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    10 min read

    Guided Synthetic Data Generation for ML Training

    For ML teams needing diverse training data. Generate synthetic examples guided by real data distributions. 50% reduction in data collection costs.

    Who It's For

    Machine learning teams, data scientists, and AI research labs who need to augment training datasets with synthetic examples for rare classes or edge cases

    Problem Solved

    Collecting real-world examples of rare events (defects, fraud, accidents) is expensive or impossible. Models underperform on underrepresented scenarios

    Ready to implement?

    Schedule a DemoView Documentation

    Why Mixpeek

    50% reduction in data collection costs, 3x more edge case coverage, and synthetic examples that maintain distribution fidelity

    Overview

    Rare events are critical but hard to collect. This use case shows how Mixpeek guides synthetic data generation to fill training data gaps.

    Challenges This Solves

    Rare Event Scarcity

    Cannot collect enough examples of rare events

    Impact: Models fail on important edge cases

    Data Collection Cost

    Real data collection is expensive and slow

    Impact: Budget constraints limit dataset size

    Synthetic Realism

    Generated data must be realistic to be useful

    Impact: Poor synthetic data degrades model performance

    Distribution Matching

    Synthetic data must match real-world distributions

    Impact: Biased synthetic data creates biased models

    Implementation Steps

    Mixpeek analyzes existing data distributions, identifies gaps, and guides synthetic data generation to create realistic examples that fill coverage holes

    1

    Analyze Data Distribution

    Identify gaps in current training data

    import { Mixpeek } from 'mixpeek';
    

    const client = new Mixpeek({ apiKey: process.env.MIXPEEK_API_KEY });
    

    // Analyze training data distribution
    async function analyzeDistribution(datasetId: string) {
      const analysis = await client.datasets.analyzeDistribution({
        collection_id: datasetId,
        dimensions: [
          'class_distribution',
          'attribute_coverage',
          'embedding_space_density',
          'temporal_distribution',
          'edge_case_coverage'
        ]
      });
    

      return {
        class_balance: analysis.class_distribution,
        underrepresented: analysis.sparse_regions,
        edge_cases_needed: analysis.gap_analysis,
        target_synthetic_count: analysis.recommendations
      };
    }
    2

    Generate Synthetic Examples

    Create synthetic data guided by distribution analysis

    // Generate synthetic examples for underrepresented classes
    async function generateSyntheticData(config: {
      dataset_id: string;
      target_class: string;
      count: number;
      variation_level: 'low' | 'medium' | 'high';
    }) {
      // Get real examples as seeds
      const seeds = await client.retrieve({
        collection_id: config.dataset_id,
        filters: { class: config.target_class },
        limit: 50
      });
    

      // Generate synthetic examples
      const synthetic = await client.synthetic.generate({
        seeds: seeds,
        count: config.count,
        variation: config.variation_level,
        constraints: [
          'maintain_class_characteristics',
          'vary_background',
          'vary_lighting',
          'vary_perspective',
          'maintain_semantic_content'
        ],
        quality_filters: {
          min_realism_score: 0.8,
          max_seed_similarity: 0.95
        }
      });
    

      return synthetic;
    }
    3

    Validate Synthetic Quality

    Ensure synthetic data is useful for training

    // Validate synthetic data quality
    async function validateSynthetic(syntheticIds: string[], realDatasetId: string) {
      const validation = await client.synthetic.validate({
        synthetic_ids: syntheticIds,
        real_collection_id: realDatasetId,
        metrics: [
          'distribution_alignment',
          'realism_score',
          'diversity_score',
          'class_preservation',
          'artifact_detection'
        ]
      });
    

      return {
        overall_quality: validation.quality_score,
        distribution_match: validation.distribution_alignment,
        realism: validation.avg_realism_score,
        diversity: validation.diversity_score,
        rejected: validation.rejected_examples,
        recommendations: validation.improvement_suggestions
      };
    }

    Feature Extractors Used

    Image Embedding

    Generate visual embeddings for similarity search and clustering

    Video Embedding

    Generate vector embeddings for video content

    Retriever Stages Used

    Expected Outcomes

    50% reduction in data collection spend

    Data Collection Costs

    3x more edge cases in training data

    Edge Case Coverage

    20% improvement on rare class accuracy

    Model Performance

    60% faster dataset creation

    Time to Dataset

    90%+ match to real data distributions

    Distribution Fidelity

    Frequently Asked Questions

    Related Resources

    Ready to Implement This Use Case?

    Our team can help you get started with Guided Synthetic Data Generation for ML Training in your organization.

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