Resources for serving models in production
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Updated
Sep 25, 2019 - Python
Resources for serving models in production
Applied Machine Learning Projects
Heterogeneous System ML Pipeline Scheduling Framework with Triton Inference Server as Backend
Example solution to the MLOps Case Study covering both online and batch processing.
A curated list of awesome open source and commercial platforms for serving models in production 🚀
Integrating Aporia ML model monitoring into a Bodywork serving pipeline.
🌐 Language identification for Scandinavian languages
Big ML Project with infrastructure (MLflow, Minio, Grafana), backend (FastAPI, Catboost) and frontend (React, Maplibre)
Miscellaneous codes and writings for MLOps
Collection of OSS models that are containerized into a serving container
Serving large ml models independently and asynchronously via message queue and kv-storage for communication with other services [EXPERIMENT]
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