MLOps Consulting Services
If your models are trained by one person and deployed once, we make them rerunnable, monitored, and kept in production. That means training pipelines anyone on the team can rerun, a registry that says which version is live, deployment that can roll back, and drift monitoring against thresholds you agreed. Our MLOps consulting services start with a two-to-three-week assessment of how a model gets from a notebook to production in your company today. They end with a pipeline your team runs, or we run for you. Our open-source AML training pipeline is public, so you can read the training step before you call.
MLOps Consulting Services That End in a Running Pipeline
MLOps (machine learning operations) is the set of practices that take a model from an experiment to a system someone can retrain, redeploy, and watch. Our MLOps consulting services answer four questions about each model you run. How is it trained, how does it reach production, how do you know it still works, and who can change it? The assessment maps the answers for the models you have today, and the engineering closes the gaps.
Two pieces of our work are public. Our open-source AML training pipeline (Apache 2.0) shows the training step. One command trains random forest, XGBoost, and LightGBM models, selects the best by ROC-AUC, and saves it with its metrics. The next run’s numbers can be compared with the last. In Environmental.AI, a language-model feature we built for production, each generated statement shows its source. The platform drafts environmental assessment reports from a knowledge base of more than 4,000 verified documents and runs on AWS.
The first pipeline, for one model or one model family (models that share training code and data), is planned at four to eight weeks. The assessment before it produces:
- A lifecycle map for each model in scope: data sources, training code, deployment path, owner, and what happens when the data drifts (stops looking like the training set).
- The gap list ordered by risk, with the first pipeline chosen.
- The monitoring plan: which signals, which thresholds, who is alerted, and what triggers a retrain.
- A build estimate for the first pipeline and a tooling recommendation that fits what you already run.
Tell us the one model you depend on to start the assessment.
This page covers the pipeline underneath the AI work on our other pages. Model routing and cost optimization are on the AI platform development hub, and CI/CD, infrastructure, and application monitoring are under DevOps services.
The evaluation design for AI agents and AI chatbots lives there, predictive models are under healthcare data analytics, and building the model itself is machine learning development.
MLOps Services We Deliver
An MLOps engagement is made of up to six kinds of work. Each leaves your team with code in your repository, a dashboard, and a runbook (how to retrain, roll back, and respond to each alert). The assessment says which of the six your models need first.
01
Training and Retraining Pipelines
Training reruns from a commit, with the data version, feature code, and metrics stored with each run. Our open-source AML pipeline shows the training step: three model types, the best by ROC-AUC, one command to retrain. We add data versioning and the trigger.
01
Training reruns from a commit, with the data version, feature code, and metrics stored with each run. Our open-source AML pipeline shows the training step: three model types, the best by ROC-AUC, one command to retrain. We add data versioning and the trigger.
02
Model Deployment and Serving
Batch scoring, a real-time API, or on-device inference goes out as canary (a share of traffic) or shadow (a copy of traffic users never see) before full rollout. Rollback redeploys the previous registered version, and the log shows which version served before.
02
Batch scoring, a real-time API, or on-device inference goes out as canary (a share of traffic) or shadow (a copy of traffic users never see) before full rollout. Rollback redeploys the previous registered version, and the log shows which version served before.
03
Model Monitoring and Drift Detection
We watch input and prediction drift, and performance once labels (the true outcomes) arrive, each with a threshold and an alert route. A retrain is triggered by the signal and approved by a person. Model and business metrics share one dashboard.
03
We watch input and prediction drift, and performance once labels (the true outcomes) arrive, each with a threshold and an alert route. A retrain is triggered by the signal and approved by a person. Model and business metrics share one dashboard.
04
Experiment Tracking and Model Registry
Each run is logged with its parameters, data version, and metrics, and each model is versioned with its approval status. We set this up in MLflow or your cloud's registry, so the registry records which model is live and what it was trained on.
04
Each run is logged with its parameters, data version, and metrics, and each model is versioned with its approval status. We set this up in MLflow or your cloud's registry, so the registry records which model is live and what it was trained on.
