Machine Learning That Actually Ships
Develop ML platforms that integrate with existing systems, handle real workloads, and ship reliable predictions.
👋 Get ML architecture guidance.
Trusted and top rated tech team
Production-ready ML development
Machine learning that ships requires more than data science expertise. You need engineers who understand production environments, reliable data pipelines, and scalable MLOps. Our teams build ML infrastructure that integrates with existing systems, handles large data volumes, and maintains model performance in production.
Our capabilities include:
- Real-time prediction serving
- Automated model retraining
- Data quality assurance
- Scalable inference architecture
- Cross-platform model deployment
- Production performance optimization
Who we support
Curotec helps engineering leaders build production-ready ML platforms that integrate with existing infrastructure. From prototypes to prediction platforms, we create machine learning solutions that solve real business challenges and integrate with your existing systems.
Growing Tech Organizations
Need to operationalize data science work? We help scaling teams build infrastructure, implement automated training pipelines, and create monitoring tools that turn experimental models into reliable business tools.
Data-Driven Product Companies
We work with your existing tech stack: APIs, databases, and cloud platforms. Our engineers build production-grade systems that process real-time data, serve predictions at scale, and integrate with your product.
Enterprise Engineering Teams
Deploy models in production environments. Our engineers build robust MLOps pipelines, implement monitoring, and create infrastructure that handles large data volumes while maintaining model accuracy in production.
Ways to engage
We offer a wide range of engagement models to meet our clients’ needs. From hourly consultation to fully managed solutions, our engagement models are designed to be flexible and customizable.
Staff Augmentation
Get access to on-demand product and engineering team talent that gives your company the flexibility to scale up and down as business needs ebb and flow.
Retainer Services
Retainers are perfect for companies that have a fully built product in maintenance mode. We'll give you peace of mind by keeping your software running, secure, and up to date.
Project Engagement
Project-based contracts that can range from small-scale audit and strategy sessions to more intricate replatforming or build from scratch initiatives.
We'll spec out a custom engagement model for you
Invested in creating success and defining new standards
At Curotec, we do more than deliver cutting-edge solutions — we build lasting partnerships. It’s the trust and collaboration we foster with our clients that make CEOs, CTOs, and CMOs consistently choose Curotec as their go-to partner.
Why choose Curotec for machine learning?
We build ML platforms that integrate with existing infrastructure without disrupting your workflows. We deliver MLOps pipelines, data engineering, and model monitoring that support real business operations. You get technical competence without vendor overhead.
1
Extraordinary people, exceptional outcomes
Our outstanding team represents our greatest asset. With business acumen, we translate objectives into solutions. Intellectual agility drives efficient software development problem-solving. Superior communication ensures seamless teamwork integration.
2
Deep technical expertise
We don’t claim to be experts in every framework and language. Instead, we focus on the tech ecosystems in which we excel, selecting engagements that align with our competencies for optimal results. Moreover, we offer pre-developed components and scaffolding to save you time and money.
3
Balancing innovation with practicality
We stay ahead of industry trends and innovations, avoiding the hype of every new technology fad. Focusing on innovations with real commercial potential, we guide you through the ever-changing tech landscape, helping you embrace proven technologies and cutting-edge advancements.
4
Flexibility in our approach
We offer a range of flexible working arrangements to meet your specific needs. Whether you prefer our end-to-end project delivery, embedding our experts within your teams, or consulting and retainer options, we have a solution designed to suit you.
Machine learning engineering capabilities
MLOps Pipeline Development
Data Pipeline Architecture
Model Serving Infrastructure
Performance Monitoring
Business System Integration
Model Lifecycle Management
Machine learning technology stack
ML Frameworks and Libraries
Curotec implements machine learning frameworks based on your use case, performance needs, and infrastructure.
- TensorFlow: Google’s open-source framework for building and deploying ML models at scale, with strong support for distributed training and production serving.
- PyTorch: Facebook’s dynamic neural network framework preferred for research and rapid prototyping, with growing production deployment capabilities.
- Scikit-learn: Python library for traditional machine learning algorithms including classification, regression, and clustering for structured data problems.
- XGBoost: Gradient boosting framework optimized for speed and performance, particularly effective for tabular data and structured prediction tasks.
- Keras: High-level neural network API that simplifies deep learning model development while maintaining flexibility for complex architectures.
- Hugging Face Transformers: Pre-trained models and tools for natural language processing tasks including text classification, generation, and embedding creation.
MLOps and Deployment Platforms
We automate machine learning operations, including model training, versioning, and deployment across development and production.
- MLflow: Open-source platform for managing the complete machine learning lifecycle including experiment tracking, model registry, and deployment management.
- Kubeflow: Kubernetes-native ML workflows that orchestrate training pipelines, hyperparameter tuning, and model serving across distributed infrastructure.
- Apache Airflow: Workflow orchestration platform for scheduling and monitoring complex ML pipeline dependencies and data processing tasks.
