iterativemlemArchived

Name: iterative/mlem
Author: iterative

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718 نجوم42 تفرعاتPythonApache-2.011 مشاهداتmlem.ai

Mlem

🐶 A tool to package, serve, and deploy any ML model on any platform. Archived to be resurrected one day🤞

Features

Machine Learning Operations - Tool for packaging and deploying machine learning models.
Model Management - Tool for packaging and deploying models across various platforms.
Model Serving - Deploys models using GitOps principles for versioning and serving.
Infrastructure and Serving - Version and deploy ML models using GitOps.
MLOps and Pipelines - GitOps-based model versioning and deployment.
Data Science Tooling - GitOps-based model versioning and deployment.
Data Science Tools - GitOps-based model versioning and deployment.

بدائل مفتوحة المصدر لـ Mlem

مشاريع مفتوحة المصدر مشابهة، مرتبة حسب عدد الميزات المشتركة مع Mlem.

iterative/dvc
iterative/dvc
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DVC is a data versioning tool and pipeline orchestrator designed to track large datasets and machine learning models. It functions as a system for managing large data artifacts by storing lightweight metadata in version control while keeping the actual binaries in a separate cache. The project serves as an experiment tracker and remote storage synchronizer, enabling the execution and comparison of machine learning iterations based on hyperparameters and performance metrics. It provides a bridge for pushing and pulling these large data artifacts between local environments and cloud or on-premi
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comet-ml/comet-examples
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Examples of Machine Learning code using Comet.ml
Jupyter Notebook
عرض على GitHub174
feast-dev/feast
feast-dev/feast
6,727عرض على GitHub
Feast is an open-source feature store for machine learning that provides a central platform for defining, storing, and serving features across both training and inference workflows. It operates as a declarative system where feature definitions are written as code in Python files, synchronized to a central registry, and made available for low-latency online retrieval or point-in-time correct historical joins for training datasets. The project abstracts storage behind a pluggable architecture, allowing offline and online backends to be swapped without changing retrieval logic, and coordinates ma
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allegroai/clearml
allegroai/clearml
6,733عرض على GitHub
ClearML is a comprehensive MLOps platform designed to manage the entire machine learning lifecycle. It functions as an experiment tracking tool, a data versioning system, and a pipeline orchestrator, while providing infrastructure for GPU cluster management and model serving. The platform is distinguished by its ability to handle hybrid-cloud compute scheduling and fractional GPU allocation, allowing multiple workloads to share a single hardware accelerator. It employs a metadata-based approach to data versioning, using virtual views to track large datasets and artifacts without duplicating r
Python
عرض على GitHub6,733

عرض جميع البدائل الـ 30 لـ Mlem

الأسئلة الشائعة

ما هي وظيفة iterative/mlem؟

🐶 A tool to package, serve, and deploy any ML model on any platform. Archived to be resurrected one day🤞

ما هي الميزات الرئيسية لـ iterative/mlem؟

الميزات الرئيسية لـ iterative/mlem هي: Machine Learning Operations, Model Management, Model Serving, Infrastructure and Serving, MLOps and Pipelines, Data Science Tooling, Data Science Tools.

ما هي البدائل مفتوحة المصدر لـ iterative/mlem؟

تشمل البدائل مفتوحة المصدر لـ iterative/mlem: iterative/dvc — DVC is a data versioning tool and pipeline orchestrator designed to track large datasets and machine learning models.… comet-ml/comet-examples — Examples of Machine Learning code using Comet.ml. feast-dev/feast — Feast is an open-source feature store for machine learning that provides a central platform for defining, storing, and… dslp/dslp — The Data Science Lifecycle Process is a process for taking data science teams from Idea to Value repeatedly and… asavinov/lambdo — Feature engineering and machine learning: together at last! allegroai/clearml — ClearML is a comprehensive MLOps platform designed to manage the entire machine learning lifecycle. It functions as an…

سجل النجوم

بدائل مفتوحة المصدر لـ Mlem

مشاريع مفتوحة المصدر مشابهة، مرتبة حسب عدد الميزات المشتركة مع Mlem.

iterative/dvc
iterative/dvc
15,680عرض على GitHub
DVC is a data versioning tool and pipeline orchestrator designed to track large datasets and machine learning models. It functions as a system for managing large data artifacts by storing lightweight metadata in version control while keeping the actual binaries in a separate cache. The project serves as an experiment tracker and remote storage synchronizer, enabling the execution and comparison of machine learning iterations based on hyperparameters and performance metrics. It provides a bridge for pushing and pulling these large data artifacts between local environments and cloud or on-premi
Python
عرض على GitHub15,680
comet-ml/comet-examples
comet-ml/comet-examples
174عرض على GitHub
Examples of Machine Learning code using Comet.ml
Jupyter Notebook
عرض على GitHub174
feast-dev/feast
feast-dev/feast
6,727عرض على GitHub
Feast is an open-source feature store for machine learning that provides a central platform for defining, storing, and serving features across both training and inference workflows. It operates as a declarative system where feature definitions are written as code in Python files, synchronized to a central registry, and made available for low-latency online retrieval or point-in-time correct historical joins for training datasets. The project abstracts storage behind a pluggable architecture, allowing offline and online backends to be swapped without changing retrieval logic, and coordinates ma
Pythonbig-datadata-engineeringdata-quality
عرض على GitHub6,727
allegroai/clearml
allegroai/clearml
6,733عرض على GitHub
ClearML is a comprehensive MLOps platform designed to manage the entire machine learning lifecycle. It functions as an experiment tracking tool, a data versioning system, and a pipeline orchestrator, while providing infrastructure for GPU cluster management and model serving. The platform is distinguished by its ability to handle hybrid-cloud compute scheduling and fractional GPU allocation, allowing multiple workloads to share a single hardware accelerator. It employs a metadata-based approach to data versioning, using virtual views to track large datasets and artifacts without duplicating r
Python
عرض على GitHub6,733

عرض جميع البدائل الـ 30 لـ Mlem