What does CI/CD for machine learning include?

Automated model testing, validation, deployment, and versioning, so every release is repeatable, faster, and more reliable.

Can you build MLOps from scratch?

Yes. We design the framework, select tools and architecture, and implement pipelines, monitoring, validation, and infrastructure tailored to your environment.

Do you integrate with our existing tools?

Yes. We work with your current platforms and pipelines rather than forcing a rip-and-replace.

How do you validate a model before production?

We automate model testing, QA, and approval workflows so every model meets your performance standards before release.

How long does an MLOps rollout take?

It depends on your model count and infrastructure. The discovery and audit phase scopes this, and we usually start with a focused pipeline for your highest-value models.

Model drift is when a model's accuracy declines because real-world data changes after training. It is easy to miss without continuous monitoring.

How do you detect drift?

We integrate real-time monitoring to track performance, detect drift, and trigger alerts or automated retraining when thresholds are crossed.

Is retraining automated?

Yes. Retraining pipelines can be triggered by drift or schedule, validated automatically, and promoted through staged releases.

What does observability cover?

Full-stack observability across data, models, and infrastructure, with performance tracking and easy maintenance in production environments.

What happens when a model fails in production?

Alerts fire, lineage and audit trails help trace the cause, and rollback or retraining restores service.

Can we trace how a prediction was made?

Model lineage and audit trails let you explain predictions, trace failures, and support compliance reviews.

Ownership and versioning are defined as part of the pipeline, so every model in production has a clear owner and history.

Are approvals built into releases?

Yes. Approval workflows are part of the CI/CD pipeline so promotion to production is controlled and logged.

Is our data kept secure?

Access controls and security practices are designed into the infrastructure for cloud and on-prem environments.

Can you work alongside our data science team?

Yes. We integrate data science, engineering, and DevOps teams to remove silos and speed up iteration.

What results can we expect?

Results vary by starting point. In our documented logistics case, release cycles dropped from 6 weeks to 4 days with 99.9% pipeline uptime.

Where do I start a conversation?

Use any Request A Demo button and we will respond with a scoped proposal.

MLOps Services

Deploy, Scale, And Manage ML Models Efficiently

Our services automate ML operations from development to deployment, offering model training, CI/CD pipelines, monitoring, and automation.

Request A Demo How our MLOps works

Aarvexa MLOps pipeline analysis

CI/CD for MLAutomated testing, versioning, and deployment for every model release.

Drift detectionReal-time monitoring triggers alerts and retraining before accuracy erodes.

Governed releasesLineage, audit trails, and approvals built into the pipeline.

CI/CD PIPELINE · RUN #482DEMAND FORECASTING

Data

Train

Validate

Deploy

Monitor

MODEL ACCURACY · LIVE94.0%

threshold 90%

● healthy

MODEL REGISTRY

v1.7archived

v1.8production

v1.9queued

latency p9542 ms

uptime99.9%

models live31

undetected drift0

RELEASE CYCLEmonitoring…

manual releases…

Auto-retrain on drift

The cost

Why Unmanaged ML Models Are Silently Costing You?

Relying on ad-hoc deployment without a proper MLOps framework creates three critical liabilities:

Model Drift Goes Undetected

Without continuous monitoring, outdated models silently erode accuracy and compound business losses before anyone notices.

Deployment Bottlenecks Stall Growth

Manual handoffs between data science and engineering add weeks, making every model update risky and time-consuming.

No Governance, No Accountability

Without model lineage and audit trails, you cannot explain predictions, trace failures, or pass compliance reviews.

Request A Demo

What we do

Our MLOps Services

CI/CD for Machine Learning

We set up end-to-end CI/CD pipelines tailored for ML workflows, automating model testing, deployment, and versioning so your releases are faster and more reliable.

MLOps Development

We help you build a strong MLOps foundation, from designing wireframes to integrating tools, and ensure your ML lifecycle is efficient, repeatable, and scalable.

Cloud & On-Prem MLOps

On AWS, Azure, GCP, or on-prem infrastructure, we build flexible MLOps systems that easily integrate into your environment.

Governance & Compliance

We implement governance controls, audit logs, and compliance workflows that align with industry standards like HIPAA, GDPR, and ISO, so your models stay secure and compliant.

