Chris Tozzi is an Adjunct Analyst at The New Stack. He specializes in areas such as cloud computing, data governance, and machine learning, with a focus on deep tech and data management. Chris's insights and analyses are featured in prominent publications including DevOps.com, Security Boulevard, and TechTarget.
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Chris Tozzi's articles predominantly focus on software and SaaS, with an emphasis on citing data and providing how-to guides. He covers topics such as cloud computing, data protection, open source, machine learning, and DevOps.
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Compute resources are frequently at the center of conversations about AI workloads' technical requirements. AI models require vast computing power.
The more external applications and data sources a business can connect to its ERP platform, the more effective the ERP tends to be.
No data governance strategy is complete without a plan for tracking relevant KPIs and other metrics.
At first glance, cloud networking may not seem to exert a major impact on AI workloads. After all, cloud networks are typically quite fast and reliable.
Virtually all enterprises rely on clean, well-structured data. But very few sell data.
Conversations about AI-attributed job loss center on whether AI technology is sufficiently reliable, accurate and...
Although we're three decades into the era of cloud computing, legacy systems are still a common part of enterprise IT landscapes.
One thing that Kubernetes doesn't do -- at least not natively -- is manage costs. Kubernetes doesn't ensure that the resources available within a cluster are used cost-effectively.
Data issues are frequently cited as a top barrier to AI adoption. It's easy enough to understand why.
Mark Zuckerberg's manifesto promises a bright future for AI and technological democratization. Read one analyst's thoughts on why this is just more PR hype.
