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The AI Lifecycle: Training vs. Fine-Tuning vs. Inference
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Cloud vs. Edge Inference: Choosing the Right Deployment
How AI Inference Works: A Three-Step Process
The AI Lifecycle: Training vs. Fine-Tuning vs. Inference
The Nexqloud Advantage for AI Inference
Your Partner in Operationalizing AI
Real-World Use Cases for Enterprise Developers
Knowledge Base
The AI Lifecycle: Training vs. Fine-Tuning vs. Inference

The AI Lifecycle: Training vs. Fine-Tuning vs. Inference

Understanding where inference fits is key to building effective AI solutions:

  • AI Training: The initial, computationally heavy phase where a model learns patterns from a massive dataset. This is like a student attending university—it requires significant time and resources.‍
  • AI Fine-Tuning: A more efficient process of adapting a pre-trained, general-purpose model for a specific task using a smaller, specialized dataset. This is akin to a graduate specializing in a specific field.‍
  • AI Inference: The operational phase where the finalized model makes predictions on live data. This is the professional applying their expertise to solve real-world problems instantly.