MLOps Services — From Notebook to Production
87% of ML projects die before production. We rescue them. Opsio's MLOps services automate the full ML lifecycle — data pipelines, model training, deployment, monitoring, and retraining — so your models deliver real business value, not just notebook demos.
Trusted by 100+ organisations across 6 countries · 4.9/5 client rating
87%
Models Rescued
97%+
Production Accuracy
40-60%
ML Cost Reduction
8-16 wk
Time to Production
What is MLOps Services?
MLOps (Machine Learning Operations) is the practice of automating and operationalising the full ML lifecycle — from data processing and model training through deployment, monitoring, drift detection, and automated retraining in production environments.
MLOps That Gets Models Into Production
87% of data science projects never make it to production. The gap between a working notebook and a reliable, scalable production model is massive — and it's growing. Data scientists build brilliant models that never see a single real prediction because the infrastructure to deploy, monitor, and maintain them doesn't exist. Opsio bridges that gap with production-tested MLOps engineering: automated data pipelines, reproducible training, scalable serving, continuous monitoring, and automated retraining when performance degrades.
We implement MLOps on AWS SageMaker, Azure ML, Vertex AI, or fully open-source stacks including Kubeflow, MLflow, and Apache Airflow. Our platform-flexible approach means you're never locked into a single vendor. We build infrastructure that lets data scientists focus on modeling and experimentation while we handle the operational complexity of production ML systems — from data ingestion through model retirement.
The difference between MLOps and ad-hoc ML deployment is the difference between a production system and a science experiment. Without MLOps, models degrade silently, retraining is manual and inconsistent, feature computation drifts between training and serving, and nobody knows when a model starts making bad predictions. Our MLOps implementations solve every one of these problems systematically.
Every Opsio MLOps deployment includes experiment tracking with full reproducibility, model versioning and lineage, A/B testing for safe production rollouts, data and concept drift detection, automated retraining pipelines, and GPU cost optimization. The complete ML lifecycle — managed professionally from day one through ongoing production operations.
Common MLOps challenges we solve: training-serving skew causing production accuracy drops, GPU cost overruns from unoptimized instance selection, lack of model versioning making rollbacks impossible, missing monitoring leaving model degradation undetected for weeks, and manual retraining processes that take days instead of minutes. If any of these sound familiar, you need MLOps.
Following MLOps best practices, our MLOps maturity assessment evaluates where your organisation stands today and builds a clear roadmap to production-grade ML. We use proven MLOps tools — SageMaker, MLflow, Kubeflow, Weights & Biases, and more — selected based on your specific environment and team capabilities. Whether you're exploring MLOps vs DevOps differences for the first time or scaling an existing ML platform, Opsio delivers the engineering expertise to close the gap between experimentation and production. Wondering about MLOps cost or whether to hire in-house vs engage MLOps consulting? Our assessment gives you a clear answer — with a detailed cost-benefit analysis tailored to your model portfolio and infrastructure.
How We Compare
| Capability | DIY / Ad-hoc ML | Open-Source MLOps | Opsio Managed MLOps |
|---|---|---|---|
| Time to production | Months | 6-12 weeks | 4-8 weeks |
| Monitoring & drift detection | None / manual | Basic setup | Full automation + alerting |
| Retraining | Manual, inconsistent | Semi-automated | Fully automated with approval gates |
| GPU cost optimisation | Over-provisioned | Basic spot usage | 40-60% savings guaranteed |
| Feature store | None | Self-managed Feast | Managed + consistency guaranteed |
| On-call support | Your data scientists | Your DevOps team | Opsio 24/7 ML engineers |
| Typical annual cost | $200K+ (hidden costs) | $100-150K (+ ops overhead) | $96-180K (fully managed) |
What We Deliver
ML Pipeline Automation
End-to-end automated training pipelines on SageMaker, Azure ML, or Vertex AI. We orchestrate data ingestion, feature engineering, model training, evaluation, and deployment — triggered on schedule, new data arrival, or drift detection alerts. Pipelines are version-controlled and fully reproducible.
Model Serving & Deployment
Production model deployment with A/B testing, canary releases, shadow deployments, and auto-scaling. We configure SageMaker Endpoints, Vertex AI Endpoints, or custom KServe clusters to handle thousands of inference requests per second with sub-100ms latency and automatic failover.
Feature Store Implementation
Centralized feature stores using SageMaker Feature Store, Feast, or Vertex AI Feature Store. We ensure consistent feature computation between training and serving, eliminating the training-serving skew that causes production accuracy drops — the #1 reason ML models fail in production.
Monitoring & Drift Detection
Comprehensive production model monitoring for data drift, concept drift, prediction distribution shifts, and accuracy degradation. We configure automated retraining triggers, Slack/PagerDuty alerting, and dashboards so model performance issues are caught within hours, not weeks.
GPU Optimization & Cost Management
Strategic GPU instance selection (P4d, G5, T4), spot instance strategies, multi-GPU distributed training, mixed-precision training, and model optimization techniques like quantization, pruning, and knowledge distillation. Our clients typically reduce ML compute costs by 40-60% without sacrificing model quality.
