Before starting a machine learning project, we'll help you plan your work, identify the resource you'll need and create a compelling business case highlighting the value created for your business.
Once you're ready to architect a new ML-powered application, we'll work with you to create or review the architecture, touching on important areas such as security, scalability, high availability and cost.
Machine Learning can consume lots of resources so it's important to keep cost under control. We'll review your architecture, modelling practices and inference process to find areas where you can optimise costs.
Creating ML models is significantly different from developing applications. We'll help you adapt traditional DevOps processes to fit the lifecycle of a model, from creation to monitoring production inference.