Four principles for deploying AI responsibly Telecoms.com periodically invites expert third parties to share their views on the industry’s most pressing issues. In this piece Alejandro Saucedo, Machine Learning Engineering Director at Seldon, looks at best practice for the use of ethical ai. Artificial Intelligence (AI) is in the process of transforming every industry, with over one […]
Four principles for deploying AI responsibly Read More »
Covéa Insurance Plc is the UK underwriting business of leading French mutual insurance group Covéa, and offers commercial, motor, high net worth, property and protection insurance through its Standard & Poor’s A+ stable rating. Covéa Insurance serves two million policyholders and generated over £725.7 million in premiums in 2020. Their goal is to become the most
How Covea made an 11x ROI detecting fraudulent insurance policies Read More »
Machine learning model inference is the use of a machine learning model to process live input data to produce an output. It occurs during the machine learning deployment phase of the machine learning model pipeline, after the model has been successfully trained. Machine learning model inference can be understood as making a model operational, or
Machine Learning Model Inference vs Machine Learning Training Read More »
If you’re a retailer looking for innovative approaches to engage customers, sometimes the answers are more available than you realize and found in evaluating company data. Data analysis provides insights to help you increase consumer engagement with the right products, services or messaging at the right time, through potentially multiple devices. However, evaluating large sets
Personalization Through Machine Learning Read More »
One of the most critical challenges with the data of the retailer is to determine the substitutable product pairs, usually online. While we apply machine learning approaches, it should involve assortment optimization engines to generate outcomes. The shopping experience is definitely ruling the brick and mortar stores, however, the rise of online shopping has revolutionized
When Substitution Models Go Wrong Read More »
Machine learning optimization is an important part of all machine learning models. Whether used to classify an image in facial recognition software or cluster users into like-minded customer groups, all types of machine learning model will have undergone a process of optimization. In fact, machine learning itself can be described as solving an optimization problem,
Algorithm Optimization for Machine Learning Read More »
Artificial neural network models are behind many of the most complex applications of machine learning. Classification, regression problems, and sentiment analysis are some of the ways artificial neural networks are being leveraged today. As an emerging field, there are many different types of artificial neural networks. They vary for a variety of reasons, such as
Neural Network Models Explained Read More »
How to Deploy Models on Kubernetes Kubernetes is a container orchestration platform used to manage containerized applications. It used to automate important parts of the container management process such as container replication, scaling, monitoring and scheduling. It’s an open-source platform written in Google’s Go programming language. Kubernetes is one of the most popular container management
A Guide to Deploying Machine Learning Models on Kubernetes Read More »
Machine learning pipelines are used to optimize and automate the end-to-end workflow of a machine learning model. Core elements of the machine learning process can be refined or automated once mapped within a machine learning pipeline. As more and more organizations leverage the power of machine learning, models are developed and deployed within increasingly diverse
What is a Machine Learning Pipeline? A Step By Step Guide Read More »
Machine learning is increasingly becoming an integral part of the modern digital landscape, with models being deployed in more industries and organizations than ever before. An exponential increase in the amount of data being collected by organizations has increased the complexity of processing this data. But the potential value an organization can extract from ever-more
MLOps: What is it and Why it Matters Read More »
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