Experiment Tracking & Reproducibility
MLflow or Weights & Biases integration for fully reproducible experiments with comprehensive metrics logging, hyperparameter tracking, dataset versioning, model lineage, and artifact management — ensuring every production model can be traced back to its exact training data, code, and configuration.
Ready to get started?
Get Your Free MLOps AssessmentWhat You Get
“Opsio's focus on security in the architecture setup is crucial for us. By blending innovation, agility, and a stable managed cloud service, they provided us with the foundation we needed to further develop our business. We are grateful for our IT partner, Opsio.”
Jenny Boman
CIO, Opus Bilprovning
Investment Overview
Transparent pricing. No hidden fees. Scope-based quotes.
MLOps Assessment
$15,000–$30,000
1-3 week engagement
Platform Build
$35,000–$80,000
Most popular — full pipeline
Managed MLOps
$8,000–$15,000/mo
Ongoing operations
Pricing varies based on scope, complexity, and environment size. Contact us for a tailored quote.
Questions about pricing? Let's discuss your specific requirements.
Get a Custom QuoteWhy Choose Opsio
Production-focused
We deploy models to reliable production systems, not just notebooks — with SLAs, monitoring, and on-call support.
Platform-flexible
SageMaker, Azure ML, Vertex AI, or fully open-source stacks — we use the platform that fits your environment, not ours.
Cost-optimized from day one
GPU optimization, spot strategies, and right-sizing reducing ML infrastructure costs by 40-60% without accuracy trade-offs.
End-to-end ML lifecycle
Data pipelines, feature stores, training, serving, monitoring, retraining — the complete MLOps lifecycle under one team.
Data engineering included
We build the data ingestion and feature engineering pipelines that feed your models — not just the ML infrastructure.
Monitoring and retraining built in
Drift detection, accuracy monitoring, and automated retraining configured from day one — models stay accurate in production.
Not sure yet? Start with a pilot.
Begin with a focused 2-week assessment. See real results before committing to a full engagement. If you proceed, the pilot cost is credited toward your project.
Our Delivery Process
ML Assessment
We evaluate your ML workloads, data infrastructure, model inventory, team maturity, and production readiness. Deliverable: MLOps maturity scorecard and prioritised roadmap. Timeline: 1-2 weeks.
Platform Architecture
Design the complete MLOps platform: training pipelines, feature store, model registry, serving infrastructure, monitoring stack, and CI/CD for ML. We select the optimal platform based on your cloud environment. Timeline: 2-3 weeks.
Build & Deploy
Implement the full MLOps platform with automated training pipelines, model serving endpoints, drift detection, experiment tracking, and retraining automation. We migrate your first 2-3 models to production. Timeline: 4-8 weeks.
Operate & Optimise
Ongoing ML infrastructure management including model performance monitoring, GPU cost optimisation, pipeline maintenance, new model onboarding, and quarterly platform reviews. We become your MLOps operations team. Timeline: Ongoing.
Key Takeaways
- ML Pipeline Automation
- Model Serving & Deployment
- Feature Store Implementation
- Monitoring & Drift Detection
- GPU Optimization & Cost Management
Industries We Serve
Manufacturing
Visual inspection, predictive maintenance, and quality control ML models at production line speed.
Financial Services
Risk scoring, fraud detection, credit decisioning, and anti-money-laundering models with regulatory compliance.
Retail & E-commerce
Demand forecasting, product recommendations, dynamic pricing, and customer churn prediction at scale.
Healthcare & Pharma
Clinical prediction models, drug discovery pipelines, diagnostic support, and medical imaging analysis.
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MLOps Services — From Notebook to Production FAQ
What is MLOps and why does it matter?
MLOps (Machine Learning Operations) is the practice of automating the entire ML lifecycle: data processing, model training, deployment, monitoring, and retraining. Without MLOps, 87% of ML projects never reach production — models degrade silently, deployments are manual and error-prone, features drift between training and serving, and data scientists spend 80% of their time on infrastructure instead of modeling. MLOps matters because it transforms ML from an experimental capability into a reliable production system that delivers measurable business value consistently. Companies with mature MLOps practices deploy models 10x faster and maintain 30% higher accuracy in production.
What is the difference between MLOps and DevOps?
DevOps automates software delivery — code goes through CI/CD pipelines from development to production. MLOps extends this to machine learning, which has unique challenges DevOps doesn't address: data versioning, experiment tracking, feature stores, model training pipelines, serving infrastructure with A/B testing, production monitoring for data drift and accuracy degradation, and automated retraining. Think of MLOps as DevOps plus data management plus model lifecycle management. A DevOps engineer can deploy code, but deploying a model requires managing the training data, feature computation, model artifacts, serving endpoints, and continuous monitoring — all of which MLOps automates.
Which MLOps platforms do you support?
We implement MLOps on AWS SageMaker (the most popular choice for AWS-native organisations), Microsoft Azure ML (ideal for Microsoft-ecosystem companies), Google Vertex AI (best for BigQuery-centric data teams), and fully open-source stacks using Kubeflow, MLflow, Apache Airflow, and KServe. Platform selection depends on your existing cloud environment, team expertise, model complexity, and vendor preferences. We often combine platforms — for example, MLflow for experiment tracking with SageMaker for training and serving. During our assessment phase, we evaluate all options and recommend the architecture that balances capability, cost, and operational simplicity.
