Methodolgy
In order to implement a AI project, users will need a process to follow. At valuedate, we have implemented and end-to-end workflow to empower data scientist to become independent
Intelligence
Unlike typical software and data projects, AI/ML projects will need to be developed on production environments due to real data being needed to train models. We know how to do it
Production
AI Models have a decay process and will need to be retrained from time to time. MLOps implemented by valuedate will automate everything for you and pre-defined triggers will check accuracy to train a new version
Engineering Science
We dont developed rocket boosters and for sure we arent rocket scientists, but we know a few things about Data Engineering and how to apply it to Data Science
Technology
We work with all main cloud providers and data analytics platforms
-
Databricks
-
AWS
-
Azure
-
Azure ML Studio
-
AWS Sagemaker
Our Services
Using Data Science as a service, your company can benefit from an external look to discover patterns and new ways of seeing and analyzing information that an internal look can sometimes ignore due to habits.
- Data Engineering for Science
- Data Science
- Use-cases Business Plan (Cost Vs ROI)
- Model Engineering
- Development/Training/Testing workflows
- MLOps
