Infrastructure for Data Analysis and Machine Learning
CASE STUDY
The Department for Work and Pensions is a public service department in the United Kingdom, handling welfare, pensions, child maintenance policy.
They are responsible for a variety of working age disabilities benefits to around 20 million claimants and customers.
ML Sense
Architected and implemented infrastructure to allow for sophisticated data analysis and machine learning.
How does it work?
How does it work?
AWS SageMaker
AWS SageMaker
The Department for Work and Pensions is a public service department in the United Kingdom, handling welfare, pensions, child maintenance policy.
They are responsible for a variety of working age disabilities benefits to around 20 million claimants and customers.
Why SageMaker?
Why SageMaker?
SageMaker provides a straightforward and user-friendly interface that allows you to choose the type of machine you want without worrying about AMIs or security groups. You can get started in seconds!
SageMaker lets developers use the power of GPUs and big machines in a way that is not possible with local setups.
SageMaker is a highly flexible machine learning platform. If you want to train your model and use its hyper-parameter tuning, SageMaker will provide that for you without any hassle.
With Sagemaker, you don’t have to worry about infrastructure because everything is ready out-of-the-box so that any data scientist can get up and running right away on their first day.
How it helped our client?
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This data platform setup was done on AWS using Terraform so that it can be cloned by other teams easily.
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This platform allows data scientists to deliver cutting edge analysis.
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What would take months to do with SageMaker can now be done in hours.
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The productivity of data scientists and data engineers has increased 10x due to good infrastructure setup.
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The cost of this infrastructure is kept as low as possible.