How much do MLOps services cost?
MLOps investment varies by scope. An MLOps assessment and strategy engagement runs $15,000-$30,000 (1-3 weeks) and delivers a maturity scorecard, platform recommendation, and implementation roadmap. Full platform build and deployment ranges from $35,000-$80,000 depending on the number of models, pipeline complexity, and integration requirements. Ongoing managed MLOps operations cost $8,000-$15,000/month covering pipeline management, model monitoring, retraining, GPU optimisation, and platform maintenance. Most clients see ROI within 6-9 months through reduced data science infrastructure time (typically 60-80% reduction), faster model deployment cycles (weeks instead of months), and lower GPU compute costs (40-60% savings).
How long does it take to set up an MLOps platform?
A production-ready MLOps platform typically takes 8-16 weeks end-to-end. The assessment phase runs 1-2 weeks, architecture design takes 2-3 weeks, implementation and first model migration takes 4-8 weeks, and stabilisation and knowledge transfer adds 1-2 weeks. The timeline depends on the number of models being productionised, data pipeline complexity, integration requirements with existing systems, and team readiness. We can accelerate timelines by starting with a focused pilot — productionising your highest-priority model first, then expanding the platform to additional models incrementally.
Do I need MLOps if I only have a few models?
Yes — even a single production model needs monitoring, versioning, and retraining capability. Without MLOps, you won't know when your model starts degrading (and it will — data distributions change, user behaviour shifts, and seasonal patterns evolve). The cost of a degraded model making bad predictions silently is almost always higher than the cost of basic MLOps infrastructure. For small model portfolios (1-5 models), we recommend a lightweight MLOps stack: MLflow for tracking, a simple training pipeline, basic drift monitoring, and manual retraining triggers. This can be implemented in 4-6 weeks for $15,000-$25,000 and scaled as your ML practice grows.
What tools are used in MLOps?
The MLOps toolchain depends on your platform choice, but common tools include: training orchestration (SageMaker Pipelines, Vertex AI Pipelines, Kubeflow Pipelines, Apache Airflow), experiment tracking (MLflow, Weights & Biases, Neptune), feature stores (SageMaker Feature Store, Feast, Tecton), model serving (SageMaker Endpoints, KServe, Seldon Core, TorchServe), model monitoring (Evidently AI, Arize, WhyLabs, SageMaker Model Monitor), CI/CD for ML (GitHub Actions, GitLab CI with ML-specific stages), and infrastructure (Terraform, Docker, Kubernetes). We select and integrate the optimal combination based on your specific requirements rather than forcing a one-size-fits-all stack.
What are the stages of the MLOps lifecycle?
The MLOps lifecycle has six stages: (1) Data management — ingestion, validation, versioning, and feature engineering through feature stores. (2) Model development — experiment tracking, hyperparameter tuning, and model selection with full reproducibility. (3) Model training — automated, versioned training pipelines triggered by new data or schedules. (4) Model deployment — CI/CD for models with A/B testing, canary releases, and automated rollback. (5) Model monitoring — production performance tracking, data drift detection, and accuracy monitoring with alerting. (6) Model retraining — automated retraining triggered by drift or performance thresholds, with human-in-the-loop approval for critical models. Each stage feeds into the next, creating a continuous improvement loop.
How can I reduce MLOps cost without sacrificing quality?
The biggest MLOps cost drivers are GPU compute, data storage, and engineering time. We reduce GPU costs 40-60% through spot instance strategies, right-sizing (most teams over-provision by 2-3x), mixed-precision training, and model optimization techniques like quantization. For storage, we implement tiered retention — hot data on SSD, warm on S3/GCS, cold archived. Engineering time drops dramatically with automation: what takes a data scientist 2 days to deploy manually takes 15 minutes with our CI/CD pipelines. The net result is that managed MLOps through Opsio typically costs less than the hidden costs of DIY — fewer production incidents, faster iteration cycles, and no need to hire dedicated ML infrastructure engineers at $180K+ each.
Should I hire MLOps engineers or use MLOps consulting?
For most organisations with fewer than 20 models in production, MLOps consulting and managed services are more cost-effective than hiring. A senior MLOps engineer costs $150,000-$200,000/year in salary alone, plus benefits, training, and retention risk. You typically need 2-3 engineers for 24/7 coverage. Opsio's managed MLOps service provides an entire team — platform architects, ML engineers, and on-call support — for $8,000-$15,000/month. That's $96,000-$180,000/year vs $450,000-$600,000 for an in-house team. MLOps consulting also gets you production-ready faster: our team has already solved the problems your new hires would spend months figuring out. We recommend in-house MLOps teams only when you have 20+ production models and ML is a core competitive differentiator.
Still have questions? Our team is ready to help.
Get Your Free MLOps AssessmentReady to Get Your Models Into Production?
87% of ML projects fail before production. Get a free MLOps readiness assessment and a clear roadmap to production-grade ML.
MLOps Services — From Notebook to Production
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