Data Science Enablement

Data Science is a field that has been talked about for over 30 years but has really come to the fore over the last few years with recent advances in processing power, large publicly available datasets, and powerful open source data science frameworks.

Data science is not just about the experimentation and modelling phase where a lot of data science projects focus exclusively – a big part of any data science project should be about how the model is going to be deployed into production, secured, monitored, and updated as new data becomes available.

Quorum believe that it is extremely important to think at an early stage of the project about all of these facets, and build a multi-disciplinary team involving data scientists, data engineers, infrastructure experts, developers and security specialists to ensure a smooth transition from trained model to production ready application. Our approach is based on the Microsoft Team Data Science Process, which is illustrated below.

Our Data Science Enablement Service designs, delivers and supports the cloud architecture for your data science programme.  Quorum ensure that you have a robust data science platform to support your data analytics requirements, including:

• Secure cloud networking services to protect your data whilst in transit and at the security perimeter;
• Design of your cloud storage solution to provide scalable data lake storage capabilities;
• Traditional relational and multi-dimensional databases for transaction processing / analytics;
• NoSQL database technologies such as document and graph databases;
• Data processing and mining technologies such as Spark Databricks, Hadoop and Windows Batch services;
• Data Science virtual machine images for data exploration;
• Deep learning technologies such as Tensorflow and Keras.

 Our team-based approach includes a mix of cloud infrastructure consultancy, development, and data consulting to ensure you have access to the right mix of skills to deliver your data science programme. Our team are used to working in highly secure, Agile and DevOps environments and have adopted these approaches to deliver scalable data science architectures.