Rank Group & endjin

Productionising Data Science models and automating Search Results Enhanced Due Diligence processes.

Rank Group are a gaming, leisure, and entertainment company, who own Grosvenor Casinos and Mecca Bingo. With new online platforms and physical smart gaming devices, they were experiencing an explosion of data across the organization.

Alongside traditional business intelligence and reporting requirements, Rank assembled a new dedicated data science team tasked with surfacing new types of customer insight using advanced analytics, machine learning and predictive modelling.

DataOps Blueprint

Starting with exploratory models in R, endjin helped Rank to develop fully productionized solutions using Azure data services - orchestrating data production pipelines with Azure Data Factory, processing data with on-demand with HDInsight, hosting and executing R models in Azure Machine Learning, displaying interactive data visualisations in Power BI, all build and deployed using Azure DevOps.

With endjin's expertise in productionizing advanced analytics solutions, Rank's investment in data science could be realized across the business.

Rank Group used our Synapse Enterprise blueprint, designed to scale to meet all your enterprise data needs.

Source On Prem SAAS Internal Cloud Ingest Synapse Pipelines (ADF) Functions Prep Synapse Sql on-demand Synapse Spark Consume Functions (APIs) Power BI Power BI Embedded DevOps & Governance

Machine Learning - the process is the science

James Broome03/02/2016

What do machine learning and data science actually mean? This post digs into the detail behind the endjin approach to structured experimentation, arguing that the "science" is really all about following the process, allowing you to iterate to insights quickly when there are no guarantees of success.

Machine Learning - mad science or a pragmatic process?

James Broome02/19/2016

This post looks at what machine learning really is (and isn't), dispelling some of the myths and hype that have emerged as the interest in data science, predictive analytics and machine learning has grown. Without any hard guarantees of success, it argues that machine learning as a discipline is simply trial and error at scale – proving or disproving statistical scenarios through structured experimentation.