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iterative/mlemArchived

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718 स्टार्स·42 फोर्क्स·Python·Apache-2.0·6 व्यूज़mlem.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.

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Mlem के ओपन-सोर्स विकल्प

समान ओपन-सोर्स प्रोजेक्ट्स, जो Mlem के साथ साझा की गई सुविधाओं के आधार पर रैंक किए गए हैं।
  • iterative/dvciterative का अवतार

    iterative/dvc

    15,680GitHub पर देखें↗

    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
    GitHub पर देखें↗15,680
  • comet-ml/comet-examplescomet-ml का अवतार

    comet-ml/comet-examples

    174GitHub पर देखें↗

    Examples of Machine Learning code using Comet.ml

    Jupyter Notebook
    GitHub पर देखें↗174
  • feast-dev/feastfeast-dev का अवतार

    feast-dev/feast

    6,727GitHub पर देखें↗

    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
    GitHub पर देखें↗6,727
  • allegroai/clearmlallegroai का अवतार

    allegroai/clearml

    6,733GitHub पर देखें↗

    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
    GitHub पर देखें↗6,733
Mlem के सभी 30 विकल्प देखें→

अक्सर पूछे जाने वाले प्रश्न

iterative/mlem क्या करता है?

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

iterative/mlem की मुख्य विशेषताएं क्या हैं?

iterative/mlem की मुख्य विशेषताएं हैं: Machine Learning Operations, Model Management, Model Serving, Infrastructure and Serving, MLOps and Pipelines, Data Science Tooling, Data Science Tools।

iterative/mlem के कुछ ओपन-सोर्स विकल्प क्या हैं?

iterative/mlem के ओपन-सोर्स विकल्पों में शामिल हैं: 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…