Platforme MLOps end-to-end
For platformă pentru gestionarea ciclului de viață ML, the strongest matches are allegroai/clearml (ClearML is a comprehensive open-source MLOps platform that tracks), kubeflow/kubeflow (Kubeflow is a comprehensive Kubernetes-native MLOps platform that orchestrates) and mlflow/mlflow (MLflow is the leading open-source platform for managing the). transformerlab/transformerlab-app and polyaxon/polyaxon round out the shortlist. Each is ranked by relevance to your query, popularity and recent activity.
Suite software complexe pentru gestionarea dezvoltării, deployment-ului, monitorizării și orchestrarea automatizată a pipeline-urilor pentru modele de machine learning.
Platforme MLOps end-to-end
Găsește cele mai bune repo-uri cu AI.Vom căuta cele mai potrivite repository-uri folosind AI.
- allegroai/clearml
allegroai/clearml
6,733Vezi pe GitHubClearML 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
ClearML is a comprehensive open-source MLOps platform that tracks experiments, versions data, orchestrates pipelines, serves models, monitors performance, and manages compute resources, covering nearly all the lifecycle stages and features you need in a unified workflow.
PythonExperiment TrackingExperiment Tracking SystemsHyperparameter Optimizers - kubeflow/kubeflow
kubeflow/kubeflow
15,739Vezi pe GitHubKubeflow is a Kubernetes machine learning platform and containerized toolkit designed to orchestrate the entire machine learning lifecycle. It functions as an MLOps workflow orchestrator and infrastructure layer for building, training, and deploying models within containerized environments. The project provides specialized infrastructure for scaling compute resources and managing GPU workloads for large-scale distributed training. It automates the transition of models from experimental development to production through workflow orchestration and model deployment services. The platform covers
Kubeflow is a comprehensive Kubernetes-native MLOps platform that orchestrates the full machine learning lifecycle — from data preparation and distributed training through pipeline automation to model serving — making it a strong fit for your end-to-end workflow needs.
Model Serving - mlflow/mlflow
mlflow/mlflow
26,554Vezi pe GitHubMLflow is the leading open-source platform for managing the ML lifecycle, covering experiment tracking, model registry, and model serving, with integrations for orchestration and hyperparameter tuning, though it lacks native model monitoring, a feature store, and full data versioning — making it a genuine but not fully comprehensive MLOps platform.
PythonData LineageDeep Learning Experiment TrackersExperiment Tracking - transformerlab/transformerlab-app
transformerlab/transformerlab-app
5,103Vezi pe GitHubTransformerLab is an MLOps orchestration platform and research environment designed for the training, fine-tuning, and evaluation of large language models. It serves as a centralized control plane for managing machine learning jobs and coordinating distributed GPU compute across hybrid cloud and on-premise providers. The platform distinguishes itself through agent-driven model optimization, using AI assistants to analyze metrics and automatically propose and queue hyperparameter experiments. It provides a remote development environment that allows users to launch interactive notebooks, code e
TransformerLab is an open-source MLOps orchestration platform that manages training, fine-tuning, evaluation, and deployment of models, with experiment tracking, hyperparameter tuning, and model serving, though it focuses on large language models and omits some features like a built-in feature store or explicit drift detection.
PythonExperiment TrackingExperiment TrackingExperiment Tracking Systems - polyaxon/polyaxon
polyaxon/polyaxon
3,707Vezi pe GitHubPolyaxon is a Kubernetes-native machine learning orchestration platform and MLOps pipeline orchestrator. It serves as a control plane for managing distributed deep learning workloads, automated machine learning pipelines, and experiment tracking. The platform distinguishes itself through specialized services for distributed training management, including MPI-based coordination for PyTorch and TensorFlow. It provides an automated hyperparameter optimization service utilizing Bayesian, random, and grid search algorithms, alongside managed interactive AI workspaces for launching Jupyter notebook
Polyaxon is an open-source MLOps platform that orchestrates the full ML lifecycle — including experiment tracking, pipeline automation, model deployment, and monitoring — making it a strong fit for a unified end-to-end workflow.
MDXData LineageExperiment TrackingExperiment Tracking Systems - pycaret/pycaret
pycaret/pycaret
9,811Vezi pe GitHubPyCaret is a Python AutoML platform and MLOps lifecycle manager designed to automate machine learning workflows. It functions as a low-code environment that leverages a scikit-learn native engine to execute preprocessing, training, and evaluation for tabular data. The platform distinguishes itself as an LLM-powered ML copilot, using large language model agents to analyze datasets, design experiment configurations, and explain model results. It also serves as a Kubernetes ML orchestrator and model registry, enabling the versioning of trained pipelines and their promotion to production API endp
PyCaret is an AutoML and MLOps lifecycle manager that covers experimentation, model registry, and deployment, making it a valid end-to-end MLOps platform, though it focuses on low-code automation and may not include dedicated monitoring or feature stores.
PythonDrift DetectionExperiment TrackingHyperparameter Tuning - netflix/metaflow
Netflix/metaflow
9,764Vezi pe GitHubMetaflow is a Python machine learning framework and MLOps workflow orchestrator designed to manage the lifecycle of data pipelines from local prototyping to production. It serves as a distributed compute manager and an experiment tracking system, enabling the creation of reproducible pipelines that transition between development and high-availability production environments. The framework distinguishes itself through an integrated checkpointing system that automatically persists intermediate data artifacts to remote storage, allowing failed runs to be resumed from the last successful step. It
Metaflow is a Python MLOps workflow orchestrator and experiment tracker that manages pipelines from prototyping to production, but it lacks native model serving, monitoring, and feature store capabilities, so it fits the category but is narrower in scope.
PythonExperiment TrackingExperiment Tracking Systems - clearml/clearml
clearml/clearml
6,740Vezi pe GitHubClearML is a comprehensive MLOps platform designed to manage the end-to-end machine learning lifecycle, from initial experimentation to production deployment. It provides a suite of integrated tools including a pipeline orchestrator for automating workflows, an experiment tracking tool for logging hyperparameters and metrics, and a metadata-driven data versioning system for managing large-scale datasets and model artifacts. The platform is distinguished by its advanced compute management and serving capabilities. It features a GPU compute manager that supports fractional resource slicing and
ClearML is a comprehensive MLOps platform that covers experiment tracking, pipeline orchestration, data versioning, model serving, and hyperparameter tuning, making it a strong fit for managing the full machine learning lifecycle.
PythonExperiment TrackingHyperparameter OptimizationHyperparameter Optimizers
