Mike Vizard

Mike Vizard

About the Author:

Mike Vizard is a veteran IT journalist with more than 25 years of experience covering the technology industry, having previously served as Editor-in-Chief of both CRN and InfoWorld and as editorial director for Ziff-Davis Enterprise, where he oversaw titles including eWEEK, CIO Insight and Baseline. Over his career he has also edited or contributed to a wide range of enterprise technology publications, including IT Business Edge, Channel Insider, ComputerWorld, TMCNet and Digital Review, and he later led editorial for CTOEdge.com. His reporting and analysis span software development, cloud computing, cybersecurity, IT channel strategy and, more recently, artificial intelligence and DevOps practices. A recognized voice in enterprise IT journalism, Vizard is known for tracking emerging technology trends as they move from early adoption into mainstream enterprise use. He now serves as Chief Content Officer for Techstrong Group, where he oversees editorial strategy across the full network — DevOps.com, Security Boulevard, Cloud Native Now, Digital CxO, Techstrong.ai, TechStrong.IT, Techstrong Semi and PlatformEngineering.com — in addition to writing and hosting content for Techstrong TV and the Techstrong Gang podcast.

Articles by Mike Vizard

personalization,AI agents, agentic ai, legacy, Agentic AI, databricks, ai agents,

Databricks Simplifies Building and Training of AI Agents

June 15, 2025

Databricks this week launched a series of initiatives, including a beta release of an Agent Bricks framework that makes it simpler to create and modify artificial intelligence agents using techniques developed by Mosaic AI Research using multiple types of large language models (LLMs).

QA, testing, app developer, Dell, data scientist, testing, hands typing, app testing, GenAI

Dell Readies Mobile Workstation for AI Professionals

May 24, 2025

Dell Technologies, later this year, will make available a mobile workstation that incorporates neural processing units (NPUs) from Qualcomm that are designed to enable data scientists and application developers to run 100 billion-parameter models locally.

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