Merge branch 'dev' into 395-typo-chapter-1322

.all-contributorsrc

@@ -62,20 +62,20 @@
             "profile": "https://github.com/JaredP94",
             "contributions": []
         },
-        {
-            "login": "mpstewart1",
-            "name": "Matthew Stewart",
-            "avatar_url": "https://avatars.githubusercontent.com/mpstewart1",
-            "profile": "https://github.com/mpstewart1",
-            "contributions": []
-        },
         {
             "login": "jasonjabbour",
             "name": "jasonjabbour",
             "avatar_url": "https://avatars.githubusercontent.com/jasonjabbour",
             "profile": "https://github.com/jasonjabbour",
             "contributions": []
         },
+        {
+            "login": "mpstewart1",
+            "name": "Matthew Stewart",
+            "avatar_url": "https://avatars.githubusercontent.com/mpstewart1",
+            "profile": "https://github.com/mpstewart1",
+            "contributions": []
+        },
         {
             "login": "ishapira1",
             "name": "Itai Shapira",

README.md

@@ -95,8 +95,8 @@ This project follows the [all-contributors](https://allcontributors.org) specifi
       <td align="center" valign="top" width="20%"><a href="https://github.com/kai4avaya"><img src="https://avatars.githubusercontent.com/kai4avaya?s=100" width="100px;" alt="kai4avaya"/><br /><sub><b>kai4avaya</b></sub></a><br /></td>
       <td align="center" valign="top" width="20%"><a href="https://github.com/eliasab16"><img src="https://avatars.githubusercontent.com/eliasab16?s=100" width="100px;" alt="Elias Nuwara"/><br /><sub><b>Elias Nuwara</b></sub></a><br /></td>
       <td align="center" valign="top" width="20%"><a href="https://github.com/JaredP94"><img src="https://avatars.githubusercontent.com/JaredP94?s=100" width="100px;" alt="Jared Ping"/><br /><sub><b>Jared Ping</b></sub></a><br /></td>
-      <td align="center" valign="top" width="20%"><a href="https://github.com/mpstewart1"><img src="https://avatars.githubusercontent.com/mpstewart1?s=100" width="100px;" alt="Matthew Stewart"/><br /><sub><b>Matthew Stewart</b></sub></a><br /></td>
       <td align="center" valign="top" width="20%"><a href="https://github.com/jasonjabbour"><img src="https://avatars.githubusercontent.com/jasonjabbour?s=100" width="100px;" alt="jasonjabbour"/><br /><sub><b>jasonjabbour</b></sub></a><br /></td>
+      <td align="center" valign="top" width="20%"><a href="https://github.com/mpstewart1"><img src="https://avatars.githubusercontent.com/mpstewart1?s=100" width="100px;" alt="Matthew Stewart"/><br /><sub><b>Matthew Stewart</b></sub></a><br /></td>
     </tr>
     <tr>
       <td align="center" valign="top" width="20%"><a href="https://github.com/ishapira1"><img src="https://avatars.githubusercontent.com/ishapira1?s=100" width="100px;" alt="Itai Shapira"/><br /><sub><b>Itai Shapira</b></sub></a><br /></td>

contents/contributors.qmd

@@ -82,8 +82,8 @@ We extend our sincere thanks to the diverse group of individuals who have genero
       <td align="center" valign="top" width="20%"><a href="https://github.com/kai4avaya"><img src="https://avatars.githubusercontent.com/kai4avaya?s=100" width="100px;" alt="kai4avaya"/><br /><sub><b>kai4avaya</b></sub></a><br /></td>
       <td align="center" valign="top" width="20%"><a href="https://github.com/eliasab16"><img src="https://avatars.githubusercontent.com/eliasab16?s=100" width="100px;" alt="Elias Nuwara"/><br /><sub><b>Elias Nuwara</b></sub></a><br /></td>
       <td align="center" valign="top" width="20%"><a href="https://github.com/JaredP94"><img src="https://avatars.githubusercontent.com/JaredP94?s=100" width="100px;" alt="Jared Ping"/><br /><sub><b>Jared Ping</b></sub></a><br /></td>
-      <td align="center" valign="top" width="20%"><a href="https://github.com/mpstewart1"><img src="https://avatars.githubusercontent.com/mpstewart1?s=100" width="100px;" alt="Matthew Stewart"/><br /><sub><b>Matthew Stewart</b></sub></a><br /></td>
       <td align="center" valign="top" width="20%"><a href="https://github.com/jasonjabbour"><img src="https://avatars.githubusercontent.com/jasonjabbour?s=100" width="100px;" alt="jasonjabbour"/><br /><sub><b>jasonjabbour</b></sub></a><br /></td>
+      <td align="center" valign="top" width="20%"><a href="https://github.com/mpstewart1"><img src="https://avatars.githubusercontent.com/mpstewart1?s=100" width="100px;" alt="Matthew Stewart"/><br /><sub><b>Matthew Stewart</b></sub></a><br /></td>
     </tr>
     <tr>
       <td align="center" valign="top" width="20%"><a href="https://github.com/ishapira1"><img src="https://avatars.githubusercontent.com/ishapira1?s=100" width="100px;" alt="Itai Shapira"/><br /><sub><b>Itai Shapira</b></sub></a><br /></td>

contents/ops/ops.qmd

@@ -10,7 +10,7 @@ Resources: [Slides](#sec-embedded-aiops-resource), [Videos](#sec-embedded-aiops-
 
