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README.md
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<a href="https://app.clear.ml"><img src="https://github.com/allegroai/clearml/blob/master/docs/clearml-logo.svg?raw=true" width="250px"></a>
**ClearML - Auto-Magical Suite of tools to streamline your ML workflow
</br>Experiment Manager, ML-Ops and Data-Management**
**ClearML - Auto-Magical Suite of tools to streamline your AI workflow
</br>Experiment Manager, MLOps, LLMOps, and Data-Management**
</div>

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docs/fundamentals/agents_and_queues.md
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title: Workers & Queues
---
Two major components of MLOps are experiment reproducibility, and the ability to scale work to multiple machines. ClearML workers,
Two major components of MLOps/LLMOps are experiment reproducibility, and the ability to scale work to multiple machines. ClearML workers,
coupled with execution queues, address both these needs.
A ClearML worker is instantiated by launching a ClearML Agent, which is the base for **Automation** in ClearML and can be leveraged to build automated pipelines, launch custom services

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docs/getting_started/ds/best_practices.md
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@ -2,7 +2,7 @@
title: Best Practices
---
This section talks about what made us design ClearML the way we did and how it reflects on ML / DL workflows.
This section talks about what made us design ClearML the way we did and how it reflects on AI workflows.
While ClearML was designed to fit into any workflow, we do feel that working as we describe below brings a lot of advantages from organizing one's workflow
and furthermore, preparing it to scale in the long term.

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docs/getting_started/main.md
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@ -6,7 +6,7 @@ slug: /
ClearML is an open source platform that automates and simplifies developing and managing machine learning solutions
for thousands of data science teams all over the world.
It is designed as an end-to-end MLOps suite allowing you to focus on developing your ML code & automation,
It is designed as an end-to-end MLOps and LLMOps suite allowing you to focus on developing your ML code & automation,
while ClearML ensures your work is reproducible and scalable.
@ -84,7 +84,7 @@ Want a more in depth introduction to ClearML? Choose where you want to get start
<i>
<img src="/docs/latest/icons/ico-mlops-engineer.svg" alt="MLOps engineer logo" />
</i>
<h4>MLOps Engineers</h4>
<h4>MLOps and LLMOps Engineers</h4>
<p>Learn how to use ClearML's automation, orchestration, and tracking tools</p>
<span class="btn-link">
<a href="getting_started/mlops/mlops_first_steps">START HERE</a>
@ -122,6 +122,6 @@ GUI applications
![Webapp gif](../img/gif/webapp_screenshots.gif)
## Who We Are
ClearML is supported by you :heart: and the [clear.ml](https://clear.ml) team, which helps enterprise companies build scalable MLOps.
ClearML is supported by you :heart: and the [clear.ml](https://clear.ml) team, which helps enterprise companies build scalable MLOps/LLMOps.
Join the ClearML community! Your contributions, questions, and input are always welcome. For more information, see [Community Resources](../community.md).

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docs/getting_started/mlops/mlops_first_steps.md
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@ -6,9 +6,9 @@ title: First Steps
This tutorial assumes that you've already [signed up](https://app.clear.ml) to ClearML
:::
ClearML provides tools for **automation**, **orchestration**, and **tracking**, all key in performing effective MLOps.
ClearML provides tools for **automation**, **orchestration**, and **tracking**, all key in performing effective MLOps and LLMOps.
Effective MLOps relies on the ability to scale work beyond one's own computer. Moving from your own machine can be time-consuming.
Effective MLOps and LLMOps rely on the ability to scale work beyond one's own computer. Moving from your own machine can be time-consuming.
Even assuming that you have all the drivers and applications installed, you still need to manage multiple python environments
for different packages / package versions, or worse - manage different Dockers for different package versions.

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docs/getting_started/video_tutorials/agent_remote_execution_and_automation.md
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---
title: Agent Remote Execution and Automation
description: Learn about the ClearML agent.
keywords: [mlops, components, ClearML agent]
keywords: [llmops, mlops, components, ClearML agent]
---

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docusaurus.config.js
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@ -13,7 +13,7 @@ const path = require('path');
module.exports = {
title: 'ClearML',
tagline: 'Auto-Magical Suite of tools to streamline your ML workflow',
tagline: 'Auto-Magical Suite of tools to streamline your AI workflow',
url: 'https://clear.ml',
baseUrl: '/',
onBrokenLinks: 'warn',

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sidebars.js
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@ -11,7 +11,7 @@ module.exports = {
mainSidebar: [
{'Getting Started': ['getting_started/main', {
'Where do I start?': [{'Data Scientists': ['getting_started/ds/ds_first_steps', 'getting_started/ds/ds_second_steps', 'getting_started/ds/best_practices']},
{'MLOps': ['getting_started/mlops/mlops_first_steps','getting_started/mlops/mlops_second_steps','getting_started/mlops/mlops_best_practices']}]
{'MLOps and LLMOps': ['getting_started/mlops/mlops_first_steps','getting_started/mlops/mlops_second_steps','getting_started/mlops/mlops_best_practices']}]
}, 'getting_started/architecture', {'Video Tutorials':
[
'getting_started/video_tutorials/quick_introduction',

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src/pages/index.js
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@ -97,7 +97,7 @@ function Home() {
return (
<Layout
title={`Hello from ${siteConfig.title}`}
description="Turn your code into MLOps with only 2 lines of code with ClearML! Easily develop, orchestrate, and automate ML workflows at scale"
description="Turn your code into MLOps with only 2 lines of code with ClearML! Easily develop, orchestrate, and automate AI workflows at scale"
>
<header className={clsx('hero hero--primary', styles.heroBanner)}>
<div className="container">

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static/img/infinity.svg
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