New Pluralsight Courses Released!

My new Pluralsight courses Cleaning and Preparing Data in Microsoft Azure and Architecting Xamarin.Forms Applications for Code Reuse were just released! Here are the synopsis:

Cleaning and Preparing Data in Microsoft Azure

Abstract

This course targets software developers and data scientists looking to understand the initial steps in a machine learning solution. The content will showcase methods and tools available using Microsoft Azure.

Description

No data science project of merit has ever started with great data ready to plug into an algorithm. In this course, Cleaning and Preparing Data in Microsoft Azure, you’ll learn foundational knowledge of the steps required to utilize data in a machine learning project. First, you’ll discover different types of data and languages. Next, you’ll learn about managing large data sets and handling bad data. Finally, you’ll explore how to utilize Azure Notebooks. When you’re finished with this course, you’ll have the skills and knowledge of preparing data needed for use in Microsoft Azure. Software required: Microsoft Azure.

Architecting Xamarin.Forms Applications for Code Reuse

Abstract

A well-architected application is flexible to changing business requirements. This course will teach you how to architect Xamarin.Forms applications in a way that promotes reusable patterns.

Description

As business requirements change, so do solution assumptions. In this course, Architecting Xamarin.Forms Applications for Code Reuse, you’ll learn different architectural patterns in Xamarin.Forms. First, you’ll explore project structure and organization. Next, you’ll discover patterns and standards to promote code sharing. Finally, you’ll learn how to utilize dependency injection in Xamarin.Forms. When you’re finished with this course, you’ll have the skills and knowledge of architecting Xamarin.Forms projects needed to optimally promote code reuse.

New Pluralsight Course Released!

My new Pluralsight course Sourcing Data in Microsoft Azure was just released! Here is the synopsis:

Abstract

This course targets software developers looking to source data from inside and outside of the cloud. The content will also showcase methods and tools available using Microsoft Azure.

Description

The cloud has nearly infinite compute power for processing. In this course, Sourcing Data in Microsoft Azure, you’ll learn foundational knowledge of data types, data policy, and finding data. First, you’ll learn how to register data sources with Azure Data Catalog. Next, you’ll discover how to extract, transform, and load data with Azure Data Factory. Finally, you’ll explore how to set up data processing with Azure HD Insight. When you’re finished with this course, you’ll have the skills and knowledge of the tools and processes needed to source data in Microsoft Azure. Software required: Microsoft Azure portal.

New Pluralsight Course Released!

My new Pluralsight course Deploying and Managing Models in Microsoft Azure was just released! Here is the synopsis:

Abstract

In this course, you’ll learn about how data science practitioners can utilize tools for managing the models they create. You’ll also see those tools showcased in Microsoft Azure.

Description

One of the most overlooked processes in data science is managing the life cycle of models. In this course, Deploying and Managing Models in Microsoft Azure, you’ll gain foundational knowledge of Azure Machine Learning. First, you’ll discover how to create and utilize Azure Machine Learning. Next, you’ll find out how to integrate with Azure DevOps. Finally, you’ll explore how to utilize them together to automate the deployment and management of models. When you’re finished with this course, you’ll have the skills and knowledge of model life cycle management needed to manage a machine learning project. Software required: Microsoft Azure.

New Pluralsight Courses

I’ve been busy and not able to update that I have new courses available on Pluralsight:

Developing Microsoft Azure Intelligent Edge Solutions

This course targets software developers that are looking to build edge solutions that can process data and make intelligent decisions. This course will showcase how to develop those solutions using Microsoft Azure.

Over time, what was once simply Internet of Things solutions has evolved into Edge solutions. In this course, Developing an Intelligent Edge in Microsoft Azure, you will learn foundational knowledge of edge computing, how it interacts with data and messaging systems, and how to utilize both with Microsoft Azure. First, you will learn the concepts of edge and internet of things computing. Next, you will discover how to process streaming data on hot, warm, and cold paths. Finally, you will explore how real-time and batch processing can be utilized in an edge solution. When you are finished with this course, you will have the skills and knowledge of edge and internet of things in Azure needed to architect your next edge solution. Software required: Microsoft Azure, .NET.

