PyTorch
- Custom architecture design
- Distributed multi-GPU training
- Experiment tracking (W&B/MLflow)
ML Frameworks
TensorFlow & PyTorch Engineering
Custom model development and training infrastructure for teams building beyond off-the-shelf APIs.
Why Custom Model Development Matters
Pretrained APIs can't be reshaped for a genuinely novel model architecture or task, and training and serving custom models requires infrastructure most teams haven't built. We build the model and the pipeline around it — training, evaluation, and deployment — so a custom model is genuinely production-ready, not just a research notebook.
The Business Impact
- Purpose-Built Models
Custom architectures suited to your exact problem, not a generic API.
- Faster Training
Distributed training pipelines cut time-to-trained-model significantly.
- Automated Deployment
Models move from training to production through an automated pipeline.
Where We Work
From model architecture to the pipeline that trains and serves it.
TensorFlow
- TensorFlow Extended (TFX) pipelines
- Edge & mobile deployment (TFLite)
- TensorFlow Serving
MLOps Pipeline
- Automated training pipelines
- Model versioning & registry
- Continuous evaluation & retraining
How We Help
From custom architecture to a production MLOps pipeline.
Custom Model Development
Beyond off-the-shelf APIsWe design, train, and evaluate models built specifically for your problem, using PyTorch or TensorFlow as the fit requires.
Reduced
Training Time
- Custom architecture design
- Distributed training setup
- Experiment tracking & evaluation
MLOps Pipeline Build-Out
From notebook to productionThe infrastructure that takes a trained model into a monitored, retrainable production service.
85%
Models on Automated Pipeline
- Automated training pipelines
- Model versioning & registry
- Continuous evaluation triggers
What This Looks Like in Practice
Results teams see after building a real MLOps pipeline.
Reduced
Training time
Distributed training pipelines cut time-to-trained-model significantly.
100%
Model reproducibility
Every training run tracked and reproducible via experiment tracking.
Automated
Deployment path
Models move from training to production serving through an automated pipeline, not a manual handoff.
Why Teams Choose Us
We build the pipeline, not just the model.
Pipeline-First Thinking
We won't train a one-off model without a plan to retrain, evaluate, and deploy it.
- Automated retraining pipelines delivered
- 85% of models on automated deployment
Fully Reproducible
Every training run is tracked, versioned, and reproducible on demand.
- 100% experiment tracking coverage
- Model registry & rollback support
Framework-Flexible
We choose PyTorch or TensorFlow based on your deployment target, not habit.
- PyTorch for research-driven work
- TensorFlow for edge/mobile deployment
Frequently Asked Questions
Common questions about our ML Frameworks and implementation services.