Why MLOps Matters

87% of AI projects never make it to production. The gap between a working model and a production system is massive. Our MLOps consulting services bridge this gap by providing:

  • check_circleAutomated Pipelines - Train, test, deploy without manual intervention
  • check_circleModel Monitoring - Detect drift, degradation, and performance issues early
  • check_circleVersion Control - Track models, data, and experiments systematically
  • check_circleScalability - Handle millions of predictions with auto-scaling
  • check_circleGovernance - Audit trails, compliance, and explainability built-in

The MLOps Challenge

87%

of data science projects never make it to production

6–12 months

average time to deploy without MLOps

4–6 weeks

with proper MLOps infrastructure

Our MLOps Services

End-to-end MLOps as a service — deployment infrastructure for production AI

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End-to-End MLOps Pipeline

Build complete automated pipelines from data ingestion to model deployment with CI/CD best practices.

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Model Monitoring & Drift Detection

Real-time performance tracking, automated alerting for model degradation, and drift detection systems.

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CI/CD for ML Models

Automated testing, retraining triggers, canary deployments, and rollback mechanisms for ML systems.

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Cloud Platform Integration

Expert deployment on AWS SageMaker, Google Vertex AI, Azure ML, or on-premise infrastructure.

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Model Registry & Versioning

Implement MLflow, Weights & Biases, or custom registries for experiment tracking and model versioning.

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Cost Optimization

Reduce inference costs through model quantization, pruning, distillation, and infrastructure right-sizing.

The MLOps Lifecycle

1

Development

Data prep, feature engineering, model training, experiment tracking

2

Testing

Unit tests, integration tests, model validation, A/B testing

3

Deployment

Containerization, API creation, scaling, load balancing

4

Monitoring

Performance tracking, drift detection, automated retraining

Technology Stack

Best-in-class MLOps tooling

Cloud Platforms

AWS SageMaker · Google Vertex AI · Azure ML Studio · Databricks

Orchestration

Kubeflow · Airflow · Metaflow · Prefect

Tracking & Registry

MLflow · Weights & Biases · Neptune.ai · Comet

Deployment

Docker · Kubernetes · BentoML · Seldon Core

MLOps Use Cases

🛡️ Real-Time Fraud Detection

Deploy models that process millions of transactions per second with automatic retraining on new fraud patterns.

Challenge: 99.99% uptime

📋 Recommendation Systems

Scale personalization engines to handle millions of users with A/B testing and gradual rollouts.

Challenge: Low-latency predictions

⚙️ Predictive Maintenance

Deploy IoT sensor models with edge computing, periodic updates, and centralized monitoring.

Challenge: Edge deployment

👁️ Computer Vision at Scale

Deploy object detection and image classification models processing thousands of images per second.

Challenge: Cost optimization

Key MLOps Capabilities

📊 Experiment Tracking

Track every experiment, hyperparameter, and metric systematically

🔄 Automated Retraining

Trigger retraining based on data drift or performance degradation

📈 Performance Monitoring

Real-time dashboards for accuracy, latency, throughput, and errors

🔐 Model Governance

Audit trails, compliance tracking, and explainability frameworks

⚡ Auto-Scaling

Handle traffic spikes automatically with horizontal scaling

⏪ Rollback Mechanisms

Instant rollback to previous model versions if issues detected

Further Reading

Battle-tested write-ups from real deployments:

  • Read our MLOps production lessons — drift detection that actually fires, CI/CD for models, cost-controlled retraining, and the rollback patterns we rely on across customer deployments.
  • Deploying fine-tuned models? Pair MLOps with our LLM fine-tuning best practices guide for end-to-end training-to-production workflows.
  • Shipping autonomous systems? Read our ethical AI practices guide — covers audit logging, monitoring for bias drift, and the governance controls we build into every pipeline.

MLOps Packages

View prices in:

MLOps Assessment

$8,000–$15,000

Senior specialists. Transparent scope.

  • Current state analysis
  • Gap assessment
  • Architecture design
  • Tool recommendations
  • Implementation roadmap
Get Started

Pipeline Implementation

⭐ Most Popular
$22,000–$50,000

Senior specialists. Transparent scope.

  • Complete CI/CD setup
  • Model registry
  • Automated testing
  • Monitoring dashboards
  • 6 months support
Get Started

Enterprise MLOps

$55,000–$110,000

Senior specialists. Transparent scope.

  • Multi-model orchestration
  • Advanced monitoring
  • Governance framework
  • Multi-cloud setup
  • 12 months support
Get Started

Managed MLOps

$5,000–$12,000

/month retainer

  • 24/7 monitoring
  • Performance optimization
  • Cost management
  • Regular updates
  • Priority support
Get Started

"Fantastic AI engineer with pragmatic business and technical skills. Great to work with. An asset to any team."

Andy Curtis CISO, CibrAI — managed Hemang directly View Case Study →
Industries We Serve

MLOps Built for Your Industry's Needs

Production ML has different requirements in healthcare than in e-commerce. We build MLOps pipelines tailored to your industry's compliance, scale, and performance needs.

⚕️
Healthcare & Life Sciences

HIPAA-compliant ML pipelines for diagnostic models, patient risk scoring, and drug discovery — with full audit trails and model governance.

  • ✅ HIPAA & FDA 21 CFR Part 11 compliant
  • ✅ Model drift detection for diagnostic AI
  • ✅ Full audit trail for every prediction
🏦
Banking & Finance

Low-latency MLOps for fraud detection, credit scoring, and algorithmic trading — with real-time monitoring and automatic rollback on performance drops.

  • ✅ Sub-100ms fraud model inference
  • ✅ Model explainability for RBI compliance
  • ✅ A/B testing for credit score models
🛒
E-Commerce & Retail

MLOps for recommendation engines, demand forecasting, and dynamic pricing — with auto-retraining triggered by sales events and seasonal shifts.

  • ✅ Recommendation model retrained daily
  • ✅ Demand forecast accuracy 30% better
  • ✅ Black Friday traffic auto-scaled
🏭
Manufacturing & Industry 4.0

MLOps for predictive maintenance, visual quality inspection, and yield optimization — deployed on-premise or hybrid cloud for factory environments.

  • ✅ Predictive maintenance reduces downtime 45%
  • ✅ Computer vision QC at line speed
  • ✅ Edge deployment on factory hardware
🚚
Logistics & Supply Chain

Route optimization and ETA prediction models running in production — with continuous monitoring and retraining as traffic patterns evolve.

  • ✅ Delivery ETA accuracy 92%+
  • ✅ Fleet optimization ML live in 6 weeks
  • ✅ Real-time model updates on route changes
📡
Telecom & SaaS

MLOps for churn prediction, network anomaly detection, and usage-based pricing models — serving millions of events per second at low cost.

  • ✅ Churn prediction model 88% accurate
  • ✅ Network anomaly detection real-time
  • ✅ 10M+ events/sec inference pipeline

Ready to Deploy Your AI Models?

Stop letting great models languish in notebooks. Get them to production in weeks, not months.

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