diff --git a/README.md b/README.md index 889c8e48d5d..17c4b5a5c86 100644 --- a/README.md +++ b/README.md @@ -9,33 +9,28 @@ *The fastest way to develop and deploy your AI application today.* +**MLRun** is the first end-to-end open-source MLOps solution for managing and automating your entire analytics and machine-learning life cycle, from data ingestion through model development to full pipeline deployment in production. -**MLRun** is the first end-to-end open source MLOps solution to manage and automate your entire analytics and machine learning life cycle, from data ingestion through model development and full pipeline deployment. + +#### Key Benefits -Benefits --------- +- Develop your training pipeline on any framework, locally and/or on a cluster. +- Leverage the power of the open-source functions marketplace to focus on your research. +- Deploy your pipeline at scale in a single click. +- Monitor your model performance and automate your actions. -With MLRun you can: + +#### Key Features -* Develop your training pipeline on any framework locally and/or on a cluster. -* Leverage the power of the open source function marketplace to focus on your research. -* Deploy your pipeline at scale in a single click. -* Monitor your model performance and automate your actions. - -Components ----------- - -MLRun includes the following components: - -* **Project life-cycle management**: experiment management and tracking of jobs, functions and artifacts. -* **Scalable functions**: turn code to scalable microservices in a single command. -* **Managed Pipelines**: deploy, run and monitor your machine learning execution plan. +- **Project life-cycle management** — experiment management and tracking of jobs, functions, and artifacts. +- **Scalable functions** — turn code to scalable microservices in a single command. +- **Managed pipelines** — deploy, run, and monitor your machine-learning execution plan. MLRun features a Python package (`mlrun`), a command-line interface (`mlrun`), and a graphical user interface (the MLRun dashboard). -Read more [**detailed documentation here**](https://mlrun.readthedocs.io/en/latest/) +▶ For more information, see the [MLRun Python package documentation](https://mlrun.readthedocs.io). -## In This Document +#### In This Document - [General Concept and Motivation](#general-concept-and-motivation) - [The Challenge](#the-challenge) - [The MLRun Vision](#the-mlrun-vision) diff --git a/docs/index.rst b/docs/index.rst index 799fbe520e0..95b8b4b17ed 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -11,26 +11,26 @@ MLRun Package Documentation Introduction ************ -**MLRun** is the first end-to-end open source MLOps solution to manage and automate your entire analytics and machine learning life cycle, from data ingestion through model development and full pipeline deployment. +**MLRun** is the first end-to-end open-source MLOps solution for managing and automating your entire analytics and machine-learning life cycle, from data ingestion through model development to full pipeline deployment in production. -Benefits --------- +Key Benefits +------------ -With MLRun you can: +MLRun provides the following key benefits: -* Develop your training pipeline on any framework locally and/or on a cluster. -* Leverage the power of the open source function marketplace to focus on your research. -* Deploy your pipeline at scale in a single click. -* Monitor your model performance and automate your actions. +- Develop your training pipeline on any framework, locally and/or on a cluster. +- Leverage the power of the open-source functions marketplace to focus on your research. +- Deploy your pipeline at scale in a single click. +- Monitor your model performance and automate your actions. -Components ----------- +Key Features +-------------- -MLRun includes the following components: +MLRun includes the following key features: -* **Project life-cycle management**: experiment management and tracking of jobs, functions and artifacts. -* **Scalable functions**: turn code to scalable microservices in a single command. -* **Managed Pipelines**: deploy, run and monitor your machine learning execution plan. +- **Project life-cycle management** — experiment management and tracking of jobs, functions, and artifacts. +- **Scalable functions** — turn code to scalable microservices in a single command. +- **Managed pipelines** — deploy, run, and monitor your machine-learning execution plan. .. toctree:: :maxdepth: 1