With a proven track record of delivering innovative solutions, Mosaic Data Science brings Python’s capabilities to life across diverse industries and applications. From web development to data analysis, automation to machine learning, our skilled team navigates the Python ecosystem to create robust, scalable, and efficient solutions.
Filtering search results is an essential part of any eCommerce website. Can you remember the last time you shopped online without filtering on product attributes such as color, size, brand, etc. Additionally, rich product attributes are critical to Google SEO which drives traffic and website sales.
Mosaic designed and deployed custom computer vision models to automate asset recognition & inform inspection decisions.
A leading clothing manufacturer distributor and retailer of clothing realized they needed to fortify their pricing decisions with machine learning insights.
For many pharmaceutical firms, trial recruitment forecasting plays a role in trial recruitment planning. However, these forecasts may be generated with relatively simplistic approaches based on only a small subset of available internal & external data.
Retail executives need to think more like tech companies, using AI and machine learning not to just predict how to stock and staff their stores, but also to dynamically recommend products and set prices at the individual consumer level.
Data Science, specifically machine learning forecasting techniques, enables decision makers across a spectrum of industries to set more effective sales targets and plan profitable customer routes that boost top and bottom line growth.
We examine how professional teams can deploy predictive ticket pricing to capture increased revenue and decrease empty seats.
Computer vision is a powerful AI technique with vast business applications. In this white paper, Mosaic examines how to use machine vision for detecting airport layouts from the sky.
This is post #2 of a 2-part series focused on reinforcement learning, an AI deployment approach that is growing in popularity.
In this post I will provide a gentle introduction to reinforcement learning by way of its application to a classic problem: the multi-armed bandit problem.