New copy to update website ahead of rebrand

[stichbury]

Hi @tynandebold -- following this ticket kedro-org/kedro-devrel#83 I have a final set of copy changes for the site please:

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1. Main tag

Kedro is an open sourced Python framework for creating maintainable and modular data science code.

Replace with

Kedro is a toolbox for production-ready data science.

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Pipeline Visualisation
Kedro's pipeline visualisation plugin shows a blueprint of your developing data and machine-learning workflows, provides data lineage, keeps track of machine-learning experiments and makes it easier to collaborate with business stakeholders.

Replace with:

Pipeline Visualisation
Kedro-Viz is a blueprint of your data and machine-learning workflows. It provides data lineage, keeps track of machine-learning experiments, and makes it easier to collaborate with business stakeholders.

(I noticed that the font size for "Pipeline Visualisation" is larger than for the other sections that follow -- wanted to confirm this is deliberate and check if it would look better the same size. WDYT?)

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Data Catalog
A series of lightweight data connectors used to save and load data across many different file formats and file systems. Supported file formats include Pandas, Spark, Dask, NetworkX, Pickle, Plotly, Matplotlib and many more. The Data Catalog supports S3, GCP, Azure, sFTP, DBFS and local filesystems. The Data Catalog also includes data and model snapshots for file-based systems.

Replace with:

Data Catalog
A series of lightweight data connectors used to save and load data across many different file formats and file systems. The Data Catalog supports S3, GCP, Azure, sFTP, DBFS, and local filesystems. Supported file formats include Pandas, Spark, Dask, NetworkX, Pickle, Plotly, Matplotlib, and many more. The Data Catalog also includes data and model snapshots for file-based systems.

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Machine Learning Engineering
Puts the "engineering" back into data science because it borrows concepts from software engineering and applies them to machine-learning code. It is the foundation for clean, data science code.
Handles Complexity
Provides the scaffolding to build more complex data and machine-learning pipelines. In addition, there's a focus on spending less time on the tedious "plumbing" required to maintain data science code; this means that you have more time to solve new problems.
Standardisation
Standardises team workflows; the modular structure of Kedro facilitates a higher level of collaboration when teams solve problems together.
Production-Ready
Makes a seamless transition from development to production, as you can write quick, throw-away exploratory code and transition to maintainable, easy-to-share, code experiments quickly.

Make this replacement:

Machine Learning Engineering
Kedro is the foundation for clean data science code. It borrows concepts from software engineering and applies them to machine-learning projects.
Handles Complexity
A Kedro project provides scaffolding for complex data and machine-learning pipelines. You spend less time on tedious "plumbing" and focus instead on solving new problems.
Standardisation
Kedro standardises how data science code is created and ensures teams collaborate to solve problems easily.
Production-Ready
Make a seamless transition from development to production with exploratory code that you can transition to reproducible, maintainable, and modular experiments.

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Integrations
Apache Spark, Pandas, Dask, Matplotlib, Plotly, fsspec, Apache Airflow, Jupyter Notebook and Docker.

Add the following with their logos, probably best to alphabetise if possible:

• Databricks
• Kubeflow
• MLflow
• Amazon SageMaker
• Azure ML
• VertexAI

Amazon SageMaker, Apache Airflow, Apache Spark, Azure ML, Dask, Databricks, Docker, fsspec, Jupyter Notebook, Kubeflow, Matplotlib, MLflow Plotly, Pandas and VertexAI.

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FAQs

New copy:

What is Kedro?

Kedro is an open-source Python framework hosted by the Linux Foundation (LF AI & Data). Kedro uses software engineering best practices to help you build production-ready data science code.

New copy:

What's Kedro's origin story?
Kedro was born at QuantumBlack to reduce technical debt in data science experiments, making an easier transition from experimentation to production. The latest iteration of Kedro is an incubating project within https://lfaidata.foundation/.

New copy:

How can I find out more about Kedro?

You can find the Kedro community on Slack. Discussions from the Slack channels are also archived online, as are those from an earlier set of Discord channels.

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Ready to start?
You are ready to get going with the Kedro workflow. But first, head to our documentation to learn how to install Kedro and then get up to speed with concepts like nodes, pipelines, the data catalog in our introductory tutorial.

Replace the copy

Ready to start?
Visit the introductory tutorial to learn how to install Kedro and get up to speed with concepts like nodes, pipelines, and the data catalog.

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