Add figures for chapter 9

Author: meherkasam

README.md

@@ -64,7 +64,7 @@ We diversify our practical skills in a variety of topics and tools, ranging from
 
 Work smart, not hard. We utilize the power of cloud AI platforms from Google, Microsoft, Amazon, IBM and Clarifai in under 15 minutes. For tasks not solved with existing APIs, we then use custom classification services to train classifiers without coding. And then we pit them against each other in an open benchmark, you might be surprised who won.
 
-[**Chapter 9 - Scalable Inference Serving on Cloud with TensorFlow Serving and KubeFlow**](https://github.com/practicaldl/Practical-Deep-Learning-Book/tree/master/code/chapter-9) | [Read online](https://learning.oreilly.com/library/view/practical-deep-learning/9781492034858/ch09.html)
+[**Chapter 9 - Scalable Inference Serving on Cloud with TensorFlow Serving and KubeFlow**](https://github.com/practicaldl/Practical-Deep-Learning-Book/tree/master/code/chapter-9) | [Read online](https://learning.oreilly.com/library/view/practical-deep-learning/9781492034858/ch09.html) | [Figures](figures/chapter-9)
 
 We take our custom trained model to the cloud/on-premises to scalably serve from tens to millions of requests. We explore Flask, Google Cloud ML Engine, TensorFlow Serving, and KubeFlow, showcasing the effort, scenario, and cost-benefit analysis.
 

figures/README.md

@@ -16,7 +16,7 @@ This directory contains references to the figures used within the book. All imag
 | [Chapter 6 - Maximizing Speed and Performance of TensorFlow: A Handy Checklist](chapter-6/) |
 | Chapter 7 - Practical Tools, Tips, and Tricks |
 | [Chapter 8 - Cloud APIs for Computer Vision: Up and Running in 15 Minutes](chapter-8/) |
-| Chapter 9 - Scalable Inference Serving on Cloud with TensorFlow Serving and KubeFlow |
+| [Chapter 9 - Scalable Inference Serving on Cloud with TensorFlow Serving and KubeFlow](chapter-9/) |
 | Chapter 10 - AI in the Browser with TensorFlow.js and ml5.js |
 | Chapter 11 - Real-Time Object Classification on iOS with Core ML |
 | Chapter 12 - Not Hotdog on iOS with Core ML and Create ML |

figures/chapter-8/README.md

@@ -1,11 +1,11 @@
-# Chapter 6 - Maximizing Speed and Performance of TensorFlow: A Handy Checklist
+# Chapter 8 - Cloud APIs for Computer Vision: Up and Running in 15 Minutes
 
 Note: All images in this directory, unless specified otherwise, are licensed under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/legalcode).
 
 ## Figure List
 
-| Figure number | Description | Notes |
-|:---|:---|:---|
+| Figure number | Description |
+|:---|:---|
 | [8-1](1-clarifai-results.png) | Sample of Clarifai’s results |
 | [8-2](2-cognitive-results.png) | Sample of Microsoft Cognitive Services results |
 | [8-3](3-google-cloud-results.png) | Sample of Google Cloud Vision’s results |

figures/chapter-9/1-stacks-chart.png

@@ -0,0 +1,23 @@
+# Chapter 9 - Scalable Inference Serving on Cloud with TensorFlow Serving and KubeFlow
+
+Note: All images in this directory, unless specified otherwise, are licensed under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/legalcode).
+
+## Figure List
+
+| Figure number | Description | Notes |
+|:---|:---|:---|
+| [9-1](1-stacks-chart.png) | A high-level overview and comparison of different inference serving options | |
+| [9-2](2-flask-hello-world.png) | Navigate to http://localhost:5000/hello within a web browser to view the “Hello World!” web page | |
+| [9-3](3-mlmodels-listing.png) | Listing page for machine learning models on the Google Cloud ML Engine dashboard | |
+| [9-4](4-model-creation-page.png) | Model creation page on Google Cloud ML Engine | |
+| [9-5](5-models-listing-page.png) | Model listings page on Google Cloud ML Engine | |
+| [9-6](6-model-detail-page.png) | Details page of the just-created Dog/Cat classifier | |
+| [9-7](7-cloudml-model-create-version.png) | Creating a new version for a machine learning model | |
+| [9-8](8-creating-storage-bucket.png) | Creating a new Google Cloud Storage bucket within the ML model version creation page | |
+| [9-9](9-bucket-details.png) | Google Cloud Storage Browser page showing the uploaded Dog/Cat classifier model in TensorFlow format | |
+| [9-10](10-uri-gcs.png) | Add the URI for the model you uploaded to Google Cloud Storage | |
+| [9-11](11-kubeflow-pipeline.png) | An end-to-end pipeline illustrated in KubeFlow | |
+| [9-12](12-kubeflow-notebook-server.png) | Creating a new Jupyter Notebook server on KubeFlow | |
+| [9-13](13-kubeflow-creation-obfuscated.png) | Creating a KubeFlow deployment on GCP using the browser | |
+| [9-14](14-model-monitoring.png) | Google Cloud ML Engine showing incoming queries and latency of serving the calls, with end-to-end latency at user’s end of about 3.5 seconds | |
+| [9-15](15-cost-vs-qps-gcml-byos.png) | Cost comparison of infrastructure as a service (Google Cloud ML Engine) versus building your own stack over virtual machines (Azure VM) (costs as of August 2019) | |