We helped the customer build their Data Science Accelerator product that spans across both AWS and GCP. It makes use of Serverless & PaaS technologies to enable consumers to download Jupyter notebooks that have been pre-configured to access sample data feeds.
The underlying platform is built around Google BigQuery, Google Cloud Functions, AWS Elasticsearch, AWS API Gateway, AWS Lambda and Neo4J.
The customer now has a platform that is capable of showcasing the value of its data sets to external data scientists through a quickstart notebook environment.
The platform also allows the customer to monitor the usage and consumption of data, allowing them to better understand the value of their feeds and how to subsequently monetize the data to relevant customers.
About Financial & Risk Business
Our customer was an established financial and risk business looking for new ways to entice its customers to sign up to its data feeds.
The main challenge for our customer was that its data is sat behind a paywall, meaning that users are unable to quickly evaluate the quality of data sets.
The Data Science Accelerator product was designed to give external data scientists quick access to various samples of data, allowing them to evaluate the feeds by incorporating them into their own models.
Related Case Studies
Cloudreach helped Aware build a machine learning pipeline with Amazon SageMaker to enable their internal data scientists to focus on innovation.
Cloudreach helped Dufry reduce their AWS bill by 66% with cost visibility and optimization.