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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

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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

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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

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Technology

We work with all main cloud providers and data analytics platforms

  • Databricks
  • AWS
  • Azure
  • Azure ML Studio
  • AWS Sagemaker
Data

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
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