We operationalise your machine learning workflows. Training pipelines, experiment tracking, model serving and drift monitoring — built for production scale on AWS, GCP or Kubernetes.
Book a Free MLOps AuditMost ML models never make it to production — or if they do, they silently degrade. MLOps closes that gap.
Data scientists build models in Jupyter notebooks. Without MLOps, those models never reach production in a reliable, reproducible way.
If you can't reproduce a training run, you can't debug a bad model. We build versioned, parameterised pipelines that run the same way every time.
Production data changes over time and model accuracy silently degrades. Without drift monitoring, you find out when users complain.
Teams run hundreds of experiments but can't compare results, reproduce the best model or audit what went into a production model.
Deploying a new model version is a manual, error-prone process. We automate it with CI/CD for models — including rollback on performance regression.
Unoptimised training jobs and idle GPU clusters cost tens of thousands per month. We right-size training jobs and use spot instances where safe.
End-to-end MLOps infrastructure — from raw data to production model serving.
Automated, versioned training pipelines with data validation, feature engineering and model evaluation steps. Built on Kubeflow Pipelines, SageMaker Pipelines or Vertex AI Pipelines.
MLflow or Weights & Biases integration — every training run logged with parameters, metrics, artifacts and environment. Full reproducibility and comparison of hundreds of experiments.
Centralised model registry with staging/production promotion workflows, lineage tracking and rollback capability. Never lose a model version again.
Low-latency, high-throughput model APIs using Seldon Core, BentoML, TorchServe or TF Serving — deployed on Kubernetes with autoscaling and canary deployments.
Evidently AI or Arize integration — continuous monitoring of prediction distributions, feature drift and data quality with automated retraining triggers.
Feast or Tecton integration for centralised feature management — consistent features between training and serving, point-in-time correct data and feature sharing across teams.
Cost-optimised GPU infrastructure on AWS, GCP or Azure. Spot/preemptible instance training, autoscaling clusters, CUDA environment management and job scheduling.
Continuous training and continuous delivery for models — automated retraining on data drift, A/B testing of model versions and shadow mode deployment before full rollout.
We recommend the right platform for your cloud provider, team size and model complexity — no vendor bias.
End-to-end ML on AWS — SageMaker Pipelines, Model Registry, Feature Store, Clarify and Model Monitor.
Google Cloud ML platform — Vertex Pipelines, Experiments, Feature Store, Model Monitoring and Workbench.
Kubernetes-native ML workflows — Kubeflow Pipelines, Katib (hyperparameter tuning) and KServe for model serving.
Open-source experiment tracking, model registry and project management. We self-host or integrate with Databricks.
Production model serving with A/B testing, canary deployments, drift detection and explainability integration.
Production ML monitoring — data drift, model performance degradation, data quality and feature attribution tracking.
MLOps (Machine Learning Operations) applies DevOps principles to the machine learning lifecycle. It covers training pipelines, experiment tracking, model deployment, serving infrastructure and continuous monitoring — bridging the gap between data science experimentation and production-ready ML systems.
A foundational setup — training pipeline, experiment tracking and model registry — typically takes 2–4 weeks. Full production infrastructure including model serving, A/B testing and drift monitoring takes 4–8 weeks depending on your existing stack.
We have production experience with AWS SageMaker, GCP Vertex AI, Kubeflow, MLflow, Seldon Core, BentoML, Feast and Evidently AI. We recommend the right platform based on your existing cloud provider and team structure.
Not necessarily. Managed platforms like AWS SageMaker and GCP Vertex AI are strong alternatives for teams not running Kubernetes. Kubeflow is an excellent option for teams already on Kubernetes. We recommend the right fit for your infrastructure.
Model drift occurs when production data distribution shifts away from your training data, causing prediction quality to silently degrade. Without automated drift monitoring, a model can fail in production for weeks before anyone notices. LitDevs implements alerting so you catch drift before it impacts users.