05
LLMOps: Evaluation, Traces, and Cost
Language-model features get the same lifecycle with different metrics. An evaluation set is re-run on each change, each request leaves a trace (prompt, context, model, answer, cost), and cost per task is tracked. The set exists to catch a bad prompt change early.
05
Language-model features get the same lifecycle with different metrics. An evaluation set is re-run on each change, each request leaves a trace (prompt, context, model, answer, cost), and cost per task is tracked. The set exists to catch a bad prompt change early.
06
Model Governance for Regulated Industries
The registry holds a model inventory with owners, a model card per version (a one-page record of data, metrics, and limits), validation evidence, and an audit log. Governance evidence is a report run from the registry, and your model-risk function keeps the policy.
06
The registry holds a model inventory with owners, a model card per version (a one-page record of data, metrics, and limits), validation evidence, and an audit log. Governance evidence is a report run from the registry, and your model-risk function keeps the policy.
How an MLOps Engagement Runs
MLOps Assessment
For each model in scope we trace the path from data to prediction. We record where the training data lives, who ran the last training and how, how the model reached production, what is monitored, and who is paged when it fails. We need read-only access to the training code, the serving setup (how the model is hosted and called), and the dashboards. No training data is copied out, and nothing is changed in production. Where the training code is gone, the assessment documents the serving artifact (the model file in production) and its inputs. The first pipeline then rebuilds training from the data that is still available. The output is a lifecycle map and a gap list ordered by risk, and it names the first pipeline to build. The assessment team is an ML engineer and a DevOps engineer.
Pipeline Engineering
We rebuild training as a pipeline. Data is pulled at a recorded version (a dated copy of the table or a tagged dataset), and features are computed by code in the repository. Training is triggered by a commit or a schedule. Metrics and the trained model file are stored with the run. The CI job (GitHub Actions, GitLab CI, or Jenkins) runs on machines inside your network (self-hosted runners) or in your cloud's ML service, so data stays in your cloud account. The data scientist keeps the notebook for exploration, and a change to the model is a pull request against the training code.
Deployment Patterns
Releases go out as canary or shadow before full rollout. Rollback redeploys the previous registered version when the input schema is unchanged. A schema change is rolled back together with the feature pipeline. On-premises and on-device inference get the same release path: the pipeline builds the model package, and your team deploys it where the data lives.
Monitoring and Retraining Triggers
Monitoring has three layers: input drift (the data no longer looks like the training set), prediction drift (the outputs shift), and performance once labels arrive. Each has a threshold and an alert route agreed at the assessment. Thresholds are set from a baseline window (a period of normal live data), so the threshold marks the edge of the data's usual range. A retrain is triggered by a signal or a schedule and goes through the same pipeline and approval as the first release. Dashboards in Grafana, Datadog, or your cloud's monitoring service show model metrics next to the business metrics.
LLM Operations
Prompts and model choice are versioned, an evaluation set of tasks taken from production is re-run on each change, each request leaves a trace, and cost per task is tracked. Drift shows up as new kinds of requests the evaluation set does not cover, and the set grows from them. Routing between models is configured by task, and the endpoint can change without changing the application code. This is the operations layer we specify for the agents and chatbots we build.
Governance, Security, and Handover
The registry doubles as the model inventory: owner, data version, metrics, approval, and the model card for each version. Access to data, training, and deployment is role-based, and each deployment is logged. The pipeline, the registry, and the serving endpoint run in your cloud account or on your own infrastructure. We stay on through the first retraining cycle, and then your team or ours runs the pipeline. The pipeline code, configuration, dashboards, and runbook written for you are yours, and our open-source AML pipeline stays under its Apache 2.0 license.
For each model in scope we trace the path from data to prediction. We record where the training data lives, who ran the last training and how, how the model reached production, what is monitored, and who is paged when it fails. We need read-only access to the training code, the serving setup (how the model is hosted and called), and the dashboards. No training data is copied out, and nothing is changed in production. Where the training code is gone, the assessment documents the serving artifact (the model file in production) and its inputs. The first pipeline then rebuilds training from the data that is still available. The output is a lifecycle map and a gap list ordered by risk, and it names the first pipeline to build. The assessment team is an ML engineer and a DevOps engineer.
