Model
A trained AI system that can make predictions or generate outputs. Models encode learned patterns from training data in their parameters.
In-Depth Explanation
In AI, a model is a mathematical system that has learned to perform tasks by identifying patterns in training data. The model's "knowledge" is encoded in its parameters - numerical values that were adjusted during training.
Types of AI models:
- Language models: Process and generate text (GPT, Claude, Llama)
- Vision models: Understand and generate images (CLIP, Stable Diffusion)
- Multimodal models: Handle multiple data types (GPT-4V, Gemini)
- Embedding models: Create vector representations (ada-002, BGE)
- Classification models: Categorise inputs (BERT-based classifiers)
Key model properties:
- Architecture: The structure of the neural network
- Parameters: Learned values that define behaviour
- Size: Number of parameters (7B, 70B, etc.)
- Training data: What the model learned from
- Capabilities: What tasks it can perform
Model selection factors:
- Task requirements
- Accuracy vs speed trade-offs
- Cost considerations
- Deployment constraints
- Data privacy needs
Business Context
Choosing the right model balances capability, cost, speed, and deployment requirements. No single model is best for all use cases.
How Clever Ops Uses This
We help businesses select appropriate models for each task. Often, a combination of models - larger for complex tasks, smaller for simple ones - provides the best results and cost efficiency.
Example Use Case
"Selecting GPT-4 for complex analysis requiring high accuracy vs GPT-3.5 for high-volume simple tasks where speed and cost matter more."