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Storage: The unsung hero of AI deployments

CIO

As enterprises begin to deploy and use AI, many realize they’ll need access to massive computing power and fast networking capabilities, but storage needs may be overlooked. For example, Duos Technologies provides notice on rail cars within 60 seconds of the car being scanned, Necciai says. Last year, Duos scanned 8.5

Storage 195
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How MCP can revolutionize the way DevOps teams use AI

CIO

Much of it centers on performing actions, like modifying cloud service configurations, deploying applications or merging log files, to name just a handful of examples. Imagine, for example, asking an LLM which Amazon S3 storage buckets or Azure storage accounts contain data that is publicly accessible, then change their access settings?

DevOps 196
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Thanks to AI, the data reckoning has arrived

CIO

That approach to data storage is a problem for enterprises today because if they use outdated or inaccurate data to train an LLM, those errors get baked into the model. This example drives home that we may need more data to power AI, but not if the data is wrong. This is a clear example of how more data is not always better.

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9 IT skills where expertise pays the most

CIO

Cloud computing Average salary: $124,796 Expertise premium: $15,051 (11%) Cloud computing has been a top priority for businesses in recent years, with organizations moving storage and other IT operations to cloud data storage platforms such as AWS.

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See clearly, spend wisely: The power of data platform observability

Xebia

For example, a retailer might scale up compute resources during the holiday season to manage a spike in sales data or scale down during quieter months to save on costs. For example, data scientists might focus on building complex machine learning models, requiring significant compute resources. Yet, this flexibility comes with risks.

Data 130
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See clearly, spend wisely: The power of data platform observability

Xebia

For example, a retailer might scale up compute resources during the holiday season to manage a spike in sales data or scale down during quieter months to save on costs. For example, data scientists might focus on building complex machine learning models, requiring significant compute resources. Yet, this flexibility comes with risks.

Data 130
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Unlocking the full potential of enterprise AI

CIO

Research from Gartner, for example, shows that approximately 30% of generative AI (GenAI) will not make it past the proof-of-concept phase by the end of 2025, due to factors including poor data quality, inadequate risk controls, and escalating costs. [1] Reliability and security is paramount.