Considerations for Enterprise AI

Let’s talk through some of the challenges that Enterprises will have with AI - from data location to GPU location, to model biases, to data privacy to training vs. execution.

SHOW: 748

CLOUD NEWS OF THE WEEK - http://bit.ly/cloudcast-cnotw

CHECK OUT OUR NEW PODCAST - "CLOUDCAST BASICS"

SHOW SPONSORS:

• Datadog Security Solution: Modern Monitoring and Security
• Start investigating security threats before it affects your customers with a free 14 day Datadog trial. Listeners of The Cloudcast will also receive a free Datadog T-shirt.
• Find "Breaking Analysis Podcast with Dave Vellante" on Apple, Google and Spotify
• Keep up to data with Enterprise Tech with theCUBE
• AWS Insiders is an edgy, entertaining podcast about the services and future of cloud computing at AWS. Listen to AWS Insiders in your favorite podcast player. 
• Cloudfix Homepage

SHOW NOTES:

• An Interview with Daniel Gross and Nat Friedman on the AI Hype Cycle (Stratechery)

ARE THERE EXPECTATIONS OF “OLD AI” vs. “NEW AI”?

• Are business leaders thinking about unique AI applications and use-cases, or just “ChatGPT-everything”?
• Formal data scientists vs. citizen data scientists?
• Will this just be an application, or have an impact on every aspect of a business and the IT industry?

WILL ENTERPRISE AI BE DIFFERENT THAN CONSUMER AI? 

• The industry is actively working on a broad set of models that can be used for different use-cases. 
• It's commonly accepted that AI models need to be trained near the sources of data. 
• Many businesses are concerned about including their company data into these public models
• Many businesses will want to deploy tuned models and applications in data center, public cloud and edge environments. 
• New AI applications will be required to meet security, regulatory and compliance standards, like other business applications. 

FEEDBACK?

• Email: show at the cloudcast dot net
• Twitter: @thecloudcastnet