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Introducing cloud for doing ML training at scale

Business context

BICS is a communications platform company providing international communications, messaging and connectivity to operators and enterprises, globally.

BICS relies heavily on ML predictive analytics in several use-cases: to forecast pricing evolution, combat fraud, analyse mobility, etc. Most of the data processing and analytics is happening in a local data center, BICS wants to experiment with a hybrid setup, where some ML jobs run in the cloud. The main benefits of the cloud in this context is scalability and cost efficiency.

Scope & objectives

  • execute one ML use-case in the cloud, fully integrated into the existing data processing framework

  • Adapt the existing software tooling (CI/CD, scheduler, network connectivity) to work with cloud resources.

Key results

We set up a machine learning environment on AWS, leveraging SageMaker. Model predictions, model training and also hyperparameter tuning jobs are all running on SageMaker.

The SageMaker jobs are integrated with the on-premises scheduler, and data flows seamlessly between on-premises network and the cloud.


  • Scalability of the cloud means that BICS can run on-demand ML tuning jobs, while keeping their existing on-premises cluster for scheduled jobs

  • This drives more innovation and experimentation, resulting in a more scalable and sustainable way-of-working for their data scientists

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