How do I choose the right model?

Consider: task complexity (complex = larger model), accuracy requirements, volume (high = smaller/cheaper), latency needs (real-time = smaller), and privacy requirements (sensitive = self-hosted options).

What is the difference between model and algorithm?

An algorithm is a procedure or set of rules. A model is a specific trained instance that has learned from data. The same algorithm (like transformer) can produce different models (GPT-4, Claude) depending on training.

Can I create my own model?

Yes, through fine-tuning existing models on your data. Training a model from scratch requires enormous resources. Fine-tuning is practical for most businesses and produces task-specific models.

Why are there so many different models?

Different models optimise for different things: capability vs speed, open vs closed, general vs specialised. The diversity allows matching model choice to specific requirements and constraints.

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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 Australian 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."

Frequently Asked Questions

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