MLOps Engineering for Reliable ML Systems

Get models from notebooks to production with automated deployment, real-time monitoring, and continuous retraining that maintains performance.

👋 Talk to an MLOps expert.

Trusted and top rated tech team

Data science teams build models that never reach production

Notebooks contain accurate models but deployment infrastructure doesn’t exist. Manual deployment takes weeks, monitoring doesn’t track drift, and retraining requires engineering intervention. DevOps teams don’t understand ML requirements, data scientists can’t operationalize work, and models degrade silently. We build MLOps infrastructure with automated pipelines, drift detection, and continuous retraining so models reach production quickly and maintain performance without manual intervention.

Our capabilities include:

Automated model deployment and CI/CD pipelines
Model monitoring and drift detection systems
Continuous retraining workflow automation
Model versioning and experiment tracking
Production serving infrastructure at scale
MLOps platform implementation and integration

Who we support

Model deployment shouldn’t require months of infrastructure work. We help data science teams implement production MLOps infrastructure with automated deployment, monitoring, and retraining so models move from development to production reliably without building internal MLOps expertise.

Teams Without Deployment Infrastructure

Your data scientists build accurate models in notebooks but lack deployment pipelines to reach production. Manual handoffs to engineering create bottlenecks, model updates take weeks or months, and technical debt accumulates as workarounds replace proper infrastructure. Production deployment remains the exception rather than standard workflow.

Models Running Without Monitoring

Your models run in production but performance degrades silently as data patterns shift. No automated drift detection alerts teams to problems, retraining requires manual intervention each cycle, and you discover model failures only after business impact occurs rather than through proactive monitoring systems.

Engineers Managing ML Manually

Your DevOps engineers handle model deployment manually without understanding ML-specific requirements. Models are treated like standard applications, versioning doesn't track experiments properly, rollbacks break production systems, and engineers spend excessive time supporting data science workflows they don't fully understand.

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.

Helping a Series B SaaS company refine and scale their product efficiently

Why choose Curotec for MLOps engineering?

Our engineers build automated deployment pipelines, implement drift detection systems, and configure continuous retraining workflows. We integrate MLOps platforms like MLflow and Kubeflow with your infrastructure, establish model versioning, and create monitoring dashboards. You get production ML infrastructure that deploys models reliably and maintains performance without hiring MLOps specialists.

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.

MLOps capabilities for production machine learning

Pipeline Automation Configuration

Configure Jenkins or GitHub Actions to automatically test, validate, and deploy models whenever you commit code, ensuring seamless updates from development to production.

Real-Time Prediction API Development

Build REST or gRPC prediction endpoints handling thousands of requests per second with load balancing and caching so applications access model predictions reliably.

Experiment Tracking System Setup

Implement MLflow or Weights & Biases tracking every training run's parameters, metrics, and artifacts so data scientists compare experiments and reproduce winning models.

Drift Alert Configuration

Set up Evidently AI or Arize monitoring pipelines detecting data distribution shifts and performance degradation with Slack or email alerts triggering team response.

Feature Pipeline Engineering

Build feature transformation pipelines ensuring training and production data undergo identical preprocessing so models receive consistent inputs across environments.

Model Rollback Infrastructure

Implement versioned model deployments with instant rollback capabilities so teams revert to previous model versions when production issues occur without service downtime.

Infrastructure for MLOps implementation

Model Training & Experiment Tracking

Our engineers implement experiment tracking platforms that version models, log parameters, and compare training runs systematically.

MLflow — Open-source platform tracking experiments, packaging models, and managing deployment lifecycle with reproducible training runs
Weights & Biases — Experiment tracking system visualizing model performance, hyperparameter optimization, and collaborative experiment management
Neptune.ai — Metadata store logging model versions, datasets, and training artifacts with team collaboration and comparison features
Comet ML — Experiment management platform tracking code changes, hyperparameters, and metrics across distributed training runs
TensorBoard — Visualization toolkit monitoring training progress, analyzing model graphs, and profiling performance for TensorFlow workflows
Aim — Lightweight experiment tracker designed for fast logging and comparison of thousands of training runs efficiently

Model Deployment & Serving

We deploy model serving infrastructure handling prediction requests with low latency, autoscaling, and version management capabilities.

