budgetml
Model deployer
Simplifies deployment of machine learning models to production-ready endpoints with minimal configuration and cost.
Deploy a ML inference service on a budget in less than 10 lines of code.
1k stars
26 watching
65 forks
Language: Python
last commit: about 1 year ago
Linked from 1 awesome list
apidata-sciencedeploymentfastapiinferencemachine-learningmlops
kelvins/awesome-mlopsRelated projects:
| Repository | Description | Stars |
|---|---|---|
 roboflow/inference | A platform for deploying and fine-tuning computer vision models in production-ready environments. | 1,401 |
 combust/mleap | Enables deployment of machine learning pipelines from Spark and Scikit-Learn to production | 1,506 |
 paulescu/hands-on-train-and-deploy-ml | A step-by-step guide to building and deploying a Machine Learning-based REST API for predicting crypto prices using Python. | 769 |
 balavenkatesh3322/model_deployment | Provides tools and frameworks for deploying machine learning models in production environments | 73 |
 freedomintelligence/mllm-bench | Evaluates and compares the performance of multimodal large language models on various tasks | 56 |
 nndeploy/nndeploy | An end-to-end model deployment framework providing cross-platform simplicity and high performance | 662 |
 deploykf/deploykf | Builds machine learning platforms on Kubernetes by combining popular tools and services | 381 |
 mlcommons/inference | Measures the performance of deep learning models in various deployment scenarios. | 1,256 |
 vsoch/django-river-ml | A Django plugin for deploying river online machine learning models | 11 |
 caraml-dev/merlin | A platform for deploying and serving machine learning models in a scalable, cost-efficient, and easy-to-use manner | 168 |
 minimaxir/automl-gs | Automates machine learning model creation and optimization for complex datasets | 1,857 |
 mi2datalab/pybreakdown | A Python implementation of a method to explain the predictions of machine learning models | 42 |
 eightbec/fastapi-ml-skeleton | A FastAPI-based framework for serving machine learning models in production-ready applications | 412 |
 mop/bier | This project implements a deep metric learning framework using an adversarial auxiliary loss to improve robustness. | 39 |
 aws-samples/machine-learning-using-k8s | Demystifies deploying machine learning models on Kubernetes | 163 |