Building Your First Data Science Project in Microsoft Azure

This course targets software developers looking to build data science solutions that can utilize the power of the cloud. The content will also showcase how to create those solutions using Microsoft Azure.

The past five years have shown a boom in the data science field with advancements in hardware and cloud computing. In this course, Building Your First Data Science Project in Microsoft Azure, you will learn about data science and how to get started utilizing it in Microsoft Azure. First, you will learn the data science and the tools surrounding it. Next, you will discover how to create a development environment in Microsoft Azure. Finally, you will explore how to maintain and utilize that development environment. When you are finished with this course, you will have the skills and knowledge of data science to build your first data science project in Microsoft Azure. Software required: Microsoft Azure.

numpy/core/_multiarray_umath.cpython-35m-arm-linux-gnueabihf.so: undefined symbol: cblas_sgemm – Raspberry Pi

While working on a Raspberry Pi image that had been used prior by an electrical engineer to setup all of the dependencies for the hardware, there was an error when trying to upgrade to use Tensorflow. Tensorflow was needed to run a model trained with Cognitive Services: Custom Vision Service. The error was when the script imported Numpy. That caused the following error:

numpy/core/_multiarray_umath.cpython-35m-arm-linux-gnueabihf.so: undefined symbol: cblas_sgemm

To remedy this, all of the installations of Numpy had to be uninstalled. The following commands were run:

apt-get remove python-numpy
apt-get remove python3-numpy
pip3 uninstall numpy

After all three of those commands complete, Numpy was reinstalled using the package provided for raspian:

apt-get install python3-numpy

Pluralsight Course Published – Designing an Intelligent Edge in Microsoft Azure

Designing an Intelligent Edge in Microsoft Azure was just published on Pluralsight! Check it out. Here is a synopsis of what’s in it:

This course targets software developers that are looking to integrate AI solutions in edge scenarios ranging from an edge data center down to secure microcontrollers. This course will showcase how to design solutions using Microsoft Azure.

Cloud computing has moved more and more out of the cloud and onto the edge. In this course, Designing an Intelligent Edge in Microsoft Azure, you will learn foundational knowledge of edge computing, its intersection with AI, and how to utilize both with Microsoft Azure. First, you will learn the concepts of edge computing. Next, you will discover how to create an edge solution utilizing Azure Stack, Azure Data Box Edge, and Azure IoT Edge. Finally, you will explore how to utilize off the shelf AI and build your own for Azure IoT Edge. When you are finished with this course, you will have the skills and knowledge of AI on the edge needed to architect your next edge solution. Software required: Microsoft Azure, .NET

Multiple TensorFlow Graphs from Cognitive Services – Custom Vision Service

For one project, there was a need for multiple models within the same Python application. These models were trained using the Cognitive Services: Custom Vision Service. There are two steps to using an exported model:

Prepare the image
Classify the image

Prepare an image for prediction

	from PIL import Image
	import numpy as np
	import cv2

	def convert_to_opencv(image):
	# RGB -> BGR conversion is performed as well.
	image = image.convert('RGB')
	r,g,b = np.array(image).T
	opencv_image = np.array([b,g,r]).transpose()
	return opencv_image

	def crop_center(img,cropx,cropy):
	h, w = img.shape[:2]
	startx = w//2–(cropx//2)
	starty = h//2–(cropy//2)
	return img[starty:starty+cropy, startx:startx+cropx]

	def resize_down_to_1600_max_dim(image):
	h, w = image.shape[:2]
	if (h < 1600 and w < 1600):
	return image

	new_size = (1600 * w // h, 1600) if (h > w) else (1600, 1600 * h // w)
	return cv2.resize(image, new_size, interpolation = cv2.INTER_LINEAR)

