iterativemlemArchived

Name: iterative/mlem
Author: iterative

View on GitHub

718 stars42 forksPythonApache-2.018 viewsmlem.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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iterative/dvc
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allegroai/clearml
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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
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Frequently asked questions

What does iterative/mlem do?

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

What are the main features of iterative/mlem?

The main features of iterative/mlem are: Machine Learning Operations, Model Management, Model Serving, Infrastructure and Serving, MLOps and Pipelines, Data Science Tooling, Data Science Tools.

What are some open-source alternatives to iterative/mlem?

Open-source alternatives to iterative/mlem include: 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…

Star history

Open-source alternatives to Mlem

Similar open-source projects, ranked by how many features they share with Mlem.

iterative/dvc
iterative/dvc
15,680View on 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
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View on GitHub15,680
comet-ml/comet-examples
comet-ml/comet-examples
174View on GitHub
Examples of Machine Learning code using Comet.ml
Jupyter Notebook
View on GitHub174
feast-dev/feast
feast-dev/feast
6,727View on 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
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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
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See all 30 alternatives to Mlem