Mixpeek for ML Engineers
Ship multimodal models to production without building the serving stack from scratch
ML engineers need to evaluate, deploy, and monitor embedding and classification models across text, image, video, and audio. Mixpeek provides the inference infrastructure, feature extraction pipeline, and retrieval layer so you can focus on model quality rather than MLOps plumbing.
What's Broken Today
1Model serving complexity
Deploying CLIP, E5, SigLIP, and custom models behind a consistent API with auto-scaling, batching, and health checks requires significant infrastructure work.
2Evaluation across modalities
Measuring retrieval quality when queries span text, images, and video requires custom evaluation harnesses that most ML teams build ad-hoc.
3Embedding drift detection
Production embedding distributions shift over time as data changes, but most pipelines lack automated drift detection and alerting for vector quality.
4A/B testing retrieval strategies
Comparing hybrid search versus pure vector search, or evaluating new reranking models, requires duplicating infrastructure rather than flipping a configuration.
5Feature store fragmentation
Embeddings, extracted text, classification labels, and other features end up scattered across different storage systems with no unified access layer.
How Mixpeek Helps
Pre-built model serving via Ray Serve
CLIP, E5, SigLIP, and vLLM endpoints are deployed as Ray Serve deployments with auto-scaling, health checks, and unified API access out of the box.
Configurable retriever stages
Chain feature search, attribute filters, reranking, and aggregation stages declaratively. Test different retrieval strategies by modifying configuration, not code.
Semantic drift monitoring
Track embedding distribution changes over time. Detect when production data diverges from training data and trigger model refresh workflows automatically.
Unified feature layer
All extracted features, from embeddings to transcripts to taxonomy labels, are stored as Qdrant payload fields alongside vectors, providing a single source of truth.
How It Works for ML Engineers
Select or register feature extractors
Choose from built-in extractors (CLIP, E5, SigLIP) or register custom model endpoints. Each extractor defines the embedding dimensions and modality it handles.
Configure collection processing
Assign extractors to a collection, specifying which modalities to process and what features to extract. The collection defines your model's production feature pipeline.
Build and test retriever pipelines
Define multi-stage retrievers that chain search, filter, and rerank operations. Compare retrieval quality across different configurations using the same test queries.
Monitor production model performance
Track retrieval latency, embedding drift, and feature extraction success rates. Set up alerts when model quality degrades below acceptable thresholds.
Iterate and deploy model updates
Swap extractors or update models by modifying collection configuration. Re-trigger batch processing to backfill new embeddings while keeping old ones accessible.
Relevant Features
- Ray Serve inference
- Retriever pipelines
- Drift detection
- Taxonomy classification
- Feature extractors
Integrations
- Ray
- vLLM
- Hugging Face
- Qdrant
- Weights & Biases
"Mixpeek cut our time-to-production for new embedding models from three weeks to two days. We configure a new extractor, run a backfill, and compare retrieval metrics side by side without touching infrastructure."
Priya Narayanan
ML Engineer, Vectrix Labs
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.
Anomaly Detection
Identify outliers and anomalous content using embedding distance from cluster centroids. Flag quality issues, novel content, or items that don't match expected patterns.
Get Started as a ML Engineer
See how Mixpeek can help ml engineers build multimodal AI capabilities without the infrastructure overhead.