MLflow VS clearml

Tools: Implement logging using tools like MLFlow or Weights & Biases (W&B), which provide a structured way to track experiments, compare them visually, and share findings with your team. These tools integrate seamlessly with most machine learning frameworks, making it easier to adopt them in your existing workflows.

CometML and mlMLFlow are popular development and experimentation tools, although some express concerns about their proprietary and weak data storage with its lack of tamper-proof guarantees.

MLflow is an open source MLOps tool that allows users to manage the entire life cycle of machine learning models. It has four key components:

Experiment tracking tools like MLflow, Weights and Biases, and Neptune.ai provide a pipeline that automatically tracks meta-data and artifacts generated from each experiment you run. Although they have varying features and functionalities, experiment tracking tools provide a systematic structure that handles the iterative model development approach.

How can this be? The current state of practice in AI/ML work requires adaptivity, which is uncommon in classical computational fields. There are myriad tools that capture the work across the many instances of the AI/ML lifecycle. The idea that any one tool could sufficiently capture the dynamic work is unrealistic. Take, for example, an experiment tracking tool like W&B or MLFlow; some form of experiment tracking is necessary in typical model training lifecycles. Such a tool requires some notion of a dataset. However, a tool focusing on experiment tracking is orthogonal to the needs of analyzing model performance at the data sample level, which is critical to understanding the failure modes of models. The way one does this depends on the type of data and the AI/ML task at hand. In other words, MLOps is inherently an intricate mosaic, as the capabilities and best practices of AI/ML work evolve.

MLflow is an open-source platform for managing the end-to-end machine learning lifecycle. It includes features for experiment tracking, model versioning, and deployment, enabling developers to track and compare experiments, package models into reproducible runs, and manage model deployment across multiple environments.

Platforms such as MLflow monitor the development stages of machine learning models. In parallel, Data Version Control (DVC) brings version control system-like functions to the realm of data sets and models.

MLFlow - http://mlflow.org

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/

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!