Our Case Studies
Hola Salud
Hola Salud
Digital healthcare platform for Mexico.
We built a complete healthcare ecosystem, including a patient app, a doctor’s portal, and an admin backend, with self-registration, appointment booking, medical records, and integrated Stripe and Oxxo payments.
Environmental AI
Environmental AI
Environmental compliance automation.
We built a RAG-based system that automates environmental assessment documents and regulatory templates, cutting preparation time by up to 90%.
Revvel
Revvel
AI-powered women’s health platform.
We developed an AI platform that turns elite health experts into digital twins and reviews years of patient data in under 60 seconds, delivering risk assessments up to 10x faster and cutting time-to-care from days to minutes.
InnerPeak.AI
InnerPeak.AI
Student mental wellness platform.
We developed a comprehensive web and mobile application that provides students and teachers with 24/7 personalized support and engaging resilience training to build essential life skills.
Hello, We Hire
Hello, We Hire
AI-driven recruitment automation platform.
We built an AI-powered recruitment system with automated pre-screening, real-time skills evaluation, and multi-stage verification that speeds up hiring by 94%.
Hola Salud
Hola Salud
Digital healthcare platform for Mexico.
We built a complete healthcare ecosystem, including a patient app, a doctor’s portal, and an admin backend, with self-registration, appointment booking, medical records, and integrated Stripe and Oxxo payments.
Environmental AI
Environmental AI
Environmental compliance automation.
We built a RAG-based system that automates environmental assessment documents and regulatory templates, cutting preparation time by up to 90%.
Revvel
Revvel
AI-powered women’s health platform.
We developed an AI platform that turns elite health experts into digital twins and reviews years of patient data in under 60 seconds, delivering risk assessments up to 10x faster and cutting time-to-care from days to minutes.
InnerPeak.AI
InnerPeak.AI
Student mental wellness platform.
We developed a comprehensive web and mobile application that provides students and teachers with 24/7 personalized support and engaging resilience training to build essential life skills.
Hello, We Hire
Hello, We Hire
AI-driven recruitment automation platform.
We built an AI-powered recruitment system with automated pre-screening, real-time skills evaluation, and multi-stage verification that speeds up hiring by 94%.
Flexible Developer Hiring Model
Your priorities define the way we work together — from how much control you want to the level of flexibility your project demands.
Team Augmentation
Quickly scale your workforce with skilled developers who integrate seamlessly into your in-house team, filling talent gaps and boosting productivity.
Read more
Dedicated Development Team
A whole, cross-functional team working exclusively on your project, providing end-to-end development, management, and long-term support.
Read more
Software Development Outsourcing
Delegate your entire project to our specialists and focus on business growth, while we handle development, delivery, and quality.
Read more
Why Plus8Soft?
01
Experience
Multiplied by AI
Multiplied by AI
We blend deep engineering expertise with cutting-edge AI acceleration. By integrating intelligent tools into our workflow, we don't just write code—we engineer solutions faster and with higher precision.
02
Business-First
Transparency
Transparency
We look beyond the ticket. Our team operates with hyper-transparency, treating your budget and goals as our own. We align technical decisions with your business strategy to create real, measurable value.
03
Committed to
Overdelivery
Overdelivery
Meeting requirements is our baseline; exceeding them is our culture. Whether it's optimizing performance, refining UX, or anticipating future scalability, we consistently go the extra mile.
MLOps Consulting FAQ
What is the difference between MLOps consulting and MLOps engineering, and which do we need?
Consulting is the assessment; engineering is the pipeline. The assessment maps how each model is trained, deployed, and monitored today and hands you a gap list ordered by risk. We do not score you against a maturity level, and the gap list is the output. Engineering builds the first pipeline from that list. With one data scientist and one model, the assessment is short and most of the budget goes to the pipeline. With a platform team and twenty models, most of the value is in the assessment, and your team may build from it.
