Getting a prototype ML model to deliver a measurable business benefit is a significant challenge. To reap the benefits of your Machine Learning models you need to rapidly deploy, test and optimise them in production.
Optimising and retraining models once they're released is time consuming and unreliable
Models that showed promise when prototyped aren't performing as expected in production.
Getting models from prototype to production takes too long
What we do
Provision the infrastructure needed (including GPUs/TPUs where neccessary) to power AI applications (using Terraform)
Perform feature engineering
Bring domain knowledge of most suitable algorithms and features for specific machine learning problems.
Rapidly engineer Dataflow ELT process based on templates, patterns and bespoke encoders
Implement parallel processing using Dataflow for large batch (millions) predictions
Deliver secure APIs for your end-users or other applications to consume
What you get
A state of the art machine learning layer, built of data pipelines
Optimal hardware provisioning for your use case
Tooling for model training and hyperparameter tuning
Model retraining capability through feedback loops