Demand is a key indicator of the operational and expansion prospects for retail organizations, and being able to forecast this can be the difference between retailers surviving and thriving in a competitive landscape. The most critical business factors, such as revenue, profit margins, capital expenditure, supply chain management etc., are directly dependent on demand. […]
Predicting Customer Demand With Machine Learning Read More »
Many companies are using data to drive their decisions. The aim is to remove uncertainties, guesswork, and gut feeling. A/B testing is a methodology that can be applied to validate a hypothesis and steer the decisions in the right direction. In this blog post, I want to show how to create a containerized micro-service architecture
A practical guide to A/B Testing in MLOps with Kubernetes and Seldon Core 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 »
The implementation and growth of open source software (OSS) across business functions and geographies has had many positive impacts making it sometimes worth the hidden risks, in particular to help businesses to quickly develop new processes faster. OSS is generally free, making it popular for teams wanting quick results without budget sign-off or the fear
Open Source Machine Learning : Managing the Hidden Risks Read More »
Whether being used to clean data, surface trends and insights, or make forecasts, machine learning is increasingly becoming an important tool for data analytics. Organisations are processing more data than ever, so naturally want to harness this data to make decisions. Data analytics is the process of gaining insight and actions from data. As organisations gain
Machine Learning in Analytics Read More »
As machine learning gains traction across industries, businesses are finding new and novel uses for the technology. However, as soon as your organization moves beyond a handful of models, management and observability of this growing system can become a significant challenge. This is where the machine learning maturity model plays a part. Without proper monitoring,
Understanding the Machine Learning Maturity Model Read More »
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