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Data Engineering

Data engineers are responsible for building the infrastructure that’s needed to acquire quality information. They design the pipelines that transport raw data from one system to another and develop sophisticated data modelling platforms to help analyse the results.

This means that data engineering requires a broad spectrum of skills such as systems architecture, programming, databases, networks and sensors to build and deliver effective data pipelines. Furthermore, the ability to build in security controls, optimisation cost and integrate DevOps process are vital when developing a modern data engineering strategy. 

Data engineers are vital in developing effective data science solutions and our extensive knowledge and experience in the above fields means we are able to leverage cutting edge tools and cloud platforms to deliver data engineering solutions that are secure, efficient and resilient at scale.  

 

Our expertise

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Machine Learning

More than 90% of all data in existence has been created over the past two years and the stream of data shows no signs of running dry. It simply is not possible to analyse such vast amounts of data without using statistical techniques designed for large datasets.

Simultaneously, the world  is changing fast and becoming more unpredictable. Without robust statistical testing methods, even simple datasets are subject to misinterpretation. When the price of failure is high, machine learning must form part of any business decision.

Deep Blue AI has extensive experience in machine learning, mathematics, statistics and modelling so we have the tools to handle large datasets and make sense of them. With our Design for Six Sigma Black Belt certification, we can distil your problem down to its roots, design robust and effective A/B testing, infer significance and deliver results that are robust to noise and variation.

We are not only about the numbers though - years of presenting complex technical problems at high level overviews has given us the ability to present data so that it intuitively makes sense to stakeholders. This means that you will understand our solutions as well as we do.

 
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Domain Knowledge

AI/ML is a powerful approach but it is prone to misidentifying correlation as causation. Robust data science solutions require that ML predictions be subject to guard rails based on traditional subject matter expertise. These complementary approaches are required to produce practical and achievable solutions that draw on historical trends and data.

 

Together, at Deep Blue AI we have over 35 years' experience with automotive, mining, technology, business and  academia.  This includes significant individual contributions in the fields of Mechanical and Structural Engineering, Software Engineering and IT.

 

Our years of experience in post-graduate research mean that we are experienced at gauging the scope of a problem based on a review of available data and literature and coming up with novel, innovative solutions that are feasible to implement.

 

We leverage this expertise to put our results in context to help you make informed decisions for your company, backed up by a robust data analysis methodology.