MLOps

Building walls to tear them down  Traditional FSI organizations have silos due to regulatory, legal and operating pressures. Business units and functions try to unify across customer journey transformations to stop the short-term fix of stitching processes and data flows together to bridge customer touch points, often unsustainable, carrying higher cost implications further down the […]

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

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

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

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