MLflow
clearml
MLflow | clearml | |
---|---|---|
74 | 20 | |
20,461 | 5,985 | |
2.4% | 1.4% | |
9.9 | 7.0 | |
6 days ago | 4 days ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
MLflow
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Future AI Deployment: Automating Full Lifecycle Management with Rollback Strategies and Cloud Migration
AI Model Lifecycle Management:MLflow Documentation
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10 Must-Know Open Source Platform Engineering Tools for AI/ML Workflows
MLflow provides developers with comprehensive tools for managing the entire ML lifecycle. Its four primary components—tracking, models, projects, and model registry—facilitate efficient, reproducible, and scalable ML pipeline building.
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Python in DevOps: Automation, Efficiency, and Scalability
MLOps elevates DevOps principles to AI workloads. The new frontiers of DevOps are managing large model training jobs, handling GPUs, and automating ML pipelines. Python tools like MLflow and Kubeflow make this possible.
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AIOps, DevOps, MLOps, LLMOps – What’s the Difference?
Model management: MLflow, Kubeflow are used for managing the deployment and lifecycle of models.
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Understanding the MLOps Lifecycle
Popular tools for model development are TensorFlow, MLFlow, and PyTorch.
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How to Use KitOps with MLflow
As artificial intelligence (AI) projects grow in complexity, managing dependencies, maintaining reproducibility, and deploying models efficiently become critical challenges. These processes require tools that can streamline development, tracking, and deployment. Tools like KitOps and MLflow simplify these workflows by automating key aspects of the machine learning (ML) project lifecycle. KitOps simplifies the AI project setup, while MLflow keeps track of and manages the machine learning experiments. With these tools, developers can create robust, scalable, and reproducible ML pipelines at scale.
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20 Open Source Tools I Recommend to Build, Share, and Run AI Projects
MLflow is an open source platform for managing the machine learning project lifecycle, from model development to deployment and performance evaluation. It is beneficial for several reasons.
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Top 10 MLOps Tools for 2025
6. MLflow
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Top 8 OpenSource Tools for AI Startups
Star on GitHub ⭐ - MLflow
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10 MLOps Tools That Comply With the EU AI Act
MLflow is an open source platform for managing end-to-end machine learning lifecycle —including experimentation, reproducibility, and deployment. It supports strong governance by tracking data and validating the models. It allows the machine learning teams to log and manage experiments, including model metrics, parameters, and artifacts. This facilitates the reproducibility of results, which is crucial for transparency in AI systems.
clearml
- FLaNK Stack Weekly 12 February 2024
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clearml VS cascade - a user suggested alternative
2 projects | 5 Dec 2023
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cascade alternatives - clearml and MLflow
3 projects | 1 Nov 2023
- Is there any workflow orchestrator that is Hydra friendly ?
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Show HN: Open-source infra for data scientists
It looks like Magniv is targeting Python in general. This is similar to ClearML. What are the differentiating points to Magniv compared to similar products?
It seems like the product also integrates with SCM systems. Are you using gitea and then containers to push code and data to execution like CodeOcean?
https://github.com/allegroai/clearml
https://codeocean.com/
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[D] Drop your best open source Deep learning related Project
Hi there. ClearML is our open-source solution which is part of the PyTorch ecosystem. We would really appreciate it if you read our README and starred us if you like what you see!
- Start with powerful experiment management and scale into full MLOps with only 2 lines of code.
- Everything you need to log, share, and version experiments, orchestrate pipelines, and scale within one open-source MLOps solution.
- Start with powerful experiment management and scale into full MLOps with only 2 lines of code
What are some alternatives?
gensim - Topic Modelling for Humans
metaflow - Build, Manage and Deploy AI/ML Systems
Sacred - Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.
BentoML - The easiest way to serve AI apps and models - Build Model Inference APIs, Job queues, LLM apps, Multi-model pipelines, and more!
H2O - H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
aws-mlu-explain - Visual, Interactive Articles About Machine Learning: https://mlu-explain.github.io/