	def resize_to_256_square(image):
	h, w = image.shape[:2]
	return cv2.resize(image, (256, 256), interpolation = cv2.INTER_LINEAR)

	def update_orientation(image):
	exif_orientation_tag = 0x0112
	if hasattr(image, '_getexif'):
	exif = image._getexif()
	if (exif != None and exif_orientation_tag in exif):
	orientation = exif.get(exif_orientation_tag, 1)
	# orientation is 1 based, shift to zero based and flip/transpose based on 0-based values
	orientation -= 1
	if orientation >= 4:
	image = image.transpose(Image.TRANSPOSE)
	if orientation == 2 or orientation == 3 or orientation == 6 or orientation == 7:
	image = image.transpose(Image.FLIP_TOP_BOTTOM)
	if orientation == 1 or orientation == 2 or orientation == 5 or orientation == 6:
	image = image.transpose(Image.FLIP_LEFT_RIGHT)
	return image

	def prepare_image(image):
	# Update orientation based on EXIF tags, if the file has orientation info.
	image = update_orientation(image)

	# Convert to OpenCV format
	image = convert_to_opencv(image)

	# If the image has either w or h greater than 1600 we resize it down respecting
	# aspect ratio such that the largest dimension is 1600
	image = resize_down_to_1600_max_dim(image)

	# We next get the largest center square
	h, w = image.shape[:2]
	min_dim = min(w,h)
	max_square_image = crop_center(image, min_dim, min_dim)

	# Resize that square down to 256×256
	augmented_image = resize_to_256_square(max_square_image)

	augmented_image = crop_center(augmented_image, 244, network_input_size)

	return augmented_image

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CustomVisionImageProcessor.py

hosted with ❤ by GitHub

Classify the image

To run multiple models in Python was fairly simple. Simply call tf.reset_default_graph() after saving the loaded session into memory.

	import tensorflow as tf
	import numpy as np


	# The category name and probability percentage
	class CategoryScore:
	def __init__(self, category, probability: float):
	self.category = category
	self.probability = probability


	# The categorizer handles running tensorflow models
	class Categorizer:
	def __init__(self, model_file_path: str, map: []):
	self.map = map

	self.graph = tf.Graph()
	self.graph.as_default()
	self.graph_def = self.graph.as_graph_def()

	with tf.gfile.GFile(model_file_path, 'rb') as f:
	self.graph_def.ParseFromString(f.read())
	tf.import_graph_def(self.graph_def, name='')

	output_layer = 'loss:0'
	self.input_node = 'Placeholder:0'

	self.sess = tf.Session()
	self.prob_tensor = self.sess.graph.get_tensor_by_name(output_layer)

	tf.reset_default_graph()

	def score(self, image):
	predictions, = self.sess.run(self.prob_tensor, {self.input_node: [image]})

	label_index = 0
	scores = []

	for p in predictions:
	category_score = CategoryScore(self.map[label_index],np.float64(np.round(p, 8)))
	scores.append(category_score)
	label_index += 1

	return scores

view raw

CustomVisionCategorizer.py

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After the CustomVisionCategorizer is create, just call score and it will score with the labels in the map.

Azure IoT Edge – YOLO, Stream Analytics Service, and Blob Storage

As a continuation of the Izon camera hack //TODO: link to previous article, I wanted to detect if my dog was using the doggy door in the main room. The approach was going to be simple at first, detect the dog in the room and not in the room. When in the room changes (or not in the room), upload 10 seconds worth of images to Azure to see if the dog used the door.

