Fine-tuning

Step 1: Data Preparation

Prepare and format your clinical data for fine-tuning:

• Collect high-quality, representative examples
• Ensure proper de-identification of PHI
• Format data according to our API specifications
• Split data into training, validation, and test sets
• Validate data quality with our assessment tools

Step 2: Model Selection

Choose the appropriate base model for your use case:

• Diagnostic Model: For symptom-based diagnosis and clinical reasoning
• Imaging Models: Specialized for different imaging modalities
• Treatment Model: For therapy recommendations and care planning
• Risk Prediction: For prognosis and outcome prediction

Our team will help you select the optimal base model for your specific requirements.

Step 3: Fine-tuning Configuration

Configure the fine-tuning process with our API:

// Example fine-tuning configuration
const fineTuningJob = await telesoft.fineTuning.create({
  baseModel: "telesoft-diagnostic-v2",
  approach: "peft", // Options: "peft", "full", "expert_feedback", "knowledge_base"
  trainingData: "your-dataset-id",
  validationData: "your-validation-dataset-id",
  hyperparameters: {
    learningRate: 1e-5,
    epochs: 3,
    batchSize: 16,
    warmupSteps: 500,
    weightDecay: 0.01
  },
  specialty: "cardiology", // Optional: medical specialty focus
  targetMetrics: {
    primary: "f1_score",
    minimum: 0.85
  },
  preserveCapabilities: [
    "general_medical_knowledge",
    "regulatory_compliance"
  ]
});

Step 4: Training and Monitoring

Monitor the fine-tuning process:

• Track progress through our dashboard or API
• Monitor metrics on validation data
• Receive notifications for key milestones
• Address any issues flagged during training

// Check fine-tuning job status
const status = await telesoft.fineTuning.status({
  jobId: fineTuningJob.id
});

console.log(`Training progress: ${status.progress}%`);
console.log(`Current metrics: ${JSON.stringify(status.metrics)}`);
console.log(`Estimated completion: ${status.estimatedCompletion}`);

Step 5: Evaluation

Evaluate the fine-tuned model's performance:

• Comprehensive performance metrics on test data
• Comparison to base model performance
• Subgroup analysis across demographics
• Error analysis and edge case identification
• Clinical validation with expert review

// Evaluate fine-tuned model
const evaluation = await telesoft.fineTuning.evaluate({
  modelId: fineTuningJob.resultModel.id,
  testData: "your-test-dataset-id",
  compareToBase: true,
  subgroupAnalysis: true
});

console.log("Performance increase:", evaluation.improvementSummary);
console.log("Detailed metrics:", evaluation.metrics);
console.log("Subgroup performance:", evaluation.subgroupPerformance);

Step 6: Deployment

Deploy your fine-tuned model to production:

• Deploy through our secure API infrastructure
• Implement in staging environment first
• Conduct A/B testing against base model
• Set up monitoring and feedback loops
• Prepare rollback options if needed

// Deploy fine-tuned model
const deployment = await telesoft.fineTuning.deploy({
  modelId: fineTuningJob.resultModel.id,
  environment: "staging", // Options: "staging", "production"
  rolloutPercentage: 25,  // Start with partial traffic
  monitoringSettings: {
    alertThresholds: {
      errorRate: 0.05,
      latency: 500
    },
    performanceTracking: true
  }
});

// Use your custom model
const analysis = await telesoft.diagnostics.analyze({
  patientData,
  modelId: fineTuningJob.resultModel.id  // Specify your custom model
});