ML Infrastructure That Handles Production Load
Build MLOps pipelines, model serving, and monitoring systems that are scalable and accurate under load.
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Trusted and top rated tech team
Engineering ML systems for production
Machine learning fails in production without proper infrastructure. Models require automated deployment pipelines, scalable serving architecture, and monitoring that detects drift before accuracy degrades. Our teams build MLOps platforms that integrate with existing systems, handle traffic spikes, and maintain model performance without constant manual intervention.
Our capabilities include:
- Automated model deployment and versioning
- Scalable inference serving architecture
- Model performance monitoring and alerting
- Automated retraining workflows
- Data pipeline engineering and validation
- Production environment integration
Who we support
We work with engineering teams where ML models show promise in development but lack the infrastructure to deploy reliably, scale with traffic, or maintain accuracy in production environments.
Engineering Teams Scaling ML Capabilities
Your data scientists build promising models, but moving them to production takes months. You need automated deployment pipelines, monitoring systems, and infrastructure that can support multiple models without dedicated engineers for each one.
SaaS Platforms Adding ML Features
Your product roadmap includes recommendation engines, predictive features, or automated decisions. You lack MLOps expertise to deploy models that serve predictions at scale, handle traffic spikes, and maintain accuracy as user patterns change.
Enterprises With Legacy Infrastructure
Your ML initiatives stall because models can't integrate with existing databases, APIs, and security requirements. You need infrastructure that deploys models within current tech constraints without platform rewrites or architectural disruptions.
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?
ML infrastructure succeeds when deployment is automated, serving scales with demand, and monitoring catches issues before users notice. Our teams build MLOps pipelines that integrate with existing databases and APIs without platform rewrites. You get production-ready infrastructure that handles real traffic rather than proof-of-concept systems that collapse under load.
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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.
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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.
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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
Automated Model Deployment
Scalable Inference Architecture
Model Performance Monitoring
Automated Retraining Pipelines
Feature Store Management
Model Versioning & Registry
Tools & technologies for ML infrastructure
MLOps Platforms & Orchestration
Workflow tools automate training pipelines, manage dependencies, and coordinate deployment across development and production.
- Kubeflow — Kubernetes-native ML toolkit that orchestrates training pipelines, manages hyperparameter tuning, and coordinates deployments across distributed clusters
- Apache Airflow — Workflow scheduler that automates ML pipeline execution, manages task dependencies, and monitors job completion with visual DAG interface
- MLflow — Open-source platform for tracking experiments, packaging trained artifacts, and managing deployment lifecycle with centralized registry capabilities
- Prefect — Dataflow automation framework with dynamic task generation, failure handling, and monitoring for complex ML workflow orchestration
- Argo Workflows — Container-native orchestration engine for Kubernetes that runs parallel ML jobs with dependency management and resource optimization
- Metaflow — Netflix’s workflow framework that simplifies pipeline development with automatic versioning, scalable compute, and production deployment paths
Model Serving & API Infrastructure
Serving platforms deliver predictions with low latency, batch requests, and scale inference workloads automatically.
- TensorFlow Serving — Production serving system with versioning, request batching, and GPU acceleration for deploying TensorFlow predictions at scale
- TorchServe — PyTorch inference server with multi-framework support, A/B testing capabilities, and metrics logging for production PyTorch deployments
- NVIDIA Triton — Inference platform that optimizes serving across CPUs and GPUs with dynamic batching, concurrent execution, and multi-framework support
- FastAPI — Modern Python web framework for building high-performance ML APIs with automatic documentation, type validation, and async request handling
- Seldon Core — Kubernetes-native serving platform with canary deployments, explainability features, and advanced routing for complex inference workflows
- KServe — Serverless inference platform on Kubernetes with autoscaling, traffic splitting, and standardized interfaces for deploying ML workloads efficiently
Containerization & Deployment
Container platforms package applications with dependencies and orchestrate deployments with auto-scaling and load balancing.