Image Classification

To detect the dog, the first and largest challenge to these types of tasks is getting enough images to train the model. For me, this meant saving images of the dog in a pre-aligned shot. This is easy enough to accomplish; the room the images will be processed in should only have the dog moving in it. Since he is the only moving object, YOLO can be used to detect the position of objects in the room and then the position of these objects can be checked to see if there is any movement. If there is any movement, the images can be saved for later categorization. To accomplish this, there will be four modules:

Camera Module – Accesses the camera feeds to save the images
Object Detection Module – Uses YOLO to detect object and object positions
Motion Detection Module – Uses Stream Analytics Service to detect if object positions are moving.
Image Storage Module – Uses Blob Storage so save and delete the images

The Camera module will send the timestamped images to the Object Detection Module and the Image Storage Module. The Object Detection Module will then use YOLO to detect the objects and their positions in the image. Those detection results will be sent to the Motion Detection Module, which will use Streaming Analytics Service to see if there was motion detected over the last ten seconds. If there is no motion detected over the last ten seconds, then the Motion Detection Module will send a delete command to the Image Storage Module to remove the image without motion from the store. The routing Table will look as so:

	"routes": {
	"cameraToObjectDetection": "FROM /messages/modules/camera/outputs/imageOutput INTO BrokeredEndpoint(\"/modules/objectDetection/inputs/incomingImages\")",
	"cameraToImageStorage": "FROM /messages/modules/camera/outputs/imageOutput INTO BrokeredEndpoint(\"/modules/imageStorage/inputs/incomingImages\")",
	"objectDetectionToMotionDetection": "FROM /messages/modules/objectDetection/outputs/objectDetectionOutput INTO BrokeredEndpoint(\"/modules/motionDetection/inputs/incomingObjectDetection\")",
	"motionDetectionToDeleteImage": "FROM /messages/modules/motionDetection/outputs/motionDetectionOutput INTO BrokeredEndpoint(\"/modules/imageStorage/inputs/deleteImages\")"
	}

view raw

routes.json

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These modules will be broken up into their own articles for readability and searchability. If there is no link to a module article it is because that article is not completed or is not published yet.

Microsoft Azure Cognitive Services: Text to Speech API – Published!

My new Pluralsight course, Microsoft Azure Cognitive Services: Text to Speech API, has just been published. You can find it here. If you would like to check out my other courses, you can find them on my author’s profile. Here is the course synopsis:

Short description:

In this course, you will gain a foundational knowledge of the Text to Speech API that will help you move forward with your overall understanding of the Microsoft Cognitive Services Suite.

Long description:

With AI becoming more and more ubiquitous in application development, it is important to quickly and easily integrate intelligence into your application. In this course, Microsoft Azure Cognitive Services: Text to Speech API, you will learn how to understand, configure, and utilize the Text to Speech API. First, you will discover how to use out of the box voices. Next, you will explore how to use machine learning-based voices in your app. Finally, you will learn how to create and use custom voices for your application and brand. When you are finished with this course, you will have a foundational knowledge of the Text to Speech API that will help you move forward with your overall understanding of the Microsoft Cognitive Services Suite.

Tags for this course:

Audience/Roles: software-development

Topics/Subjects: cloud-platforms

Tools: azure-cognitive-services

Authoring for Pluralsight – Microsoft Azure Cognitive Services: Text to Speech API

I’m excited to announce that I am authoring another course for Pluralsight. This course targets software developers who are looking to get started with Microsoft Azure Cognitive Services: Text to Speech API to build modern AI solutions and want to get started building an AI solution with a simple REST interface. This course continues from the other Cognitive Services courses created and being created for the Cognitive Services track.

Abstract

With AI becoming more and more ubiquitous, it is important to quickly and easily integrate with AI services. This course will show how to create modern applications using Microsoft Azure Cognitive Services: Text to Speech API with JavaScript, C#, Java, C++, and Python.

Prerequisites

This course assumes viewers are familiar with C# or Java or JavaScript or Python or C++ and understands REST APIs and JSON.

Description

Contoso is an insurance company that has decided to integrate text to speech for multiple consumer facing applications. This course will take a look at utilizing the following features of Cognitive Services – Text to Speech API:

Default API interface through multiple SDKs: JavaScript, C#, Java, C++, and Python
Creating custom voice fonts
Popular scenarios and use case for Text to Speech