Models & Processing
Fine-Tuning
Customize Mixpeek’s embedding models for your specific use case
Available to Enterprise customers only. To trial fine-tuning capabilities, contact our team at info@mixpeek.com
Why Fine-Tune?
Domain Adaptation
- Optimize for industry-specific terminology
- Better understand your content context
- Improve matching for technical/specialized content
Search Quality
- Boost relevance scores for important matches
- Reduce false positives
- Better handle edge cases
Use Cases
Fine-Tuning Process
1
Send Annotated Data
Upload your training data to a designated S3 bucket. Data should include:
- Positive examples (good matches)
- Negative examples (poor matches)
- Relevance scores
- Content pairs with similarity annotations
Our team will provide detailed specifications for your use case.
2
Initiate Fine-Tuning
Use the Mixpeek Dashboard to schedule a fine-tuning job:
This returns a new model_id
(e.g., model_1askdh2390
) for use in your API calls.
3
Version Control
Track model versions in your metadata for reproducibility:
Performance Monitoring
Metrics
- Mean Average Precision (MAP)
- Recall
- Query latency
Validation
- A/B testing support
- Automated regression testing
- Performance comparison with base model
Technical Challenges & Solutions
- Data Quality Control: Automated validation pipelines detect inconsistencies, duplicates, and annotation errors
- Format Standardization: Handles diverse input formats (CSV, JSON, XML) and normalizes to training-ready structure
- Scale Management: Distributed processing for large datasets (100M+ entries)
- Privacy & Security: Automated PII detection and redaction before training
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