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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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
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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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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
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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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Outlier detection is a key consideration within the development and deployment of machine learning algorithms. Models are often developed and leveraged to perform outlier detection for different organizations that rely on large datasets to function. Economic modeling, financial forecasting, scientific research, and e-commerce campaigns are some of the varied areas that machine learning-driven outlier detection
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