Full-scale solution to deploy robust, cost-effective machine learning models using our expertise in Data Engineering, DevOps, and SaaS platforms within the Azure ecosystem.
The Implementation phase follows on from our free Proof of Concept [LINK]. At this stage, we already have a good idea of your in-house data, operational requirements, and desired insights. We now move to a full-scale solution to deploy robust, cost-effective machine learning models using our expertise in Data Engineering, DevOps, and SaaS platforms within the Azure ecosystem. Our internal structure allows us to be fully flexible to suit your timelines.
We offer solutions accessible through custom APIs deployed on your Azure environment or as a managed SaaS solution. We can also help up skill your in-house data science team through our collaboration. Periodic re-training of the model (optional) can also be added as part of a longer-term engagement.
At the conclusion of this phase, you will have access to the custom ML model and Azure Functions APIs, either hosted on your cloud environment or accessible on Deep Blue AI’s Azure infrastructure. Periodic retraining will be done through Azure ML Studio, with retraining frequency to be determined on a case-by-case basis.