85% of ML Projects Never Reach Production.
We Fix That.
Your data science team built impressive models. They work perfectly in notebooks. But getting them to production? That's where projects stall, budgets burn, and AI investments fail to deliver ROI. We implement end-to-end MLOps infrastructure that takes your models from experiment to production in 8 weeks—with continuous monitoring, automated retraining, and governance built in.
The Production Gap: Where ML Projects Go to Die
The hard truth about enterprise machine learning isn't that building models is difficult. It's that deploying and maintaining them in production is a completely different challenge—one that most organizations aren't prepared for.
Gartner found only 54% of AI models make it from pilot to production. Rexer Analytics puts the number at 32%. O'Reilly research shows only 26% of organizations have models actively deployed. This isn't a data science problem. It's an operations problem.
The Notebook Trap
Your best model lives in a Jupyter notebook on a data scientist's laptop. It works perfectly there. But the moment you try to deploy it—different Python versions, missing dependencies, data format mismatches—everything breaks.
The Silent Degradation
You deployed a model six months ago. It was accurate then. But real-world data has shifted. Your model is making predictions based on patterns that no longer exist—and nobody knows because there's no monitoring.
The Deployment Bottleneck
Data scientists build. ML engineers translate. DevOps deploys. Each handoff introduces delays. A model that took 2 weeks to develop takes 6 months to deploy—if it ever gets deployed at all.
The Governance Vacuum
Regulators are asking about your AI models. Which version is in production? What data was it trained on? Can you prove it's not discriminating? Without governance, you can't answer these questions.
The Scaling Ceiling
One model in production is manageable. Ten models becomes a full-time job. A hundred models is impossible without automation. Your AI strategy is constrained by operational bottlenecks.
What is MLOps? The Missing Infrastructure for Production ML
MLOps—Machine Learning Operations—is the discipline of deploying, monitoring, and maintaining ML models in production reliably and efficiently. Think of it as DevOps specifically designed for the unique challenges of machine learning.
Unlike traditional software, ML models have characteristics that require specialized infrastructure: models degrade over time as data changes, reproducibility requires versioning of code, data, and environment, and testing model quality is fundamentally different from testing software correctness. MLOps provides the automation, monitoring, and governance that bridges data science experimentation with production operations.
Automated Deployment Pipelines
CI/CD designed for ML: automated testing, validation, and deployment that understands model quality. Safe rollouts with canary deployments and automatic rollback.
Model Registry & Versioning
Central repository for all model artifacts with full versioning. Track which model is in production, what data it was trained on, who approved it.
Drift Detection & Monitoring
Continuous monitoring that detects when data diverges from training distributions or when input-output relationships change. Alerts before performance degrades.
Automated Retraining
When drift is detected or performance drops, automatically trigger retraining with fresh data. Models stay current without manual intervention.
Model Governance
Complete audit trail: who trained the model, what data was used, who approved deployment. Compliance-ready documentation generated automatically.
Experiment Tracking
Every experiment logged: parameters, metrics, artifacts. Compare runs, reproduce results, build on what works. Data scientists stay productive.
Production MLOps in 8 Weeks
We don't sell MLOps platforms and leave you to figure out implementation. We build production-ready infrastructure tailored to your models, your data, and your deployment requirements—operational within 8 weeks, not 8 months.
Assessment & Architecture
We analyze your current ML workflow, existing infrastructure, and production requirements. You get a clear picture of your MLOps maturity and a concrete roadmap.
View Deliverables
- ML workflow assessment
- Infrastructure inventory
- Production requirements documentation
- MLOps architecture design
- Technology recommendations
- Implementation roadmap
Pipeline Foundation
We implement core infrastructure: version control for ML artifacts, experiment tracking, and the foundation for automated pipelines. Your team starts working production-ready.
View Deliverables
- Model registry deployment
- Experiment tracking setup
- Data and model versioning
- Feature store (if required)
- Development environment standardization
Deployment Automation
We build automated deployment pipelines that take models from trained to production with confidence. CI/CD for ML with testing, validation, and safe rollouts.
View Deliverables
- ML CI/CD pipeline
- Automated model testing
- Validation and quality gates
- Canary deployment configuration
- Rollback automation
- Model serving infrastructure
Monitoring & Launch
We implement continuous monitoring, configure alerting, set up automated retraining triggers. Models go to production with visibility and governance built in.
View Deliverables
- Performance monitoring dashboard
- Drift detection configuration
- Automated alerting
- Retraining pipeline triggers
- Governance and audit trails
- Training and documentation
Assessment & Architecture
We analyze your current ML workflow, existing infrastructure, and production requirements. You get a clear picture of your MLOps maturity and a concrete roadmap.
