MLOps Solutions

From Experimentation to Production at Scale

Machine learning holds tremendous promise for competitive advantage, but the gap between a successful prototype and a production system is vast. Modern ML/AI operations require sophisticated infrastructure, robust processes, and deep expertise.

IS Nordic's MLOps solutions bridge the gap between data science and operations. We provide the infrastructure, practices, and expertise to move machine learning from research to a sustainable, governed, enterprise capability.

Challenges We Solve

Most organizations struggle with critical MLOps challenges that prevent them from realizing the full value of their AI investments:

• Siloed data science teams disconnected from operations and production systems
• Models that perform well in notebooks but fail when deployed to production
• Lack of monitoring, governance, and explainability for deployed models
• Difficulty scaling from pilot projects to enterprise-wide ML systems
• Regulatory and ethical concerns around AI model deployment and bias
• Manual processes for model deployment leading to errors and delays
• Inability to detect and respond to model drift and performance degradation

Our comprehensive MLOps platform addresses these challenges with proven infrastructure patterns and operational best practices developed from years of enterprise experience.

Comprehensive MLOps Platform & Services

Development Infrastructure

• Shared data science environments with Jupyter, RStudio, and VS Code
• GPU/TPU compute resources for model training at scale
• Feature store and data pipeline management with versioning
• Experiment tracking and comprehensive model registry
• Version control integration with GitHub, GitLab, and Bitbucket
• Collaborative workspace for data science teams

Model Deployment & Serving

• Containerized model serving with Kubernetes orchestration
• Real-time inference APIs with load balancing and auto-scaling
• Batch prediction pipelines for large-scale inference workloads
• A/B testing and canary deployments for safe model rollouts
• Model versioning with instant rollback capabilities
• Multi-region deployment for high availability and low latency

Monitoring & Operations

• Real-time model performance monitoring and drift detection
• Data quality monitoring and feature distribution tracking
• Automated alerts for model degradation and anomalies
• Explainability and interpretability tools for model insights
• Comprehensive audit trails for regulatory compliance
• Resource utilization tracking and cost optimization

Governance & Responsible AI

• Model governance frameworks with approval workflows
• Fairness and bias detection across the entire model lifecycle
• GDPR compliance for personal data in training and inference
• Model explainability to meet regulatory requirements
• Responsible AI checklists and ethical review processes
• Documentation and lineage tracking for all model artifacts

Common Use Cases

Predictive Analytics

Demand forecasting, churn prediction, customer lifetime value—models that drive critical business decisions require reliable infrastructure, robust governance, and continuous monitoring to ensure accuracy and compliance.

Recommendation Engines

Real-time personalization at scale requires sophisticated data pipelines, low-latency serving infrastructure, and continuous model improvement through A/B testing and feedback loops.

Anomaly Detection

Fraud detection, quality monitoring, operational alerts—anomaly detection models require robust serving infrastructure with guaranteed uptime, real-time inference capabilities, and sensitive alert management.

Computer Vision & NLP

Image recognition, document processing, sentiment analysis—complex deep learning models demanding GPU infrastructure, high throughput serving, and careful governance for ethical deployment.

Time Series Forecasting

Financial modeling, supply chain optimization, predictive maintenance—time series models requiring sophisticated feature engineering, retraining automation, and drift detection.

Our MLOps Technology Stack

We build on proven, open-source foundations with enterprise support, customization, and integration capabilities:

Why IS Nordic for MLOps

We understand that successful MLOps isn't just about technology—it's about people, processes, and culture. Our team works with your data scientists, engineers, and business stakeholders to create a sustainable ML practice that delivers real business value.