Monitoring & Drift Detection

We integrate real-time monitoring tools to track performance, detect drift, and trigger alerts or retraining when needed, keeping your models accurate and reliable.

Automated Model Validation

We automate model testing, QA, and approval workflows, making sure every model meets your performance standards before it goes into production.

Our process

How Our MLOps Services Work

Discovery & Audit

We begin by assessing your existing ML workflows, infrastructure, and pain points to identify gaps and opportunities for optimization.

Strategy & Architecture Design

Based on the audit, we design a customized MLOps framework, selecting the right tools, platforms, and architecture to support your long-term goals.

MLOps Implementation

We build and integrate CI/CD pipelines, monitoring systems, model validation processes, and infrastructure components tailored to your environment.

Deployment & Optimization

We deploy your models to production with automation and observability in place, then fine-tune performance based on real-world data and feedback loops.

Ongoing Support & Scaling

We provide continuous monitoring, retraining strategies, and infrastructure scaling support so your AI initiatives grow with your business.

Start Your MLOps Journey

Challenges

Overcoming Delays In Model Deployment, Limited Scalability, Or Poor Monitoring

Complex and Time-Consuming Deployments

We automate CI/CD pipelines and MLOps workflows. To speed up deployment and remove manual bottlenecks for faster time-to-value.

Silos Between Data Science and Engineering

We integrate data science, engineering, and DevOps teams, continuous collaboration, and faster iteration cycles across the ML lifecycle.

Risk of Model Drift and Non-Compliance

We implement real-time monitoring and drift detection, along with automated compliance checks, that assure models meet performance and regulatory standards.

Inefficient Resource Utilization

We optimize resource allocation through autoscaling and cost-efficient orchestration, improving efficiency and maximizing ROI in cloud and on-prem environments.

Lack of Observability and Automation

We provide full-stack observability and automate workflows. This allows continuous optimization, performance tracking, and easy maintenance in production environments.

Tech stack

MLOps Tools And Platforms We Work With

Tool choices follow your infrastructure, security constraints, and how easily the pipeline can be maintained at scale.

Experiment & Registry

MLflow
Weights & Biases
DVC
Model registries

Pipelines & Orchestration

Kubeflow
Apache Airflow
GitHub Actions
Jenkins

Serving & Infrastructure

Docker
Kubernetes
AWS, Azure, GCP
On-prem GPU clusters

Monitoring & Governance

Prometheus & Grafana
Drift detection
Audit logs
Lineage tracking

Discuss Your MLOps Stack

Customer story

Customer Story

Full MLOps Pipeline From Scratch

Project's SummaryDocumented Pipeline Outcomes

Model deployment speed70% faster

Pipeline uptime99.9%

Undetected model failuresZero

Logistics MLOps Case Study

From 6-Week Releases to 4 Days Across 30+ Models

"We had over 30 models in production with no versioning, no monitoring, and no clear ownership. Aarvexa built us a full MLOps pipeline from scratch, automated retraining, drift detection, and CI/CD deployment, cutting our release cycle from 6 weeks to 4 days." — Head of Data Engineering, Logistics Company

70%Faster Model Deployment

99.9%Pipeline Uptime

ZeroUndetected Model Failures In Production

Versioning and ownership introduced for 30+ production models.
Automated retraining keeps models current without manual effort.
Drift detection catches accuracy loss before it reaches the business.
CI/CD deployment cut the release cycle from 6 weeks to 4 days.
Monitoring covers every model in production with alerting.
Lineage and audit trails make failures traceable.

Read the Full Case Study

FAQ

Frequently Asked Questions

Straight answers on MLOps: pipelines, deployment, monitoring, governance, and support.

MLOps services apply DevOps-style automation to the machine learning lifecycle: model training, CI/CD pipelines, versioning, deployment, monitoring, retraining, and governance, so models run reliably in production.

Without monitoring, drift goes undetected and accuracy erodes silently. Manual handoffs between data science and engineering add weeks to every release, and missing lineage makes failures hard to trace or explain.

Teams with models in or heading to production, especially when they have multiple models, slow releases, no versioning, or compliance requirements.

Both. We build MLOps systems on AWS, Azure, GCP, or on-prem infrastructure that integrate into your environment.

Request a demo. We review your current ML workflows and infrastructure, then propose an audit or a pilot pipeline.