View Deliverables
- ML workflow assessment
- Infrastructure inventory
- Production requirements documentation
- MLOps architecture design
- Technology recommendations
- Implementation roadmap
Pipeline Foundation
We implement core infrastructure: version control for ML artifacts, experiment tracking, and the foundation for automated pipelines.
View Deliverables
- Model registry deployment
- Experiment tracking setup
- Data and model versioning
- Feature store (if required)
- Development environment standardization
Deployment Automation
We build automated deployment pipelines that take models from trained to production with confidence.
View Deliverables
- ML CI/CD pipeline
- Automated model testing
- Validation and quality gates
- Canary deployment configuration
- Rollback automation
- Model serving infrastructure
Monitoring & Launch
We implement continuous monitoring, configure alerting, set up automated retraining triggers.
View Deliverables
- Performance monitoring dashboard
- Drift detection configuration
- Automated alerting
- Retraining pipeline triggers
- Governance and audit trails
- Training and documentation
Complete MLOps Infrastructure
Everything you need to move from experimental notebooks to production ML—delivered as operational infrastructure, not as a platform you have to figure out.
Production ML Pipeline
End-to-end automated pipeline from training to production. Version-controlled, tested, reproducible. Deploy with confidence, roll back with one command.
Model Registry
Centralized storage for all model artifacts with versioning. Track lineage, compare versions, manage staging-to-production promotion with approvals.
Experiment Tracking
Complete visibility into all experiments: parameters, metrics, artifacts. Compare runs, reproduce results, collaborate effectively.
Drift Monitoring System
Continuous monitoring for data and concept drift. Statistical tests detect distribution shifts. Configurable alerts and automated responses.
Model Serving Infrastructure
Production-grade serving with auto-scaling, low-latency inference, A/B testing. Serve multiple versions for safe experimentation.
Governance Framework
Complete audit trail for compliance. Model cards, access controls, approval workflows. Regulatory-ready documentation generated automatically.
Automated Retraining
Trigger-based retraining when drift is detected. Fresh models trained, validated, promoted automatically.
Documentation & Training
Technical documentation for operations. Training for data scientists, ML engineers, platform teams. Runbooks for common scenarios.
Model Drift: The Silent Killer of Production ML
The model that worked perfectly at launch is quietly degrading. Real-world data is shifting. Without drift monitoring, you won't know until predictions become noticeably wrong—and by then, the damage is done.
Data Drift
What it is
The statistical distribution of input features changes over time.
Example
A credit model trained on pre-pandemic data receives applications with different income patterns and spending behaviors.
Impact
Model receives inputs it wasn't trained to handle. Predictions become unreliable.
Detection
Statistical tests compare current feature distributions against training baselines.
Concept Drift
What it is
The relationship between inputs and outputs changes. What used to predict success no longer does.
Example
A churn model learned certain behaviors predicted cancellation. A new competitor enters, and different behaviors now signal risk.
Impact
Model's learned patterns no longer reflect reality. Predictions are fundamentally wrong.
Detection
Monitor prediction accuracy against actual outcomes. Watch for divergence.
Prediction Drift
What it is
The distribution of model predictions shifts, even if underlying patterns haven't changed.
Example
A fraud model suddenly flags twice as many transactions after launching in a new market.
Impact
May indicate data drift, concept drift, or business changes requiring recalibration.
Detection
Monitor prediction distributions over time. Alert when shifts exceed thresholds.
Our Multi-Layer Detection
Data Quality
Missing values, outliers, schema violations
Feature Drift
Distribution shifts in input features
Prediction Drift
Changes in model output patterns
Performance Drift
Degradation in accuracy metrics
When drift is detected:
Where MLOps Delivers Value
Real scenarios where production MLOps infrastructure transforms ML operations from fragile to reliable.
First Model to Production
From data science experiments to production deployment
First Model to Production
From data science experiments to production deployment
Challenge
You have a data science team building models, but nothing has made it to production yet. Each deployment attempt hits different blockers.
Solution
We implement MLOps infrastructure alongside your first production deployment. You get infrastructure for all future models.
Outcome
First model in production within 8 weeks. Capability ready for every model that follows.
Scaling from 1 to 100 Models
Standardize and automate for portfolio growth
Scaling from 1 to 100 Models
Standardize and automate for portfolio growth
Challenge
Manual processes don't scale. Each new model requires custom work. Your ML engineering team is overwhelmed.
Solution
We standardize and automate deployment. Models go through consistent, automated process with monitoring built in.
Outcome
Deploy new models in days instead of months. Consistent monitoring across all models.
