Mixpeek for AI/ML Engineers
Build multimodal AI applications on production-ready embedding and retrieval infrastructure
AI and ML engineers building multimodal applications need reliable embedding generation, vector indexing, and retrieval infrastructure that scales beyond notebook experiments. Mixpeek provides the serving layer for your models -- handling ingestion, feature extraction, embedding storage, and composable retrieval -- so you can focus on model architecture and evaluation rather than infrastructure plumbing.
What's Broken Today
1Prototype-to-production gap
Models that work in notebooks fail in production due to missing infrastructure for batching, error handling, scaling, and monitoring. Bridging this gap requires significant engineering effort unrelated to model quality.
2Multi-model orchestration complexity
Production multimodal systems often chain multiple models -- embedding, classification, detection, transcription -- requiring careful orchestration of dependencies, versioning, and fallback behavior.
3Embedding infrastructure overhead
Running, scaling, and maintaining embedding model endpoints with GPU provisioning, batching optimization, and health monitoring consumes engineering time that should be spent on model research.
4Evaluation and iteration friction
Comparing retrieval quality across model versions, embedding dimensions, and indexing strategies requires reproducible evaluation pipelines that most teams build ad-hoc.
How Mixpeek Helps
Managed model serving at scale
Deploy embedding and classification models through Mixpeek's distributed Ray-based inference infrastructure with automatic scaling, batching, and health monitoring built in.
Plugin system for custom models
Register custom feature extractors that call your own model endpoints. Plug proprietary or fine-tuned models into the pipeline while leveraging Mixpeek's orchestration, retry logic, and monitoring.
Composable retrieval for evaluation
Build retrieval pipelines with filter, search, and rerank stages. Compare different configurations side by side to evaluate retrieval quality across model versions and embedding strategies.
End-to-end pipeline observability
Monitor embedding throughput, extraction latency, and indexing status through the API. Track model performance metrics across the entire pipeline from ingestion to retrieval.
How It Works for AI/ML Engineers
Register custom models as feature extractors
Package your embedding, classification, or detection models as Mixpeek plugins. Define input/output schemas and configure GPU requirements, batching parameters, and health checks.
Configure extraction pipelines per experiment
Create collections with different extractor configurations to test model variants. Each collection defines which models run, in what order, and how outputs are stored.
Ingest evaluation datasets
Upload evaluation datasets to S3 buckets and trigger batch processing. Mixpeek handles distributed extraction across GPU workers with progress tracking and error reporting.
Build retrieval pipelines for evaluation
Define retriever configurations with different search strategies, reranking models, and scoring weights. Run evaluation queries against each configuration to compare retrieval metrics.
Iterate on model and pipeline configuration
Swap models, adjust embedding dimensions, change chunking strategies, and re-run evaluations. Mixpeek handles reprocessing and re-indexing while you focus on architecture decisions.
Promote winning configuration to production
Once evaluation confirms the best model and retrieval configuration, promote it to production namespaces. Monitor throughput and quality metrics through the observability API.
Relevant Features
- Custom plugins
- Feature extractors
- Retriever pipelines
- Batch processing
- Namespace management
- Model versioning
Integrations
- Ray
- Qdrant
- S3
- HuggingFace
- PyTorch
Frequently Asked Questions
Get Started as a AI/ML Engineer
See how Mixpeek can help ai/ml engineers build multimodal AI capabilities without the infrastructure overhead.