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