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

Language Models

What is Domain Adaptation?

‍Domain adaptation refers to a set of techniques in machine learning designed to help models generalize better when they are moved from one domain to another. This process becomes crucial as the distribution between training data and real-world deployment scenarios can vary significantly due to different factors such as geographical locations, sensor types, time periods, etc.

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