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.

For decades, machine learning engineers have struggled to manage and automate ML pipelines in order to speed up model deployment in real business applications. Similar to how software developers leverage DevOps to increase efficiency and speed up release velocity, MLOps streamlines the ML development lifecycle by delivering automation, enabling collaboration across ML teams and improving the quality of ML models in production while addressing business requirements.