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ClearML has proven to be a valuable tool for data scientists and businesses in solving various critical challenges in the machine learning lifecycle. With seamless integration and collaboration capabilities, users have found it effortless to connect all their tools into ClearML. This has resulted in enhanced efficiency and productivity by streamlining the process of comparing experiments and autoscaling them. Additionally, ClearML has provided a good replacement for DevOps in companies, effectively tracking changes and optimizing the development process.

The ability of ClearML to monitor the effectiveness of implementation cycles and identify areas for improvement has been highly valued by users. They have found it instrumental in providing visibility into experiments as well as managing datasets and models. By addressing the problem of experiment reproducibility and reviewability, ClearML offers a centralized platform for reviewing the entire modeling cycle, ensuring reliable results.

ClearML's queues feature has successfully resolved resource conflicts that arose from multiple users attempting to use the same hardware simultaneously. This capability has significantly improved efficiency by preventing delays caused by resource contention. Furthermore, the ease of integrating various tools into ClearML enables users to run ML workloads at scale without heavy reliance on DevOps, ultimately facilitating the building of an MLOps platform.

The automation component of ClearML has garnered praise from users due to its resilience and usability for data scientists. It has resulted in a significant productivity boost by simplifying complex tasks related to data science workflows. While not currently utilized by all users, the serving and deployment solution offered by ClearML is considered a promising future use case.

Overall, ClearML addresses a range of business problems faced by organizations throughout the machine learning lifecycle. Its seamless integration, scalability, experiment reviewability, and automation features make it a valuable asset in achieving greater efficiency, collaboration, and optimization in data science workflows.

ClearML, from Allegro.Pl headquartered in Poland, is an MLOps platform that enables users to experiment, orchestrate, deploy, and build data stores in one place, an open source tool designed to provide collaborative experiment management and serve as the foundation for data science teams.

Weights & Biases helps machine learning teams build better models.  Practitioners can  debug, compare and reproduce their models — architecture, hyperparameters, git commits, model weights, GPU usage, datasets and predictions — and collaborate with their teammates.

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ClearML

MLOps

From Zero to Hero in Two Lines of Code with ClearML

Allegro.pl (ClearML)

Quick Introduction

A quick introduction on what exactly ClearML is!

💻 Get a server:

Get started by using our free tier servers: https://app.clear.ml
OR by hosting you own: https://github.com/allegroai/clearml-server

📄 Documentation on Fundamentals:

Get Started: https://clear.ml/docs/latest/docs/getting_started/ds/ds_first_steps
Pip Package / ClearML SDK: https://clear.ml/docs/latest/docs/clearml_sdk
ClearML Data: https://clear.ml/docs/latest/docs/clearml_data/clearml_data
ClearML Agent: https://clear.ml/docs/latest/docs/clearml_agent
Supported libraries for automagical integration: https://clear.ml/docs/latest/docs/integrations/libraries

✨ Follow us and star us!

Github page: https://github.com/allegroai/clearml
Twitter: https://twitter.com/clearmlapp
Slack Channel: https://join.slack.com/t/allegroai-trains/shared_invite/zt-c0t13pty-aVUZZW1TSSSg2vyIGVPBhg
LinkedIn: https://www.linkedin.com/company/clearml

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Overview

What is ClearML?