ML Model Deployment & Data Pipelining
Off-the-shelf AI does not fit every problem, and a model that works in a notebook still has to run reliably on your data. We build, train and deploy custom ML models with data pipelines, versioning, monitoring and APIs for live or batch predictions.
Build, train, and scale custom machine learning systems
Need to go beyond off-the-shelf AI? We help businesses build, train, and deploy custom machine learning models, with full control over your logic, performance, and data. From initial experimentation to production-grade deployment, our ML systems are designed to adapt to your data pipelines, infrastructure, and business workflows.
From preprocessing to cloud-based inference, our ML services include model selection, optimization, API exposure and reliable data pipelines using modern MLOps practices. We help you choose the right frameworks (TensorFlow, PyTorch, Scikit-learn), structure clean training data, and build reusable pipelines for real-time or batch predictions. We also implement versioning, monitoring, and auto-scaling infrastructure to ensure model reliability across environments.
We support predictive modeling, classification, fraud detection, recommendation systems and more, running on real-time or scheduled batch pipelines. Whether you’re building a customer behavior model or a fraud scoring engine, we connect your models to dynamic datasets using scalable ETL/ELT strategies and production-ready APIs. Our AI-first approach ensures that your ML models remain adaptive, accountable, and aligned with business outcomes.
Our services include:
Model training (TensorFlow, PyTorch, Scikit-learn)
ETL/ELT pipelines for clean, structured input
GraphQL/REST APIs for live inference
GPU-enabled deployment via containers or cloud
Model versioning, A/B testing, and monitoring tools
Batch or real-time inference via scalable MLOps pipelines
Questions we get asked
Which ML frameworks do you use?
TensorFlow, PyTorch and Scikit-learn, chosen to fit the problem.
Can predictions run in real time?
Yes. We build pipelines for real-time or batch predictions and expose models through GraphQL or REST APIs.
How do you keep a model reliable after launch?
We set up versioning, monitoring and auto-scaling infrastructure.
What kinds of models can you build?
Predictive models, classification, fraud detection and recommendation systems, among others.
Company
Services
© 2026, Quantzi, All Rights Reserved.