Our Work


# Project 1
Organizing 9 petabytes of disparate images and applying machine learning models.
Global Physical Energy Trading Company
#2 Project
Apply machine learning to understand the global oil/products.
Global Physical Energy Trading Company
Industry cover
#3 Project
Applying machine learning to understand production and yields.

our work

Clients using the NorthGravity Platform are able to move AI into production 5x faster with an 80% cost savings.

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AI in Production

with ng platform
total effort
1 Day
1 Business User
without ng platform
total effort
60+ Days minimum
4 resources: Business User, Data Scientist, Data Engineer, Cloud Engineer
  • Select input data
  • Use Pre-built ELT frameworks
  • Use current Notebooks or Python code
Data (data engineer)
  • Setup framework to download data
  • Setup frameworks to translate data
  • Create QA and retry logic
  • Create a monoting
  • Add a scheduler
  • Use out of the box data lake and structured storage
DataBase (cloud engineer)
  • Create a data lake
  • Create a database
machine learning
  • Use pre-built Features, ML, and Backtesting Task
machine learning (Data Scientist)
  • Build ML process  
  • Test ML process  
Move to production
  • Save the data flow pipeline to production
  • Schedule or trigger to run
Move to production (ML/Dev ops or Cloud Engineer)
  • Setup cloud framework
  • Move data flow pipeline to production 
quality monitoring
  • Use pre-built data, model, and pipeline quality monitoring
quality monitoring (ML/Dev ops)
  • Create:
  • Data quality monitoring
  • Model quality monitoring
  • Pipeline quality monitoring
  • Configure access rights
  • Review user logs and version
Governance (Cloud Engineer)
  • Create:
  • Access rights
  • Process to record user logs
  • Process to record version