and a retraining loop runs $25,000 to $70,000. A shared MLOps platform for several teams runs $90,000 to $300,000 or more. Ongoing managed MLOps typically runs $6,000 to $25,000 per month. These are effort bands derived from US contract rates, not quotes.

What is MLOps used for?

MLOps is used to make machine learning reliable in production. Concretely: shipping a model as a versioned, rollback-able service, catching accuracy decay before customers do, retraining on a schedule or a trigger instead of by hand, reproducing exactly which data and code produced a given model, and passing audits in regulated industries. It is the difference between a model that demos and a model that earns money for years.

What is an MLOps pipeline?

An MLOps pipeline is the automated path from data to a deployed model and back again. A typical one ingests and validates data, engineers features, trains and evaluates candidate models, registers the winner with its metrics and lineage, promotes it through staging gates, deploys it behind a canary or shadow test, then monitors it and feeds drift signals back into a retraining trigger. The point is that it runs the same way every time without a human running notebook cells.

What is the difference between MLOps and LLMOps?

MLOps manages models you train and own, so the core problems are training pipelines, feature stores and statistical drift. LLMOps manages systems built on models you usually rent through an API, so the core problems shift to prompt and version management, retrieval quality, evaluation of open-ended text, token cost, latency and guardrails. Most 2026 teams need both, because a typical stack has classic models scoring transactions and an LLM layer handling language.

Do we need MLOps if we only have one or two models?

You need a smaller version of it, not the full platform. With one or two models the honest minimum is version control for the training code and data, a documented way to rebuild the model, monitoring on inputs and outputs, and a rollback plan. That is a few weeks of work, not a platform program. Building a shared MLOps platform before you have several models in production is a common way to spend six figures on infrastructure nobody uses yet.

Should we hire an MLOps engineer or use a managed service?

Use managed tooling for the undifferentiated parts, such as the registry, orchestration and serving, and hire an engineer to design how they fit your stack and your risk tolerance. Managed platforms remove infrastructure work but they do not decide your promotion gates, your drift thresholds or your retraining policy. Those choices are specific to your data and they are what determines whether the model still works next year.