 ![_DALL·E 3 Prompt: Create a detailed, wide rectangular illustration of an AI workflow. The image should showcase the process across six stages, with a flow from left to right: 1. Data collection, with diverse individuals of different genders and descents using a variety of devices like laptops, smartphones, and sensors to gather data. 2. Data processing, displaying a data center with active servers and databases with glowing lights. 3. Model training, represented by a computer screen with code, neural network diagrams, and progress indicators. 4. Model evaluation, featuring people examining data analytics on large monitors. 5. Deployment, where the AI is integrated into robotics, mobile apps, and industrial equipment. 6. Monitoring, showing professionals tracking AI performance metrics on dashboards to check for accuracy and concept drift over time. Each stage should be distinctly marked and the style should be clean, sleek, and modern with a dynamic and informative color scheme._](images/png/cover_ml_ops.png)
 
-This chapter explores the practices and architectures needed to effectively develop, deploy, and manage ML models across their entire lifecycle. We examine the various phases of the ML process, including data collection, model training, evaluation, deployment, and monitoring. The importance of automation, collaboration, and continuous improvement is also discussed. We contrast different environments for ML model deployment, from cloud servers to embedded edge devices, and analyze their distinct constraints. We demonstrate how to tailor ML system design and operations through concrete examples for reliable and optimized model performance in any target environment. The goal is to provide readers with a comprehensive understanding of ML model management so they can successfully build and run ML applications that sustainably deliver value.
+This chapter explores the practices and architectures needed to effectively develop, deploy, and manage ML models across their entire lifecycle. We examine the various phases of the ML process, including data collection, model training, evaluation, deployment, and monitoring. The importance of automation, collaboration, and continuous improvement is also something we discuss. We contrast different environments for ML model deployment, from cloud servers to embedded edge devices, and analyze their distinct constraints. We demonstrate how to tailor ML system design and operations through concrete examples for reliable and optimized model performance in any target environment. The goal is to provide readers with a comprehensive understanding of ML model management so they can successfully build and run ML applications that sustainably deliver value.
 
 ::: {.callout-tip}
 
@@ -64,7 +64,7 @@ While Agile and DevOps share common principles around collaboration and feedback
 
 ### MLOps
 
-[MLOps](https://cloud.google.com/solutions/machine-learning/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning), on the other hand, stands for MLOps, and it extends the principles of DevOps to the ML lifecycle. MLOps aims to automate and streamline the end-to-end ML lifecycle, from data preparation and model development to deployment and monitoring. The main focus of MLOps is to facilitate collaboration between data scientists, data engineers, and IT operations and to automate the deployment, monitoring, and management of ML models. Some key factors led to the rise of MLOps.
+[MLOps](https://cloud.google.com/solutions/machine-learning/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning), on the other hand, stands for Machine Learning Operations, and it extends the principles of DevOps to the ML lifecycle. MLOps aims to automate and streamline the end-to-end ML lifecycle, from data preparation and model development to deployment and monitoring. The main focus of MLOps is to facilitate collaboration between data scientists, data engineers, and IT operations and to automate the deployment, monitoring, and management of ML models. Some key factors led to the rise of MLOps.
 
 * **Data drift:** Data drift degrades model performance over time, motivating the need for rigorous monitoring and automated retraining procedures provided by MLOps.
 * **Reproducibility:** The lack of reproducibility in machine learning experiments motivated MLOps systems to track code, data, and environment variables to enable reproducible ML workflows.
@@ -73,7 +73,7 @@ While Agile and DevOps share common principles around collaboration and feedback
 * **Friction:** The friction in manually retraining and deploying models motivated the need for MLOps systems that automate machine learning deployment pipelines.
 * **Optimization:** The complexity of configuring machine learning infrastructure motivated the need for MLOps platforms with optimized, ready-made ML infrastructure.
 
-While DevOps and MLOps share the common goal of automating and streamlining processes, they differ significantly in their focus and challenges. DevOps primarily deals with software development and IT operations, aiming to improve collaboration between these teams and automate software delivery. In contrast, MLOps focuses on the machine learning lifecycle, addressing additional complexities such as [data versioning](https://dvc.org/), [model versioning](https://dvc.org/), and [model monitoring](https://www.fiddler.ai/). MLOps requires collaboration among a broader range of stakeholders, including data scientists, data engineers, and IT operations. It goes beyond the scope of traditional DevOps by incorporating the unique challenges of managing ML models throughout their lifecycle. @tbl-mlops provides a side-by-side comparison of DevOps and MLOps, highlighting their key differences and similarities.
+While DevOps and MLOps share the common goal of automating and streamlining processes, they differ significantly in their focus and challenges. DevOps primarily deals with software development and IT operations. It enables collaboration between these teams and automate software delivery. In contrast, MLOps focuses on the machine learning lifecycle. It addresses additional complexities such as [data versioning](https://dvc.org/), [model versioning](https://dvc.org/), and [model monitoring](https://www.fiddler.ai/). MLOps requires collaboration among a broader range of stakeholders, including data scientists, data engineers, and IT operations. It goes beyond the scope of traditional DevOps by incorporating the unique challenges of managing ML models throughout their lifecycle. @tbl-mlops provides a side-by-side comparison of DevOps and MLOps, highlighting their key differences and similarities.
 
 +----------------------+--------------------------------------------+-------------------------------------------------------+
 | Aspect               | DevOps                                     | MLOps                                                 |