What We Build With It
We engineer MLOps pipelines that automate, monitor, and manage your ML models, transforming them into resilient business assets.
Automated ML Training & Retraining Pipelines
Building reproducible, version-controlled pipelines that automatically train, validate, and retrain models based on new data or performance drift.
CI/CD & Governance for ML Models
Implementing specialized CI/CD that treats models, data, and code as first-class citizens, with built-in versioning and registries for full auditability.
Real-Time Model Performance Monitoring
Building dashboards and alerts to track model accuracy, data drift, feature drift, and operational metrics in production, catching issues before they impact users.
Why Our Approach Works
Robust MLOps is the secret ingredient for turning machine learning from a science experiment into a predictable, high-ROI business capability.
Accelerated Time-to-Value for ML
Drastically reduce the time it takes to get models from experimentation to production, ensuring your ML investments start generating value faster.
Consistent Model Reliability
Ensure models perform as expected over time, with automated checks and retraining mechanisms that proactively address data shifts and performance degradation.
Full Transparency & Reproducibility
Every aspect of your model's lifecycle—from data to training to deployment—is versioned, tracked, and auditable, fostering trust and collaboration.
Our Go-To Stack for MLOps Engineering
We leverage a comprehensive set of specialized MLOps tools and cloud services to build robust ML lifecycle automation.
Experiment Tracking
MLflow, Weights & Biases for managing and comparing model experiments and runs.
Feature Stores
Feast, Tecton for consistent feature engineering and serving across training and inference.
ML Orchestration
Kubeflow Pipelines, Dagster, Prefect for defining and executing complex ML workflows.
Model Serving
KServe, Seldon Core, FastAPI, NVIDIA Triton for scalable, low-latency model deployment.
Monitoring & Drift Detection
Prometheus, Grafana, Evidently AI, whylogs for real-time tracking of model performance and data/concept drift.
Data & Model Versioning
DVC (Data Version Control), MLflow Model Registry for tracking and managing data and model artifacts.
Frequently Asked Questions
How is MLOps different from traditional DevOps?
+MLOps extends DevOps principles to machine learning, which has unique challenges: managing code, data, and models. It focuses on automating the entire ML lifecycle, including data validation, model retraining, and drift detection, which aren’t typically part of traditional software CI/CD.
What are the biggest challenges in implementing MLOps?
+Challenges include data and model versioning, ensuring reproducibility of experiments, managing model drift in production, maintaining a consistent feature store, and integrating disparate tools across the ML lifecycle. We specialize in navigating these complexities.
How do you ensure our ML models remain fair and unbiased over time?
+Fairness is a continuous process. We integrate fairness metrics and bias detection tools (e.g., AIF360) into our MLOps pipelines, monitoring for disparate impact in production and triggering alerts or retraining when biases emerge.
What is 'Model Drift' and how do you handle it?
+Model drift happens when the statistical properties of your input data change over time, making your model less accurate. We set up automated monitoring to detect this drift and trigger retraining or human review before performance drops too far.
How often should models be retrained?
+It depends on your data. Some models need retraining daily, while others remain stable for months. We implement automated ‘retraining triggers’ based on performance degradation or the arrival of a significant amount of new data.
What is the ROI of investing in MLOps?
+MLOps drastically reduces the ’time to production’ for ML models and minimizes the risk of costly model failures. It allows you to scale from one or two models to dozens or hundreds without a linear increase in engineering effort.