How do you choose the right model from Azure ML Model Catalog?

Question

Azure ML Model Catalog contains a large number of models (LLMs, vision models, embedding models, speech models, etc.), and I'm finding it difficult to understand how experienced practitioners decide which one to use.

What factors should be considered when selecting a model?

For example:

Accuracy vs latency

Cost of inference

Context window

Model size

Fine-tuning support

Hardware requirements (CPU/GPU)

Licensing/commercial usage

Multilingual capabilities

Reasoning ability

Benchmark scores

Is there a recommended evaluation process or checklist that teams follow before choosing a model for production?

I'd love to hear how others approach model selection in Azure ML.

Answer 1

Hello Nidhi Priya,

There isn't a one-size-fits-all model in the Azure ML Model Catalog. The best choice depends on your use case, performance requirements, and deployment constraints.

When evaluating models, teams typically consider factors such as:

• Task suitability (chat, code, vision, speech, embeddings, etc.)
• Quality and accuracy for the intended use case
• Latency and throughput requirements
• Inference cost
• Context window size
• Support for fine-tuning or customization
• CPU/GPU requirements
• Licensing and commercial usage terms
• Multilingual capabilities, if required
• Safety and Responsible AI features

While benchmark scores can be a useful starting point, they shouldn't be the only deciding factor. A model that performs well on public benchmarks may not necessarily perform best for your specific workload.

A common evaluation approach is:

1. Define your success criteria (for example, quality, latency, cost, and compliance).
2. Shortlist a few candidate models that fit your requirements.
3. Evaluate them using representative prompts or datasets from your application.
4. Compare output quality, response time, resource utilization, and inference cost.
5. Select the model that provides the best balance for your production scenario.

Azure AI Foundry and Azure Machine Learning also provide model evaluation capabilities that allow you to compare multiple models using consistent metrics and your own evaluation datasets, making it easier to assess how different models perform for your workload.

Ultimately, model selection is an iterative process. Many teams start with a small set of candidate models, evaluate them against real-world scenarios, and choose the one that best meets their functional, operational, and cost requirements rather than relying solely on benchmark rankings.