diff --git a/notebooks/workflows/fig_qualities.svg b/notebooks/workflows/fig_qualities.svg
index 330b5ab..c828daf 100644
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+ h5adABh5adh5adPolyglotModularScalablePolyglotModularScalableReproducibleReproduciblePortablePortableMaintainableMaintainableAutomated
+ style="text-align:center;text-anchor:middle;fill:#000000;fill-opacity:1;stroke:none;stroke-width:1"
+ x="101.94839"
+ y="70.253143">Automated
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diff --git a/notebooks/workflows/index.qmd b/notebooks/workflows/index.qmd
index f7610d3..3ee6120 100644
--- a/notebooks/workflows/index.qmd
+++ b/notebooks/workflows/index.qmd
@@ -7,16 +7,18 @@ Single-cell analysis has revolutionized our understanding of cellular heterogene
In the previous chapters, we've explored strategies for supporting data operability across programming language. Now, we turn our attention to how to effectively integrate these tools and languages into a cohesive and scalable analysis workflow.
-**Productionization** is the process of transforming research-oriented analysis pipelines into robust, scalable, and maintainable workflows that can be reliably executed in a production environment [@fig-productionization]. This transition is essential for ensuring the reproducibility of results, facilitating collaboration among researchers, and enabling the efficient processing of large and complex single-cell datasets.
+**Productionization** is the process of transforming research-oriented analysis pipelines into robust, scalable, and maintainable workflows that can be reliably executed in a production environment ([@fig-productionization]). This transition is essential for ensuring the reproducibility of results, facilitating collaboration among researchers, and enabling the efficient processing of large and complex single-cell datasets.
-{#fig-productionization}
+{#fig-productionization}
In this chapter, we'll delve into the key components and considerations involved in building production-ready multi-language single-cell analysis workflows. We'll explore essential elements such as data storage, compute environments, containerization, workflow management systems, and best practices for reproducibility. By the end of this chapter, you'll have an understanding of the tools and strategies needed to create robust and scalable workflows for single-cell analysis (or any other data-intensive domain).
## Qualities of a Production-Ready Workflow
-Building production-ready workflows for single-cell analysis involves integrating a variety of tools, technologies, and best practices. Here are some key qualities of a production-ready workflow:
+Building production-ready workflows for single-cell analysis involves integrating a variety of tools, technologies, and best practices. In order to meet the demands of large-scale data processing, reproducibility, and collaboration, a production-ready workflow should exhibit the following essential qualities ([@fig-qualities]):
+
+{#fig-qualities}
* **Polyglot**: Seamlessly integrate tools and libraries from different programming languages, allowing you to leverage the strengths of each language for specific tasks. This facilitates the use of specialized tools and optimizes the analysis pipeline for performance and efficiency.
* **Modular**: A well-structured workflow should be composed of modular and reusable components, promoting code maintainability and facilitating collaboration. Each module should have a clear purpose and well-defined inputs and outputs, enabling easy integration and replacement of individual steps within the pipeline.
@@ -27,7 +29,6 @@ Building production-ready workflows for single-cell analysis involves integratin
* **Maintainable**: A production-ready workflow should be well-documented, organized, and easy to understand, facilitating updates, modifications, and troubleshooting. Clear documentation of code, data, and parameters ensures that the workflow remains accessible and usable over time.
-{#fig-qualities}
## Key components