AI Bias appears in machine learning in lots of different forms and in a world that is increasingly becoming more reliant on this type of technology, it’s important to understand the most common types of bias. But as a place to start, the important thing to consider is that training a machine learning model is […]
Six Types of AI Bias Everyone Should Know Read More »
Machine learning is becoming integral to how the modern world functions, with more and more sectors harnessing the power of algorithms to automate tasks and make decisions. As machine learning models become ingrained within decision-making processes for a range of organizations, the topic of bias in machine learning is an important consideration. The aim for
Machine Learning Bias and Fairness Read More »
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
Machine Learning Model Lifecycle Read More »
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
Anomaly Detection in Machine Learning Read More »
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
Bias vs Fairness vs Explainability in AI Read More »
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
Machine Learning in Finance Read More »
Covariate shift is a specific type of dataset shift often encountered in machine learning. It is when the distribution of input data shifts between the training environment and live environment. Although the input distribution may change, the output distribution or labels remain the same. Covariate shift is also known as covariate drift, and is a very
What is Covariate Shift? Read More »
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
A/B Testing for Machine Learning Read More »
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
What is Cross Validation in Machine Learning Read More »
The concept of optimization is integral to machine learning. Most machine learning models use training data to learn the relationship between input and output data. The models can then be used to make predictions about trends or classify new input data. This training is a process of optimization, as each iteration aims to improve the
Machine Learning Optimization – Why is it so Important? Read More »
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