Artificial intelligence is a broad field of research which seeks to create systems that mimic or even surpass human intelligence. Machine learning is an approach to system or model development which sees the machine learn from the data itself without direct human control. It means an algorithm can reach a level of complexity and flexibility […]
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The machine learning lifecycle encompasses every stage of machine learning model development, deployment, and performance monitoring. This includes the initial conception of the model as an answer to an organization’s problem, to the ongoing optimization that’s required to keep a model accurate and effective. Machine learning models can degrade over time due to a range
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Anomaly detection is an important factor for every stage of the whole machine learning lifecycle. The development and building of a machine learning model will usually require a huge array of high quality training data. The more high quality data available, the more accurate the model will be if used early in the machine learning process to help
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Over the last few years, there has been a distinct focus on building machine learning systems that are, in some way, responsible and ethical. The terms “Bias”, “Fairness” and “Explainability” come up all over the place but their definitions are usually pretty fuzzy and they are widely misunderstood to mean the same thing. This blog
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The financial and banking sectors are incredibly data-rich, with millions of transactions and transfers occurring every day. Data-led decisions are an integral part of the financial sector, whether in banks, insurance providers, lenders, or stock traders. Machine learning in finance is leveraged in the financial sector to automate manual processes, help inform decision making, and enhance
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A/B testing is an optimization technique often used to understand how an altered variable affects audience or user engagement. It’s a common method used in marketing, web design, product development, and user experience design to improve campaigns and goal conversion rates. Machine learning is the training of models which learn the relationship between input and output
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Cross validation is the use of various techniques to evaluate a machine learning model’s ability to generalize when processing new and unseen datasets. Generalization is a key aim of machine learning development as it directly impacts the model’s ability to function in a live environment. Cross validation is therefore a key step in ensuring a
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Deep learning is a form of machine learning which aims to mimic and imitate the processes of the human brain through machine learning algorithms. The resulting models simulate how the human brain learns from its environment to detect, recognize and categorize objects and make nonlinear decisions. Deep learning models are also known as deep neural networks
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Concept drift is a major consideration for ensuring the long-term accuracy of machine learning algorithms. Concept drift is a specific type of model drift, and can be understood as changes in the relationship between the input and target output. The properties of the target variables may evolve and change over time. As the model has been
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Machine learning deployment is the process of deploying a machine learning model in a live environment. The model can be deployed across a range of different environments and will often be integrated with apps through an API. Learning how to deploy machine learning models is a key step in an organization gaining operational value from
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