I’ll admit it. I am a gushing fan of this new product from Allegro AI called Allegro Trains. I’m not sure what to call it — what noun I should attach to this creature. “Framework” and “Platform” have become, to my ears, rather meaningless jargon designed to detach suit-wearing types from their money. “Harness” is close.

Data scientists today have to choose between a massive toolbox where every item has its pros and cons. We love the simplicity of Python tools like pandas and Scikit-learn, the operation-readiness of Kubernetes, and the scalability of Spark and Hadoop, so we just use all of them. What happens? Data scientists explore data using pandas, then data engineers use Spark to recode the same logic to scale or with live streams or operational databases.

We are delighted to announce that Iguazio has been named a sample vendor in the 2020 Gartner Hype Cycle for Data Science and Machine Learning, as well as four additional Gartner Hype Cycles for Infrastructure Strategies, Compute Infrastructure, Hybrid Infrastructure Services, and Analytics and Business Intelligence, among industry leaders such as DataRobot, Amazon Web Services, Google Cloud Platform, IBM and Microsoft Azure (some of whom are also close partners of ours).

Survival analysis is one of the most developed fields of statistical modeling, with many real-world applications. In the realm of mobile apps and games, retention is one of the initial focuses of the publisher once the app or game has been launched. And it remains a significant focus throughout most of the lifecycle of any endeavor.

Every researcher or machine learning enthusiast faces that well-known experiment management nightmare; it’s usually a rude awakening discovered at the beginning of one’s career. Here’s how it goes.

Effectively bringing machine learning to production is one of the biggest challenges that data science teams today struggle with. As organizations embark on machine learning initiatives to derive value from their data and become more “AI-driven” or “data-driven”, it’s essential to find a faster and simpler way to productionize machine learning projects so that they can make business impact faster.

The resurrection of AI due to the drastic increase in computing power has allowed its loyal enthusiasts, casual spectators, and experts alike to experiment with ideas that were pure fantasies a mere two decades ago. The biggest benefactor of this explosion in computing power and ungodly amounts of datasets (thank you, internet!) is none other than deep learning, the sub-field of machine learning(ML) tasked with extracting underlining features, patterns, and identifying cat images.