-**ClearML - Auto-Magical Suite of tools to streamline your ML workflow
-Experiment Manager, ML-Ops and Data-Management**
+**ClearML - Auto-Magical Suite of tools to streamline your AI workflow
+Experiment Manager, MLOps, LLMOps, and Data-Management**
diff --git a/docs/fundamentals/agents_and_queues.md b/docs/fundamentals/agents_and_queues.md
index 1b3cbf9c..6f99d563 100644
--- a/docs/fundamentals/agents_and_queues.md
+++ b/docs/fundamentals/agents_and_queues.md
@@ -2,7 +2,7 @@
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
diff --git a/docs/getting_started/ds/best_practices.md b/docs/getting_started/ds/best_practices.md
index 3bd78682..1e00bd6d 100644
--- a/docs/getting_started/ds/best_practices.md
+++ b/docs/getting_started/ds/best_practices.md
@@ -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.
diff --git a/docs/getting_started/main.md b/docs/getting_started/main.md
index 323d5e70..9d39a43e 100644
--- a/docs/getting_started/main.md
+++ b/docs/getting_started/main.md
@@ -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
- Learn how to use ClearML's automation, orchestration, and tracking tools
START HERE @@ -122,6 +122,6 @@ GUI applications  ## 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). diff --git a/docs/getting_started/mlops/mlops_first_steps.md b/docs/getting_started/mlops/mlops_first_steps.md index 37d66b2b..8a123fd5 100644 --- a/docs/getting_started/mlops/mlops_first_steps.md +++ b/docs/getting_started/mlops/mlops_first_steps.md @@ -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. diff --git a/docs/getting_started/video_tutorials/agent_remote_execution_and_automation.md b/docs/getting_started/video_tutorials/agent_remote_execution_and_automation.md index c21fcd55..cb111245 100644 --- a/docs/getting_started/video_tutorials/agent_remote_execution_and_automation.md +++ b/docs/getting_started/video_tutorials/agent_remote_execution_and_automation.md @@ -1,7 +1,7 @@ --- title: Agent Remote Execution and Automation description: Learn about the ClearML agent. -keywords: [mlops, components, ClearML agent] +keywords: [llmops, mlops, components, ClearML agent] --- diff --git a/docusaurus.config.js b/docusaurus.config.js index d6c28467..b7dd1dfc 100644 --- a/docusaurus.config.js +++ b/docusaurus.config.js @@ -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', diff --git a/sidebars.js b/sidebars.js index 72c82247..ffda6790 100644 --- a/sidebars.js +++ b/sidebars.js @@ -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', diff --git a/src/pages/index.js b/src/pages/index.js index 6f59f0a7..0c767d14 100644 --- a/src/pages/index.js +++ b/src/pages/index.js @@ -97,7 +97,7 @@ function Home() { return (