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    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.

    Get Started as a ML EngineerRead the Docs

    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 payload fields alongside vectors, providing a single source of truth.

    How It Works for ML Engineers

    1

    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.

    2

    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.

    3

    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.

    4

    Monitor production model performance

    Track retrieval latency, embedding drift, and feature extraction success rates. Set up alerts when model quality degrades below acceptable thresholds.

    5

    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

    Can I bring my own fine-tuned models to Mixpeek?
    Yes. You can register custom feature extractors that point to your own model serving endpoints. Mixpeek's pipeline will call your endpoint during batch processing and index the returned embeddings alongside built-in features.
    How does Mixpeek handle multi-vector search with different embedding models?
    The Mixpeek Vector Store supports named vectors, so each feature extractor's embeddings are stored under a distinct vector name within the same point. Retriever stages can target specific vector names, enabling hybrid search across models with different embedding dimensions.
    Does Mixpeek support evaluation metrics for retrieval quality?
    The retriever execution API returns scored results that you can use with standard IR metrics (nDCG, MRR, recall@k). You can build evaluation loops that compare retriever configurations against a labeled test set through the same API endpoints.
    How are GPU resources allocated for model inference?
    Mixpeek uses Ray Serve for model inference, which handles auto-scaling based on request load. Batch processing routes work through Ray Data pipelines, distributing GPU-bound tasks across available workers with configurable concurrency.

    Related Resources

    Get Started as a ML Engineer

    See how Mixpeek can help ml engineers build multimodal AI capabilities without the infrastructure overhead.

    Schedule a DemoRead the Docs