by Iguazio | Apr 11, 2023 | Uncategorized
One of the most difficult challenges in operationalizing machine learning is feature engineering with live or production data. Generating features from real-time or online production data is far more complex than with historical data, and requires dedicated...
by Iguazio | Mar 9, 2023 | Uncategorized
Session #7Product Madness (an Aristocrat co.) on Predicting 1st-Day Churn in Real Time Product Madness’ Head of Data Science discusses how technology and new work processes can help the gaming and mobile app industries predict and mitigate 1st-day (or D0) user churn...
by Iguazio | Feb 7, 2023 | Uncategorized
Simplifying feature engineering for building real-time ML pipelines might just be the next holy grail of data science. It’s incredibly difficult and highly complex, but it’s also desperately needed for multiple use cases across dozens of industries. Currently,...
by Iguazio | Jan 13, 2023 | Uncategorized
Two main challenges are hindering the adoption of AI for enterprises and government agencies. The first is an increase in the need for hybrid solutions to manage data and data science applications, to address data locality in accordance with a rise in regulation and...
by Iguazio | Dec 19, 2022 | Uncategorized
The field of MLOps has grown up around the reality that while the theoretical ability of machine learning to make accurate predictions and solve complex problems is incredibly sophisticated, actually operationalizing machine learning is still a major blocker for most...
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