TensorFlow Serving — Production serving system for TensorFlow models with gRPC and REST APIs supporting versioning and batching
TorchServe — PyTorch model server providing RESTful endpoints, multi-model serving, and metrics collection for production deployments
BentoML — Framework packaging ML models into production APIs with containerization and deployment automation across platforms
Seldon Core — Kubernetes-native platform deploying, scaling, and monitoring ML models with advanced inference graphs and explainability
KServe — Serverless inference platform on Kubernetes enabling autoscaling, canary deployments, and multi-framework model serving
NVIDIA Triton — Inference server optimizing model serving across CPUs, GPUs, and multiple frameworks with dynamic batching

CI/CD for Machine Learning

Curotec builds automated pipelines testing models, validating data quality, and deploying to production with approval workflows.

GitHub Actions — Workflow automation platform triggering model training, testing, and deployment from code repository changes
GitLab CI/CD — Integrated pipeline system automating ML workflows with container support and artifact management
Jenkins — Extensible automation server orchestrating complex ML pipelines with plugin ecosystem for ML-specific tasks
CircleCI — Cloud-native CI/CD platform executing automated tests, model validation, and deployment with parallelization support
Argo Workflows — Kubernetes-native workflow engine managing ML pipeline orchestration with DAG-based execution and artifact passing
Tekton — Cloud-native CI/CD framework building ML pipelines as reusable, declarative components on Kubernetes

Model Monitoring & Observability

Monitoring systems track prediction accuracy, data drift, and model performance with automated alerting for degradation detection.

Evidently AI — Monitoring platform detecting data drift, model degradation, and prediction quality issues in production systems
Arize AI — ML observability platform surfacing model failures, bias patterns, and performance issues with root cause analysis
Fiddler AI — Model monitoring system identifying drift, bias, and data quality problems with explainability for debugging
WhyLabs — Lightweight monitoring tool tracking data quality and model performance without sending raw data externally
Prometheus + Grafana — Metrics collection and visualization stack monitoring model latency, throughput, and resource utilization
Datadog ML Monitoring — Observability platform tracking ML-specific metrics, traces, and logs alongside infrastructure monitoring

Feature Store & Data Management

Feature stores centralize training and serving data ensuring consistency between development and production model environments.

Feast — Open-source feature store providing low-latency feature serving and point-in-time correct training data retrieval
Tecton — Enterprise feature platform managing feature engineering, storage, and serving with real-time and batch support
Hopsworks — Data-intensive AI platform combining feature store, model registry, and workflow orchestration capabilities
AWS SageMaker Feature Store — Managed service storing, sharing, and managing ML features with online and offline access patterns
Vertex AI Feature Store — Google Cloud service providing centralized feature management with low-latency serving and version control
Redis — In-memory data store serving features with microsecond latency for real-time ML prediction applications

Workflow Orchestration

Orchestration platforms manage complex ML workflows coordinating training, evaluation, deployment, and retraining across systems.

Kubeflow Pipelines — ML workflow platform on Kubernetes defining, deploying, and managing end-to-end ML pipelines with reusability
Apache Airflow — Workflow orchestration tool scheduling ML tasks with dependency management and monitoring for complex DAGs
Prefect — Modern workflow orchestration system managing ML pipelines with dynamic task generation and failure handling
Metaflow — Framework building and managing real-world data science projects from prototyping to production deployment
ZenML — MLOps framework creating reproducible pipelines with stack abstraction and experiment tracking integration
Flyte — Workflow automation platform designed for data and ML workflows with strong typing and multi-tenancy support

FAQs about our MLOps engineering services

How long does MLOps implementation take?

Initial pipeline setup takes 4-6 weeks for basic deployment automation. Full MLOps infrastructure with monitoring, retraining, and feature stores takes 3-4 months depending on model complexity and existing infrastructure. We deliver incrementally so teams see value at each phase.

What platforms do you work with?

We implement MLOps using open-source tools like MLflow, Kubeflow, and Airflow, cloud platforms including AWS SageMaker and Google Vertex AI, or hybrid approaches combining multiple technologies. We select tools matching your infrastructure and team preferences.

Can you integrate with our existing ML workflows?

Yes. We build around your current data science workflows rather than forcing process changes. Our engineers integrate deployment pipelines with existing notebooks, training scripts, and model development practices while adding automation and monitoring layers.

How do you handle model monitoring and drift?

We implement monitoring systems tracking prediction accuracy, data distribution shifts, and feature drift. Automated alerts notify teams when performance degrades below thresholds, triggering investigation or automated retraining depending on configured policies.

Do we need MLOps engineers on our team?

No. We build and operate MLOps infrastructure so your data scientists can deploy models without specialized MLOps knowledge. We provide training and documentation, but systems are designed for data science teams to use independently after implementation.

How do you ensure model reproducibility?

We implement version control for models, data, and code with experiment tracking systems logging all training parameters. Each model deployment links to exact training configuration, enabling teams to reproduce results or debug production issues systematically.

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.