- Docker: Containerization technology that ensures consistent model environments across development, staging, and production deployments.
- Jenkins: Continuous integration platform for automating model testing, validation, and deployment processes with custom pipeline configurations.
- GitLab CI/CD: Integrated version control and deployment pipelines that manage code, data, and model versioning with automated testing gates.
Data Processing and Feature Engineering
Our engineers build scalable data pipelines that manage large-scale data and ensure consistent feature quality for model training and inference.
- Apache Spark: Distributed computing framework for processing large datasets across clusters with built-in machine learning libraries and streaming capabilities.
- Pandas: Python data manipulation library for data cleaning, transformation, and analysis with extensive support for structured data formats.
- Dask: Parallel computing library that scales pandas and NumPy workflows to larger-than-memory datasets with minimal code changes.
- Apache Kafka: Distributed streaming platform for real-time data ingestion and processing with high-throughput, fault-tolerant message delivery.
- Feature Store: Centralized repositories like Feast or Tecton for managing, sharing, and serving consistent features across training and inference environments.
- Apache Beam: Unified programming model for batch and stream processing that runs on multiple execution engines including Spark and Dataflow.
Model Serving and API Infrastructure
We build production serving platforms that deliver ML predictions with production-grade reliability, performance, and scalability.
- FastAPI: Modern Python web framework for building high-performance ML APIs with automatic documentation, type validation, and async support.
- TensorFlow Serving: Production-ready serving system for TensorFlow models with versioning, batching, and GPU acceleration capabilities.
- Kubernetes: Container orchestration platform that manages ML model deployments with auto-scaling, load balancing, and rolling updates.
- NVIDIA Triton: Inference server that optimizes model serving across CPUs and GPUs with dynamic batching and multi-framework support.
- Redis: In-memory data store for caching model predictions, feature vectors, and session data to reduce inference latency.
- AWS Lambda: Serverless computing service for lightweight model inference with automatic scaling and pay-per-request pricing.
Monitoring and Observability Tools
Curotec implements comprehensive monitoring solutions that track model performance, data quality, and infrastructure health in real-time.
- Prometheus: Open-source monitoring system that collects metrics from applications and infrastructure with flexible querying and alerting capabilities.
- Grafana: Visualization platform for creating dashboards that display model performance metrics, system health, and business KPIs in real-time.
- DataDog: Cloud monitoring service that provides comprehensive observability for applications with APM, logs, and infrastructure monitoring.
- Evidently AI: Monitoring tool that detects data drift, model performance degradation, and prediction quality issues in production systems.
- Weights & Biases: Experiment tracking and model monitoring platform that visualizes training metrics, hyperparameters, and production model behavior.
- ELK Stack: Elasticsearch, Logstash, and Kibana for centralized logging, search, and analysis of application logs and prediction data.
Cloud ML Platforms and Infrastructure
We design cloud-native ML architectures using managed services while controlling costs, security, and deployment flexibility.
- AWS SageMaker: Fully managed platform for building, training, and deploying models with built-in algorithms, notebook instances, and automated scaling.
- Google Cloud AI Platform: End-to-end service with AutoML capabilities, custom training, and prediction serving integrated with Google’s data ecosystem.
- Azure Machine Learning: Cloud-based environment for training, deploying, and managing models with MLOps capabilities and business security features.
- Databricks: Unified analytics platform that combines data engineering, machine learning, and collaborative notebooks with optimized Spark and Delta Lake storage.
- Vertex AI: Google’s unified platform that streamlines model development with pre-trained APIs, custom training, and managed inference endpoints.
- Amazon EC2: Scalable compute instances optimized for machine learning workloads with GPU support, spot pricing, and flexible instance configurations.
FAQs about our ML development
How do you move models from prototype to production?
We build MLOps pipelines that automate testing, deployment, and monitoring. This includes containerization, CI/CD, and performance validation to ensure models work reliably at scale.
What does it take to implement MLOps at scale?
We design automated workflows for model training, versioning, and deployment. This includes data pipeline architecture, monitoring, and governance frameworks that support multiple teams and models.
How do you handle data drift and model degradation?
We implement monitoring systems that detect when model performance drops due to changing data. Our solutions include automated retraining, A/B testing, and alerting for performance issues.
Can ML platforms integrate with existing infrastructure?
Yes, we build ML platforms that connect to your current databases, APIs, and business applications. Our integrations maintain data governance, security, and don’t disrupt existing workflows.
How long does infrastructure implementation take?
Basic MLOps pipelines deploy in 6-8 weeks. Comprehensive ML platforms with monitoring and governance take 16-20 weeks. Timeline depends on integration complexity and existing infrastructure.
Do you provide ongoing ML system maintenance?
Yes, we offer retainer support for model retraining, performance monitoring, and infrastructure updates. Our teams maintain familiarity with your ML systems for rapid troubleshooting and optimization.
Ready to have a conversation?
We’re here to discuss how we can partner, sharing our knowledge and experience for your product development needs. Get started driving your business forward.