Model Performance Degradation
Detect drift before business impact
Model Performance Degradation
Detect drift before business impact
Challenge
Production models are quietly degrading. You only discover problems when business metrics suffer or users complain.
Solution
Comprehensive drift monitoring and automated alerting. Problems detected before business impact.
Outcome
Early warning for degradation. Proactive intervention. Automated retraining to keep models current.
Regulatory Compliance
Audit-ready ML governance
Regulatory Compliance
Audit-ready ML governance
Challenge
Regulators asking questions you can't answer: What model is in production? How was it trained? Can you prove it's not discriminating?
Solution
Model governance with complete audit trails. Every deployment documented automatically.
Outcome
Audit-ready documentation. Demonstrable compliance. Reduced regulatory risk.
A/B Testing ML Models
Safe experimentation in production
A/B Testing ML Models
Safe experimentation in production
Challenge
You want to test new model versions against production, but rolling out is all-or-nothing.
Solution
A/B testing infrastructure for ML. Route traffic, measure differences, promote winners.
Outcome
Safe experimentation in production. Data-driven model promotion.
Real-Time Inference at Scale
Auto-scaling production serving
Real-Time Inference at Scale
Auto-scaling production serving
Challenge
Models need to serve predictions at high volume with low latency. Current infrastructure can't handle the load.
Solution
Auto-scaling serving infrastructure. Scale up during peaks, scale down to save costs.
Outcome
Production inference that scales. Consistent latency at any volume.
Who Benefits from Production MLOps
Production MLOps infrastructure serves different needs across your organization. Here's how we help each team.
Data Science Teams
Your best work deserves production
You build great models. But getting them to production isn't your job—and the current process is frustrating. Your best work sits in notebooks.
Our Approach
MLOps infrastructure that lets you focus on modeling. Experiment tracking keeps work organized. Automated pipelines deploy without manual translation.
ML Engineers
Stop building the same pipeline twice
You're the bridge between data science and production. Without infrastructure, every deployment is custom. You're fighting the same battles repeatedly.
Our Approach
Standardized infrastructure that makes deployment repeatable. Templates for common patterns. Expertise goes into improving the system, not fighting it.
Platform/DevOps Teams
ML workloads that fit your platform
ML is different from software, and your CI/CD doesn't quite work. Data scientists need things that don't fit your tooling.
Our Approach
ML-native infrastructure that integrates with your existing platform. Kubernetes-native where appropriate. ML becomes a supported workload, not an exception.
CTOs & Engineering Leadership
ROI from your AI investment
You've invested in data science. The models look impressive in demos. But production deployment is taking too long, and ROI is hard to demonstrate.
Our Approach
Production MLOps in 8 weeks, not 8 months. Clear timeline, measurable outcomes. AI investments deliver business value faster.
Compliance & Risk Teams
Audit-ready AI
AI regulations are increasing. Model decisions need to be explainable and auditable. Current documentation is inadequate.
Our Approach
Governance from the start. Complete audit trails. Documentation generated automatically. Compliance built into pipeline.
Frequently Asked Questions
Modern MLOps Stack
We implement using proven, industry-standard tools—no proprietary lock-in. Your team can operate and extend the infrastructure after we leave. We choose tools based on your requirements, not vendor relationships.
Experiment Tracking & Model Registry
Centralized tracking for all experiments. Model registry with versioning, staging, and promotion workflows.
ML Pipelines & Orchestration
Workflow orchestration for training, validation, deployment. Reproducible pipelines with dependency management.
Model Serving
Production inference with auto-scaling, A/B testing, canary deployments. Multi-framework support.
Monitoring & Observability
Drift detection, performance monitoring, alerting. Integration with existing observability stack.
Feature Store
Consistent feature computation for training and serving. Versioning and lineage tracking.
Infrastructure
Cloud-native, containerized. Infrastructure as code. Cost-optimized resource allocation.
Investment & Engagement Options
MLOps Assessment
2-3 weeks
$15,000 - $25,000
Comprehensive analysis of your ML workflow and production requirements with specific recommendations.
Includes
- ML workflow assessment
- Maturity evaluation
- Architecture recommendations
- Tool selection guidance
- Implementation roadmap
- Executive summary
Foundation MLOps
8-10 weeks
$75,000 - $125,000
Complete implementation for organizations with straightforward deployment requirements.
Everything in Assessment, plus
- Model registry & experiment tracking
- ML CI/CD pipeline
- Basic drift monitoring
- Model serving infrastructure
- Governance framework
- Training and documentation
- 30 days post-launch support
Enterprise MLOps
12-16 weeks
$125,000 - $250,000+
Comprehensive MLOps for complex environments with advanced monitoring and governance.
