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Qritrim

Enabling AI as a service for enterprises to drive efficient business management

STRATEGY

Concentrate on extracting value from data for a fraction of the cost and time that actually takes.

We helped them with:

  • The development of a data platform that allows individuals and businesses to experiment with AI for a variety of reasons without a huge upfront investment and with less risk.
  • Automating the entire AI/ML Pipeline to enable data scientists to stay focused on deriving value from the data.

ENGINEER EXPERIENCE

Solution scope included:

  • Built on open source: Used Kubeflow, SageMaker and Kubernetes for developing, orchestrating, deploying, and running scalable and portable MLOps pipelines.
  • Embedded ML/DL/AI: Train models with incorporated open source AI learning system libraries to scale knowledge.
  • Elastic deploy options: Build, manage, secure, and scale storage and compute resources quickly and easily in public, private, or your own data centres.
  • Data Analytics in AWS using AWS glue, Redshift, Kinesis etc.
  • Visualization using PowerBI, Tableau and AWS QuickSight.

PERFORMANCE

  • Increase capacity and output (end to end process and automation)
  • Enables data scientists to carry out data and analytics tasks while encompassing visualization, interactive exploration, deployment, performance engineering data preparation, and data access.
  • Improved transparency, explainability and reproducibility.
  • It’s simple to set up new business or operational logic. 
  • The selection, training, and deployment of models are all automated. 
  • New models can be created, tested, and deployed quickly by data scientists.

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