watch ideas come to action and your business processes improve

our offering

work very closely with our Data Science teams as we solve for better while building and deploying ML models on the cloud

your use cases, your model, your experiments, supported by our technical effort

pilot implementations
  • helping you pick the appropriate use cases and models to test on your choice of public cloud & defining the success criterias
  • implementing the cloud building blocks to test your workloads
  • performance tuning & architectural guidance on cost and scale optimization
ML model implementation

Post successful pilots, move to production grade model implementation at scale across following steps

  • data extraction: select and integrate data from relevant sources
  • data analysis: exploratory analysis to understand schema, etc
  • data preparation: clean and prepare the data for model consumption
  • model training: experiment with different algorithms
  • model evaluation: select the most relevant machine learning models
  • model validation: train and deploy the best model on the cloud
  • serving to production via rest API or as an embedded model or part of a batch pipeline
hyperparameter tuning

Every Machine Learning model requires rigorous hyperparameter testing to push it to the accuracy to its highest possible value. Let our Data Scientists

  • design experiments to increase accuracy
  • run experiments in a structured way
  • collect and analyze results

engagement approach

Our custom workshops and/or one time
pro-serv engagements allow us to meet you where you are. Typical process involves:


let’s connect

We help you embrace change by creating newer ways to work or optimising existing processes.

let’s talk