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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Poor application visibility can cost businesses millions each year. Choosing the right observability tool isn't just a technical decision; it's a
AIOps and MLOps are both popular buzzwords in the AI space, but they have distinct meanings: AIOps refers to the use of AI to automate IT operations, whereas MLOps is a set of practices for developing machine learning models.
At a high level, modern cloud storage systems all do the same thing -- house data -- in the same basic way.
Organizations deploying AI workloads might benefit from choosing an AI data center to host them.
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...
