Quelles sont les fonctionnalités principales de iterative/mlem ?

Les fonctionnalités principales de iterative/mlem sont : Machine Learning Operations, Model Management, Model Serving, Infrastructure and Serving, MLOps and Pipelines, Data Science Tooling, Data Science Tools.

Quelles sont les alternatives open-source à iterative/mlem ?

Les alternatives open-source à iterative/mlem incluent : 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…

iterativemlemArchived

Name: iterative/mlem
Author: iterative

View on GitHub

718 stars42 forksPythonApache-2.06 vuesmlem.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.

Alternatives open source à Mlem

Projets open source similaires, classés selon le nombre de fonctionnalités partagées avec Mlem.

iterative/dvc
iterative/dvc
15,680Voir sur 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
Voir sur GitHub15,680
comet-ml/comet-examples
comet-ml/comet-examples
174Voir sur GitHub
Examples of Machine Learning code using Comet.ml
Jupyter Notebook
Voir sur GitHub174
feast-dev/feast
feast-dev/feast
6,727Voir sur 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
Voir sur GitHub6,727
allegroai/clearml
allegroai/clearml
6,733Voir sur 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
Voir sur GitHub6,733

Voir les 30 alternatives à Mlem

Questions fréquentes

Que fait iterative/mlem ?

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

Historique des stars

Alternatives open source à Mlem

Projets open source similaires, classés selon le nombre de fonctionnalités partagées avec Mlem.

iterative/dvc
iterative/dvc
15,680Voir sur 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
Voir sur GitHub15,680
comet-ml/comet-examples
comet-ml/comet-examples
174Voir sur GitHub
Examples of Machine Learning code using Comet.ml
Jupyter Notebook
Voir sur GitHub174
feast-dev/feast
feast-dev/feast
6,727Voir sur 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
Voir sur GitHub6,727
allegroai/clearml
allegroai/clearml
6,733Voir sur 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
Voir sur GitHub6,733

Voir les 30 alternatives à Mlem