Comprehensive ML Engineering Solutions
From infrastructure setup to optimization and real-time systems, we provide complete engineering services for production machine learning.
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Our Service Approach
Our services are built on the principle that machine learning systems require the same engineering discipline as any critical software infrastructure. We don't just deploy models; we build complete systems with proper testing, monitoring, and operational procedures.
Each engagement begins with understanding your current state and desired outcomes. We assess existing infrastructure, data pipelines, team capabilities, and organizational constraints. This assessment informs our technical approach and ensures recommendations align with your actual needs rather than theoretical ideals.
Our methodology combines software engineering best practices with machine learning specific requirements. This includes version control for models and data, automated testing across the pipeline, continuous integration for model training, and comprehensive monitoring for production systems. We also emphasize documentation and knowledge transfer, ensuring your team can maintain systems after our engagement.
We work collaboratively with your data science and engineering teams. Rather than operating in isolation, we integrate with existing workflows and share knowledge throughout the process. The goal is not just to deliver working systems but to build internal capability that persists beyond the project timeline.
Assessment Phase
Understand requirements, constraints, and current capabilities
Implementation Phase
Build systems with proper engineering practices and testing
Transfer Phase
Documentation, training, and ongoing support for sustainability
Our Services
MLOps Infrastructure Setup
Establish a robust machine learning operations framework that streamlines your model lifecycle from development to production. Our comprehensive infrastructure service includes setting up version control for datasets and models, implementing continuous integration pipelines for model training, and establishing automated testing frameworks.
Key Components:
- Version control systems for models, data, and configurations
- CI/CD pipelines for automated training and deployment
- Monitoring and alerting infrastructure
- Containerization and orchestration setup
- Model registry and feature store implementation
Expected Outcomes:
Complete MLOps infrastructure supporting the full model lifecycle. Teams can train, test, and deploy models consistently across environments. Automated monitoring detects performance issues early. Documentation enables your team to operate and extend the system independently.
Model Optimization and Acceleration
Enhance the performance of your existing machine learning models through systematic optimization techniques. This service focuses on reducing inference time, minimizing resource consumption, and improving accuracy through advanced methods.
Optimization Techniques:
- Quantization and pruning for model size reduction
- Knowledge distillation for efficient models
- Hardware acceleration (GPU, TPU optimization)
- Batch processing and caching strategies
- Hyperparameter optimization and ensemble methods
Expected Outcomes:
Models achieve 2-4x faster inference while maintaining accuracy. Reduced resource requirements lower operational costs. Optimized models enable real-time applications previously constrained by latency. Performance profiling identifies and eliminates bottlenecks systematically.
Real-time ML System Development
Build sophisticated real-time machine learning systems that process streaming data and deliver instantaneous predictions. Our service covers the entire spectrum from data ingestion to serving predictions with minimal latency.
System Components:
- Stream processing architecture and data ingestion
- Feature stores for consistent computation
- Online learning and model adaptation capabilities
- High-availability architecture with load balancing
- A/B testing and continuous improvement frameworks
Expected Outcomes:
Complete real-time ML system delivering sub-second predictions. Architecture scales horizontally to handle traffic growth. Fault tolerance ensures continuous operation despite component failures. Monitoring dashboards provide visibility into system health and performance metrics.
Service Comparison and Selection Guide
| Feature | MLOps Infrastructure | Model Optimization | Real-time Systems |
|---|---|---|---|
| Primary Focus | Infrastructure & processes | Performance improvement | Low-latency deployment |
| Timeline | 6-8 weeks | 4-6 weeks | 8-10 weeks |
| Investment | €7,200 | €4,600 | €8,500 |
| Best For | Starting ML operations | Improving existing models | Streaming applications |
| Prerequisites | Basic cloud infrastructure | Existing trained models | Clear latency requirements |
| Team Training | Comprehensive | Focused on techniques | Operations-focused |
Choosing the Right Service
Start with MLOps Infrastructure if you're deploying your first production ML models or struggling with manual deployment processes. This foundation supports all future ML initiatives.
Choose Model Optimization if you have working models but face performance constraints. This is particularly valuable when compute costs are high or latency requirements are strict.
Select Real-time Systems when your application requires immediate predictions on streaming data. This is common in fraud detection, recommendation engines, and real-time analytics.
Many organizations benefit from combining services. For example, establishing MLOps infrastructure first, then optimizing models, and finally building real-time capabilities. We can discuss a phased approach during consultation.
Technology Stack and Tools
ML Frameworks
TensorFlow & Keras: Deep learning and neural networks
PyTorch: Research and production deployment
Scikit-learn: Traditional ML algorithms
XGBoost & LightGBM: Gradient boosting implementations
MLOps Platforms
Kubeflow: End-to-end ML workflows
MLflow: Experiment tracking and model registry
Airflow: Pipeline orchestration
DVC: Data version control
Infrastructure
Docker & Kubernetes: Containerization and orchestration
Terraform: Infrastructure as code
AWS/GCP/Azure: Cloud platforms
Redis & PostgreSQL: Caching and storage
Monitoring & Analytics
Prometheus & Grafana: Metrics and visualization
ELK Stack: Log aggregation and analysis
Evidently AI: ML monitoring and drift detection
Great Expectations: Data validation
Technology Selection Philosophy
We select tools based on your specific requirements rather than following trends. Established, well-supported technologies are preferred over cutting-edge options unless there's clear justification. The goal is sustainable systems that your team can maintain.
We also consider your existing technology stack and team expertise. Introducing new tools requires training and adaptation time, so we balance innovation with pragmatism. The best technology is the one that solves your problem reliably.
Service Packages and Combinations
Complete ML Platform
Comprehensive solution combining MLOps infrastructure, optimization capabilities, and real-time deployment. Suitable for organizations building their ML platform from the ground up.
Includes:
- Full MLOps infrastructure
- Model optimization framework
- Real-time serving capabilities
- Extended training and support
Benefits:
- Integrated solution
- 15% combined discount
- Unified architecture
- 12-week implementation
Infrastructure + Optimization
Establish solid MLOps foundation and optimize existing models for production deployment. Ideal for teams transitioning from prototype to production.
Includes:
- MLOps infrastructure setup
- Model optimization service
- Performance benchmarking
- Team training sessions
Benefits:
- Faster deployment
- 10% combined discount
- Optimized performance
- 8-week timeline
Optimization + Real-time
Optimize models and deploy them in real-time systems with minimal latency. Perfect for applications requiring immediate predictions on streaming data.
Includes:
- Model optimization service
- Real-time system development
- Latency optimization
- Load testing and tuning
Benefits:
- Maximum performance
- 10% combined discount
- Sub-second latency
- 10-week timeline
Ready to Get Started?
Let's discuss your machine learning engineering needs and determine which service best fits your requirements.