Mixpeek for Data Scientists
Explore and analyze multimodal datasets without building custom extraction pipelines
Data scientists working with video, image, and audio data need to extract features, explore distributions, and build classification models without spending weeks on infrastructure. Mixpeek provides the extraction and indexing layer so you can focus on analysis and model development.
What's Broken Today
1Manual feature extraction is slow
Writing custom scripts to extract embeddings, transcripts, and labels from thousands of media files takes weeks and produces fragile, one-off code.
2No unified view of multimodal data
Features end up in CSV files, embeddings in numpy arrays, and metadata in spreadsheets. There is no single place to query across modalities.
3Taxonomy and labeling at scale
Manually labeling media content for classification is expensive. Mapping content to standardized taxonomies (like IAB) requires specialized tooling.
4Reproducibility challenges
When extraction parameters change or new data arrives, reproducing an analysis requires re-running fragile notebooks and manual data wrangling.
How Mixpeek Helps
Automated feature extraction
Upload a dataset, configure extractors, and get embeddings, transcripts, classifications, and metadata indexed and queryable within hours instead of weeks.
Queryable feature store
All extracted features are stored as Qdrant payload fields. Run similarity searches, filter by metadata, and explore feature distributions through a single API.
Taxonomy enrichment
Automatically classify content against IAB and custom taxonomies. Explore how your dataset maps to industry-standard categories without manual labeling.
Reproducible pipelines
Collection configurations capture exactly which extractors and parameters were used. Re-trigger processing to reproduce results or apply updated models to the same dataset.
How It Works for Data Scientists
Upload your dataset
Push media files to a Mixpeek bucket. Each file's metadata and source information are tracked for reproducibility.
Configure and run extraction
Select feature extractors that match your analysis goals: embeddings for similarity, transcripts for text analysis, taxonomy labels for classification.
Explore features through the API
Use the Python SDK in Jupyter to run similarity searches, filter by extracted labels, and explore embedding distributions across your dataset.
Export and analyze
Pull extracted features into DataFrames for statistical analysis, visualization, or as training data for downstream models.
Relevant Features
- Feature extractors
- Taxonomy classification
- Clustering
- Retriever queries
- Batch processing
Integrations
- Jupyter
- Python SDK
- Pandas
- Qdrant
- Hugging Face
"I used to spend the first two weeks of every project writing extraction scripts. Now I upload data, pick extractors, and start exploring features the same day."
Elena Vasquez
Senior Data Scientist, Insight Research Group
Frequently Asked Questions
Related Resources
Industry Solutions
Implementation Recipes
Semantic Multimodal Search
Unified semantic search across all content types. Query by natural language and retrieve relevant video clips, images, audio segments, and documents based on meaning—not keywords or manual tags.
Feature Extraction
Multi-tier feature extraction that decomposes content into searchable components: embeddings, transcripts, detected objects, OCR text, scene boundaries, and more. The foundation for all downstream retrieval and analysis.
Clustering & Theme Discovery
Unsupervised clustering that groups content into semantic themes using HDBSCAN. Surfaces hidden patterns, content variants, and outliers without requiring predefined labels.
Get Started as a Data Scientist
See how Mixpeek can help data scientists build multimodal AI capabilities without the infrastructure overhead.