Your Enterprise Data Shouldn't Go to a Public AI Endpoint #AI #SLM

Bigger AI models aren't always better for business. Here's why enterprise AI is going small.

For years, the AI race focused on larger models with more parameters and broader capabilities. But enterprises have different priorities: data privacy, lower inference costs, faster responses, and control over their own infrastructure.

Enter Small Language Models (SLMs).

In this video, we explore why businesses are considering smaller, specialised AI models and how they could change the way enterprises deploy AI.

What you'll learn:

• Why bigger isn't always better: The cost, latency, and governance challenges of large AI models.
• What are Small Language Models (SLMs)? How compact, task-specific models can handle targeted enterprise workflows.
• Privacy and data control: How on-premises, private-cloud, and on-device deployments can help organisations maintain control over sensitive information.
• The enterprise AI shift: What Salesforce's specialised models, Microsoft's Phi family, Palantir's AI Platform, and Apple's on-device AI approach reveal about different ways to deploy AI.
• Cost and performance: Why smaller models can be a practical choice for specific tasks, depending on workload, hardware, and accuracy requirements.

The future of enterprise AI isn't necessarily one massive model doing everything. It could be a combination of specialised models, larger models, and AI agents working together.

The real question: Does your business need a bigger model, or a model built for the job?

💬 Would you trust a smaller, privately deployed AI model with your company's sensitive data? Share your thoughts in the comments.
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#EnterpriseAI #SmallLanguageModels #SLM #ArtificialIntelligence #AIAgents #LLM #AITrends #DataPrivacy #MachineLearning #AIInfrastructure