Abacus.AI Blog

Thoughtful, informed discussion of the future of AI and Machine Learning


Solving A Hard AI Problem: Generating Forecasts In Sparse Data Environments!

Anyone who’s worked in a manufacturing space can tell you that one of the most significant challenges they face is managing supply chains of the materials and speciality items used to fabricate products. Without clear foresight into demand, how, when and from where they’ll get these vital items, the manufacturing process can quickly become untenable.

Becoming An AI First Organization: The 1-800-Flowers Journey

Abacus.AI’s service empowers all our developers and data scientists to rapidly create powerful deep learning models at scale, in production. We can optimize all aspects of our user experience including personalizing emails, predictive churn and providing contextual real-time recommendations. This translates a lift in both user-engagement and revenue.

Understanding Seq2Seq Models

Most of the machine learning applications are concerned with processing data such as images or databases – their key characteristic is that they can be “taken in” by a learning model all at once. They don’t have any temporal properties. Today we’ll be talking about a different case – models that deal with data that is sequential by nature, text, and voice being several examples.

The Intuition Behind Variational Autoencoders

When reading about Machine Learning, the majority of the material you’ve encountered is likely concerned with classification problems. What about the other way around when you want to create data with predefined features? Today we’ll be breaking down VAEs and understanding the intuition behind them.

Real-Time Anomaly Detection — A Deep Learning Approach

Pattern recognition is a crucial aspect of modern data analytics. These patterns can be studied to better understand the underlying structure of data and monitor behavior over time. However, there are often rare items or observations that seem to differ significantly from these patterns. These items are called anomalies (or outliers).


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