AI Studio

AI Studio provides end-to-end model management capabilities: browse model catalogues, fine-tune models on your data, evaluate performance, and deploy for inference.

Overview

AI Studio includes:

• Model Catalogue - Browse available base and fine-tuned models
• Fine-Tuning - Train models on your custom datasets
• Evaluation - Assess model performance
• Deployments - Serve models for inference

Model Catalogue

Browsing Models

Navigate to AI Studio > Models to explore:

Base Models Pre-trained foundation models:

• Large language models
• Vision models
• Multi-modal models

Fine-Tuned Models Your custom-trained models.

Model Information

Each model shows:

• Model name and description
• Parameters and architecture
• Pricing (per token for inference)
• Domain and capabilities
• Documentation links

Filtering Models

Filter by:

• Model type (LLM, vision, etc.)
• Domain (general, code, etc.)
• Status (available, coming soon)

Fine-Tuning

Train models on your custom data to improve performance for specific tasks.

Preparing Data

Dataset Requirements

• Format: JSONL, CSV, or supported formats
• Quality: Clean, representative data
• Size: Minimum samples depend on model

Upload Dataset

1. Go to AI Studio > Fine-Tuning
2. Click Upload Dataset
3. Select your file
4. Verify upload completed

Creating a Fine-Tuning Job

1. Click Create Fine-Tuning Job
2. Configure:

Model Selection

• Choose base model to fine-tune
• Review model capabilities

Dataset

• Select uploaded training dataset
• Optionally select validation dataset

Training Parameters

3. Click Start Training

Monitoring Training

Track progress on the fine-tuning page:

• Training status
• Current epoch
• Loss metrics
• Training time

Training Results

When complete:

• Model saved to your catalogue
• Training metrics available
• Checkpoints accessible
• Ready for evaluation or deployment

Evaluation

Assess how well your models perform.

Creating an Evaluation Job

1. Go to AI Studio > Evaluation
2. Click New Evaluation
3. Configure:
   • Model to evaluate
   • Evaluation dataset
   • Metrics to compute
4. Run evaluation

Evaluation Metrics

Depending on task type:

• Accuracy: Correct predictions
• Perplexity: Language model quality
• BLEU/ROUGE: Text generation quality
• Custom metrics: Task-specific measures

Comparing Models

Compare multiple models:

• Side-by-side metrics
• Performance across datasets
• Cost vs. quality tradeoffs

Deployments

Deploy models for inference via API.

Creating a Deployment

1. Go to AI Studio > Deployments
2. Click Deploy Model
3. Configure:
   • Model to deploy
   • Instance type
   • Scaling settings
4. Click Deploy

Deployment Settings

Using Deployed Models

Access via API:

import requests

response = requests.post(
    'https://api.podstack.ai/v1/inference/your-deployment',
    headers={'Authorization': 'Bearer YOUR_TOKEN'},
    json={'prompt': 'Your input text'}
)

print(response.json()['output'])

Managing Deployments

• Scale: Adjust replica count
• Monitor: View request metrics
• Stop: Pause the deployment
• Delete: Remove deployment

Pricing

AI Studio costs include:

Fine-Tuning

• GPU time during training
• Based on GPU type and duration

Inference

• Per-token pricing
• Varies by model size
• Volume discounts available

Storage

• Dataset storage
• Model storage

Best Practices

Data Preparation

1. Clean data thoroughly - Remove noise and errors
2. Balance classes - Avoid skewed distributions
3. Validate format - Ensure correct structure
4. Start small - Test with subset first

Fine-Tuning

1. Start with defaults - Use recommended parameters
2. Monitor metrics - Watch for overfitting
3. Use validation set - Track generalization
4. Save checkpoints - Enable recovery

Evaluation

1. Use held-out data - Don’t evaluate on training data
2. Multiple metrics - Don’t rely on single measure
3. Compare baselines - Measure improvement

Deployment

1. Test thoroughly - Validate before production
2. Start small - Begin with minimal replicas
3. Monitor usage - Track latency and errors
4. Scale gradually - Increase based on demand

Feature Availability

AI Studio may require:

• Feature flag enabled
• Minimum account level
• Sufficient wallet balance

Contact support if AI Studio isn’t visible in your portal.

Next Steps

• Create a Pod for custom training setups
• Set up Storage for datasets
• Manage Billing for AI Studio costs