How long does an MLOps implementation take with Plus8Soft?
The assessment takes two to three weeks. The first pipeline is planned at four to eight weeks after that. It covers one model or one model family (models that share training code and data, such as a fraud model per region). The figures are planning estimates, confirmed in the assessment. On them, the first model runs on the pipeline six to eleven weeks from kickoff. Each further model on the same pipeline should take less time, because the data versioning, registry, and monitoring are already there.
How much do MLOps consulting services cost?
The assessment is two engineers (an ML engineer and a DevOps engineer) for two to three weeks, quoted as a fixed fee before it starts. The number of models in scope drives it. The first pipeline is priced from the assessment. Cloud costs for training and serving, the monitoring and registry services, and label collection are paid to your providers, and the monitoring tracks them per model.
Which MLOps tools do you use, and do we have to change ours?
We build on what you run. Experiment tracking and the registry run in MLflow or your cloud's ML service. Pipelines run in your CI system or your cloud's ML service. Serving runs in containers on Kubernetes if you already run it, and otherwise on a simpler container service. Monitoring runs in Grafana, Datadog, or your cloud's monitoring service. We recommend a change only where the current tool is the gap, and the assessment says why.
Can you work in our cloud without data leaving it?
Yes. Pipelines, registry, and serving deploy in your cloud account, and training data stays in it. Our engineers work through role-based access your team grants, and each deployment is logged. Nothing is copied to our machines. For language-model features on a hosted model, prompts go only to endpoints whose enterprise terms exclude training on your inputs. For on-premises or on-device inference, the pipeline builds the model package and your team deploys it where the data lives.
How do you know when a model needs retraining, and what if labels arrive late?
From the three signals under Monitoring and Retraining Triggers, each with a threshold agreed at the assessment. A signal triggers a retrain, which goes through the pipeline and the usual approval. For models whose labels arrive months later or never, drift on the inputs and predictions carries the alert, and a schedule covers the retrain. When a label batch lands, performance is scored against it after the fact.
Is LLMOps different from MLOps, and do you offer it?
Yes, with different metrics. Versioning covers the prompt and the model choice. Evaluation is a set of tasks taken from production, re-run on each change, and monitoring is the trace per request and cost per task. Drift shows up as new kinds of requests the evaluation set does not cover. The evaluation design is on the agent and chatbot pages. This page covers the operations layer.
What do we need to have in place before an MLOps assessment?
One model that matters and the people who trained and deployed it. Read access to the training code, if it still exists, and to the serving system is arranged in the first week. You do not need a platform team or a tooling decision. Those come out of the assessment if they are needed.
How does MLOps help with model risk and audit requirements?
The registry holds the records an auditor asks for. They cover the model's owner, the data and code behind each version, how it was validated, when it was approved, and what has been monitored since. SR 26-2 (banks) and ONC HTI-1 (decision-support features in certified health IT) ask for these records on predictive models, and the pipeline assembles them as it runs. We supply the evidence, and your model-risk or compliance function owns the validation and the policy.
Who runs the pipelines after the engagement?
Your team or ours. We hand over to the engineers who will run them and stay on through the first retraining cycle. The cycle ends when the first retrain, triggered by a signal or the schedule, has gone through the pipeline and approval. During that cycle alerts go to your on-call and to us. Under an operations agreement, they go to us first. In both cases the code, dashboards, and runbook are yours.
How is this different from your DevOps services?
DevOps services cover CI/CD, infrastructure, and application monitoring, and an MLOps pipeline runs on top of them. What this page adds is the data version in each run, the model registry, the drift signals, and the retrain approval. If you already have CI/CD and monitoring, the engagement reuses them and adds the model layer.
Our cloud already has a managed ML pipeline service. Why hire you?
The service gives you the runtime, and the assessment adds the lifecycle map, the gap list, the thresholds, and the approval path. The first pipeline is built by engineers whose job is the plumbing, which leaves yours on the model. If the managed service already covers your gaps, the assessment says so and ends there.
