Artificial intelligence & machine learning

If you need intelligent business applications, then you’ve come to the right place.

The goal of machine learning, typically considered a field within artificial intelligence, is to develop efficient pattern recognition methods based on maths and statistics, in order to have computers solve real world problems.

In a traditional software development methodology, we typically follow a “top-down” approach. Subject matter experts and business analysts dictate the expected behaviour of a program to software engineers, who code it using a programming language.

In machine learning, we turn that traditional methodology upside down. Data scientists and ML engineers gather real world data, which they analyse and transform. They then work to build computer models that infer the behaviour of real-world entities from those data observations.

Applications of AI/ML

Possible applications of machine learning techniques include:

•Predict values and forecast future trends by estimating relationships between variables (e.g. estimate product demand, predict sales figures, analyse marketing returns)
•Predict categories and apply the categorisation to new information (including image classification, text sentiment analysis)
•Discover structure, separating similar data points into intuitive clusters (e.g. customer segmentation, predict customer tastes, determine market price)
•Find unusual occurrences and anomalies (e.g. predict credit risk, detect fraud, catch abnormal equipment readings, predict failure times)
•Lexical analysis - chatbots capable of intelligent Q&A based on pre-existing text documents

Our methodology

Godel data scientists follow an iterative, collaborative process when they apply machine learning techniques to resolve business problems. With the help of our clients’ data engineers and subject matter experts, our team members acquire, transform and analyse data, using it to understand the underlying business.

They build and train an ML model capable of making accurate predictions, then evaluate the model’s performance when facing new data. Finally, they package the model so it can be used to infuse intelligence in our clients’ applications, and assist with the deployment to production.

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