Model Development and Training
Classification, regression, ranking and forecasting models built on your data.
SKAD IT Solutions has merged with Hexagon IT Solutions.
Machine Learning
Models that make it into production and stay there
SKAD IT Solutions builds machine learning systems end to end, from feature engineering and training through to deployment, monitoring and retraining.
Get expert help for your Machine Learning project
We’ll use your details only to respond to your Machine Learning request.
Clear ownership
Decision rights mapped early
Reviewable delivery
Visible work and release cadence
Maintainable handoff
Documentation built into delivery
Organizations SKAD Has Worked With
What We Do
Feature Engineering and Pipelines
Reproducible feature pipelines shared between training and inference, so the two do not diverge.
Model Deployment and Serving
Packaging, versioning and serving with the latency and availability your product needs.
Monitoring and Drift Detection
Performance tracking, drift alerts and retraining triggers after launch.
Recommendation and Personalisation
Ranking and recommendation systems tuned against business metrics rather than offline scores.
Model Evaluation and Validation
Evaluation frameworks that reflect the business decision, not just accuracy.
Why Teams Choose Us
Trained on the real distribution
Evaluation designed around how the model will actually be used.
Monitored after launch
A model without drift monitoring quietly degrades and nobody notices for months.
Reproducible
Data versions, training runs and artefacts tracked, so results can be reproduced and explained.
Tools and Technologies
01
Libraries
Scikit-Learn 
XGBoost 
LightGBM 
PyTorch 02
Features and Data
Pandas 
Spark 
Feast 03
Serving
SageMaker 
Vertex AI 
TorchServe 04
Tracking
MLflow 
Weights & Biases Frequently Asked Questions
It depends on the problem, but usually less than assumed for tabular problems and more than assumed for deep learning.
Yes, including reviewing, retraining and productionising models built in-house.
Against a business metric agreed before training starts, not against accuracy alone.
Your cloud, a managed setup, or on-device, depending on latency and data constraints.
Drift monitoring triggers alerts and a retraining path defined during the build.