Machine Learning Engineering Services
CoderAxo provides end-to-end machine learning engineering services for teams that need production-grade ML systems. From data pipelines and model training to MLOps and monitoring, we help enterprises deploy reliable, scalable machine learning that delivers measurable business outcomes.
Training a machine learning model is only the first step; the real challenge lies in deploying, scaling, and maintaining it in a production environment. CoderAxo provides machine learning engineering and MLOps services for teams that need resilient AI systems. We help you move models from notebooks into production-grade APIs. We focus heavily on minimizing inference latency, optimizing cloud GPU host costs, and automating data cleaning and annotation workflows. By implementing version control for datasets (DVC) and model metrics (MLflow), we guarantee that every deployment is reproducible, secure, and ready to deliver real business outcomes.
Why choose CoderAxo for ml engineering
We automate model packaging, training runs, versioning (DVC), and production registry updates.
We optimize model sizes using quantization and pruning, cutting GPU hosting costs by up to 50%.
Set up active telemetry to detect when model accuracy drops due to changes in real-world data distributions.
We design reliable ETL jobs, data cleaning steps, and high-speed feature stores using PostgreSQL and Redis.
Package models in GPU-optimized Docker containers, ready to scale on Kubernetes clusters.
Our ml engineering process
Data Infrastructure Audit
We audit your data storage, clean records, build ingestion schemas, and evaluate GPU hardware requirements.
Custom Training & Quantization
We train models, evaluate accuracy metrics, compress sizes, and check performance limits under GPU settings.
MLOps Deployment Pipelines
We build deployment scripts, set up MLflow metrics registries, and integrate real-world database connectors.
Model Telemetry & Drift Detection
We monitor prediction logs, set up accuracy alerts, and configure automated retraining tasks to maintain accuracy.
Technologies we use
Industries we serve
Frequently asked questions
What ML engineering services do you offer?
We offer custom model development, MLOps pipelines, data engineering, feature stores, model monitoring, and production ML deployment.
Which ML frameworks do you use?
We work with PyTorch, TensorFlow, scikit-learn, XGBoost, and custom architectures. We also integrate with MLflow, Weights & Biases, and Kubeflow.
Can you improve existing ML models?
Yes. We can audit, retrain, optimize, and deploy improved versions of existing models with better accuracy and performance.
Do you handle data engineering?
Yes. We build data pipelines, ETL processes, feature stores, and data quality systems to support reliable ML operations.
What industries benefit from ML engineering?
Retail, healthcare, fintech, SaaS, logistics, and any data-rich industry can benefit from custom ML models and intelligent automation.