- Docker — Container platform that packages ML applications with dependencies, ensuring consistent environments across development, testing, and production stages
- Kubernetes — Container orchestration system that manages deployments with auto-scaling, load balancing, rolling updates, and self-healing capabilities
- Helm — Kubernetes package manager that simplifies ML application deployment with templated configurations, version control, and dependency management
- Amazon EKS — Managed Kubernetes service that runs ML workloads on AWS with integrated security, monitoring, and automatic infrastructure scaling
- Google GKE — Google’s managed Kubernetes platform with GPU support, preemptible instances, and tight integration with Google Cloud AI services
- Azure AKS — Microsoft’s Kubernetes service with enterprise security, hybrid deployment options, and seamless Azure Machine Learning integration
Monitoring & Observability
Monitoring systems track inference performance, detect data drift, and alert teams before accuracy impacts business outcomes.
- Prometheus — Open-source monitoring system that collects metrics from inference services with flexible querying, alerting rules, and long-term storage capabilities
- Grafana — Visualization platform that creates dashboards displaying prediction latency, throughput, error rates, and business KPIs in real-time
- Evidently AI — Drift detection tool that identifies data quality issues, feature distribution changes, and prediction accuracy degradation in production systems
- Weights & Biases — Production monitoring platform that tracks inference performance, visualizes prediction trends, and alerts teams to anomalies and failures
- DataDog — Cloud monitoring service with APM, distributed tracing, log aggregation, and custom metrics for comprehensive ML infrastructure observability
- WhyLabs — ML observability platform that monitors data quality, detects drift, and profiles predictions without storing sensitive data locally
Feature Engineering & Data Pipelines
Pipeline frameworks process data at scale, validate quality, and ensure consistent features between training and inference.
- Apache Spark — Distributed computing framework that processes large datasets across clusters with parallel execution, SQL support, and built-in ML libraries
- Apache Kafka — Streaming platform for real-time data ingestion with high-throughput message delivery, fault tolerance, and exactly-once processing guarantees
- Feast — Feature store that manages feature definitions, serves features with low latency, and maintains consistency between training and inference
- Tecton — Enterprise feature platform with real-time and batch pipelines, feature versioning, and monitoring for production ML feature management
- dbt — Data transformation tool that builds analytics pipelines with SQL, version control, testing, and documentation for reproducible feature engineering
- Great Expectations — Data validation framework that tests pipeline outputs, detects quality issues, and prevents bad data from reaching inference systems
Experiment Tracking & Model Registry
Tracking platforms log experiments, version artifacts, and manage deployment with centralized registries for governance.
- MLflow Tracking — Experiment logging system that records parameters, metrics, and artifacts with comparison views for evaluating training runs and hyperparameters
- Neptune.ai — Metadata store for versioning experiments, tracking training progress, and comparing results across teams with collaborative features
- Weights & Biases — Experiment platform with real-time visualization, hyperparameter optimization, and team collaboration tools for ML research and development
- Comet ML — Tracking system that logs code, hyperparameters, and metrics with automatic experiment comparison and production monitoring integration
- DVC — Data version control tool that tracks datasets, pipelines, and experiments with Git-like workflows for reproducible ML development
- MLflow Model Registry — Centralized repository for managing deployment lifecycle with versioning, stage transitions, and audit trails for compliance requirements
FAQs about production ML infrastructure
How do you deploy models to production?
We create automated deployment pipelines with validation, containerization, and CI/CD. Models move through staging to production, including performance checks, integration testing, and rollback for issues.
What does scalable model serving require?
Serving infrastructure needs load balancing, auto-scaling, request batching, and caching to handle traffic spikes without slowing down. We design architectures for horizontal scaling, GPU optimization, and failover redundancy for high availability.
How do you detect and handle model drift?
We set up real-time monitoring for prediction accuracy, feature distributions, and data quality. If drift is detected, automated alerts trigger investigations and retraining to maintain accuracy as patterns change.
Can MLOps integrate with legacy systems?
Yes, we build serving APIs and data pipelines that connect to existing databases, message queues, and applications without requiring platform migrations. We ensure security, data governance, and fit within current architecture.
How long does implementation take?
Basic deployment and monitoring take 8-10 weeks. Full MLOps platforms with automated retraining, feature stores, and multi-environment orchestration take 16-24 weeks, depending on complexity and existing infrastructure.
Do you provide ongoing maintenance?
Yes, we offer retainer support for pipeline optimization, performance tuning, and updates. Our teams are familiar with your MLOps platform for quick troubleshooting and capacity adjustments.
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.
