AI Agent Frameworks

AI Model Deployment

AI Model Routing

Closed Source Model

Continual Pre-Training

Direct Preference Optimization (DPO)

Domain Adaptation

Domain-Adapted Models

Evolutionary Model Merging

Fine-Tuned Models

Gradient Descent

Knowledge Distillation

Knowledge-Based AI Agents

Large Action Models (LAM)

Large Language Model (LLM) Agents

Large Language Models (LLMs)

Machine Learning (ML)

Model Development

Model Distillation

Model Retraining

Multi-Agent systems (MAS)

Open Source LLMs vs Closed Source LLMs

Open Source Model

Pre-Trained Models

RAG Pros and Cons

RAG(Retrieval-Augmented Generation)

Robotic Process Automation (RPA)

Signal-to-Noise Ratio

Small Language Model (SLM) Agents

Small Language Models (SLMs)

Small Language Models (SLMs) vs Large Language Models(LLMs)

Spectrum-Powered Pre-training

Supervised Fine-Tuning (SFT)

Swarm Intelligence

Transfer Learning

Weights and Biases

Model Development

What is AI Model Deployment?

AI model deployment is the final phase in the lifecycle of an AI model, where the model, after being trained and validated, is introduced into a live, operational environment. This enables the model to process and analyze real-world data, make practical decisions or predictions, and integrate its capabilities into business operations or user-facing applications. It's akin to launching new software into the market after it has been developed and tested.

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