Everything in Foundation, plus
- Multi-model orchestration
- Advanced drift detection
- Automated retraining
- A/B testing infrastructure
- Feature store
- Enterprise governance
- 90 days support
Expected Return on Investment
Organizations that successfully deploy ML see 3-15% profit margin increases. MLOps typically delivers ROI within 6-12 months through faster deployment, reduced failures, and scaling efficiency.
Get Your Models to Production
Your data science investment should deliver business value, not sit in notebooks. MLOps infrastructure is the difference between AI projects that fail and AI capabilities that scale.
Start with an assessment. We'll analyze your current ML workflow, identify what's blocking production, and show you exactly what MLOps infrastructure can deliver. Clear roadmap. Specific recommendations. No commitment to implementation.
At a Glance
Real Impact
Industry Deployment Patterns
How different industries implement MLOps for model governance and reliability.
Finance
Fraud detection models with strict audit trails
Real-time fraud scoring models with comprehensive lineage tracking, bias monitoring for fair lending compliance, and automated rollback on accuracy degradation below 94%
Healthcare
Clinical prediction models with HIPAA compliance
Patient risk stratification models with explainability requirements, PHI-safe model artifacts, audit logs for all predictions, and regulatory-compliant model versioning
Manufacturing
Predictive maintenance with edge deployment
Equipment failure prediction models deployed to edge devices with drift monitoring for sensor data, automated retraining triggers, and zero-downtime model updates
Architecture Decision Guide
Choosing the right MLOps architecture for your organization's scale and governance needs.
| Approach | When to Use | Tradeoffs | Best For |
|---|---|---|---|
| Centralized MLOps | Single ML team, consistent tooling requirements, centralized governance needed | Strong governance and consistency, but may slow down autonomous teams. Best for orgs prioritizing compliance. | Finance, Healthcare, Regulated Industries |
| Federated MLOps | Multiple ML teams, poly-cloud deployment, team autonomy prioritized | Teams move faster with their preferred tools, but governance becomes harder. Requires strong platform team. | Large enterprises, Multi-cloud, Product-driven orgs |
| Hybrid MLOps | Balance of governance and flexibility needed, phased adoption | Centralize critical governance (registry, monitoring) while allowing tool flexibility. Moderate complexity. | Mid-market, Growing ML teams, Compliance-aware |
MLOps Stack Comparison
We help you choose and implement the right MLOps platform for your team's needs and constraints.
MLflow
Open source, Python-first teams
- ✓Free and open source
- ✓Strong experiment tracking
- ✓Good model registry features
- ✓Active community support
- −Limited enterprise features
- −Requires self-hosting infrastructure
- −Basic UI compared to commercial options
Weights & Biases
Experiment tracking, team collaboration
- ✓Excellent visualization and dashboards
- ✓Strong team collaboration features
- ✓Easy integration with popular frameworks
- ✓Managed cloud service available
- −Can be expensive at scale
- −Vendor lock-in with managed service
- −Less control over infrastructure
Kubeflow
Kubernetes-native, large scale
- ✓Cloud-agnostic and portable
- ✓Tight Kubernetes integration
- ✓Full ML pipeline orchestration
- ✓Enterprise-grade scalability
- −Steep learning curve
- −Kubernetes expertise required
- −Complex setup and maintenance
Custom Registry
Specific compliance, legacy integration
- ✓Full control and customization
- ✓Integrate with existing systems
- ✓Meet specific compliance requirements
- ✓No vendor dependency
- −Higher development time
- −Ongoing maintenance burden
- −Requires in-house expertise
Deployment Pipeline
Governed promotion path from dev to production with automated quality gates and rollback automation.
Technology & Integration Matrix
Drift Detection Methods
Automated statistical tests to catch model degradation before it impacts production.
Integration Points
Seamless integration with your existing ML infrastructure and tooling.
Procurement & RFP Readiness
Common requirements for MLOps vendor evaluation and model governance compliance.
Need vendor compliance docs? Visit Trust Center →
Outcomes
See the math →- •Registry & lineage in place
- •Drift/accuracy monitoring with alerts
- •Standardized deploy/rollback flows
- •Measurable reduction in model deployment time
What You Get (Acceptance Criteria)
Our standards →Timeline
3–5 weeks
Team
ML EngineerPlatform EngineerDevOps LeadQA Engineer
Inputs We Need
- •Existing model artifacts and training code
- •Deployment environments and CI/CD setup
- •Accuracy thresholds and alert policies
- •Rollback criteria
- •Budget/cost guardrails
Tech & Deployment
MLflow, Weights & Biases, or custom registry; cloud/on-prem; CI/CD integration (GitHub Actions, GitLab CI)
Proof We Show
Full evidence list →Frequently Asked Questions
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