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Five Minute Briefing - Information Management
November 29, 2022

Five Minute Briefing - Information Management: November 29, 2022. A concise weekly report with key product news, market research and insight for data management professionals and IT executives.

News Flashes

Comet, provider of an MLOps platform for machine learning (ML) teams from startup to enterprise, is releasing a new product: Kangas, open sourced to democratize large scale visual dataset exploration and analysis for the computer vision and machine learning community. Kangas helps users understand and debug their data in a new and highly intuitive way, according to the company.

Databricks is releasing MLflow 2.0, building upon MLflow's strong platform foundation and incorporating extensive user feedback to simplify data science workflows and deliver innovative, first-class tools for MLOps. Features and improvements include extensions to MLflow Recipes (formerly MLflow Pipelines) such as AutoML, hyperparameter tuning, and classification support, as well modernized integrations with the ML ecosystem, a streamlined MLflow Tracking UI, a refresh of core APIs across MLflow's platform components, and much more.

NS1, a provider of smart network control solutions, is unveiling NetBox Cloud availability in the AWS Marketplace, granting accessibility for AWS customers to NS1 network infrastructure technology.

In a roundtable webinar hosted by DBTA, experts in data management and analytics joined to examine the best practices and tools for adapting to the needs of modern data while keeping in mind the needs of modern enterprises.

Siren, provider of investigative intelligence analytics, is launching Siren 13, the latest iteration of the platform for accelerating investigations through better accessible and actionable data.

Data wants to spread, and similarly, it wants to grow; Andrew Brust, research director at Gigaom, and Dan Potter, VP of product marketing at Qlik, made this statement during DBTA's webinar, "The Business Case for Data Fabric," further explaining that data requires going with the grain, not attempting to "fix" the nature of its existence.