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.

Authoring for Pluralsight – Azure Machine Learning

Off to start another set of courses for Pluralsight:

  • Sourcing Data in Microsoft Azure
  • Deploying and Managing Models in Microsoft Azure
  • Cleaning and Preparing Data in Microsoft Azure

If you would like to check out any of my other courses, visit my author’s profile.

Sourcing Data in Microsoft Azure

This course is for people looking to move into the data sciences. They can have an existing background in development or IT.

This course will show how to find data in Microsoft Azure, how to move and change that data, and finally how to build workflows around that data.

This course assumes the developer has an understanding of basic computer terminology and the azure portal.

Deploying and Managing Models in Microsoft Azure

This course is for people looking to move into the data sciences. They can have an existing background in development or IT.

This course introduces the audience to the different data preparation steps involved with data projects. This course will show how to clean, transform, and wrangle the data needed for a data project.

This course assumes the developer has an understanding of basic computer terminology and the azure portal.

Cleaning and Preparing Data in Microsoft Azure

This course is for data science practitioners who need to learn more about how to utilize tools for managing the models they create.

The audience will be taken through automation and DevOps to learn more about how to manage their workflows. Everything from versioning, automated deployments, automated hyper-parameter tuning, and more will be discussed.

This course assumes the data scientist has an understanding of machine learning and common terminology and integration in machine learning projects. The course also assumes the data scientist has knowledge of Azure and the Azure portal.

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.

 

Creating .proto definitions from existing types at runtime

There was a need to create .proto definition files from the definitions of a reverse engineered database first project. The approach taken was that of using System.Emit to generate the type definitions and feed those to protobuf-net and use its ability to generate the .proto files.

There are only three classes needed:

  • ContextFinder
  • ClassGenerator
  • Program

The ContextFinder is pretty straight forward. It uses reflection to get all the generic parameters of DbSet<> properties within a DbContext. Then, ClassGenerator is used to copy the properties of the Types we harvested into a new type with the addition of adding ProtoContract and ProtoMember appropriately. Then, the Program class just loads the assembly from the file specified and runs the previously two mentioned classes.

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

Authoring for Pluralsight – Developing Microsoft Azure Intelligent Edge Solutions

Off to start another course for Pluralsight. This time its Developing Microsoft Azure Intelligent Edge Solutions. If you would like to check out any of my other courses, visit my author’s profile. The new course will cover the following topics:

  • Edge
    • IoT Architecture
    • IoT use cases and solutions
    • Edge Architecture
  • Azure IoT Hub
    • Overview of the IoT Ecosystem in Azure
    • IoT Hub message routing
    • Stream processing overview
  • Hot, Warm, and Cold paths
    • Use cases for hot, warm, and cold paths
    • Hot path with event hubs and log app
    • Warm path with Cosmos DB
    • Cold path with Azure Blob Storage
  • Real Time and Batch Processing
    • Overview and Demos of Stream Analytics Service
    • Overview and Demos of Time Series Insights

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

 

iotedge: error while loading shared libraries: libssl.so.1.0.2: cannot open shared object file: No such file or directory – Raspberry Pi

After installing Azure IoT Edge using the guide for Linux ARM32, the following error was presented: “iotedge: error while loading shared libraries: libssl.so.1.0.2: cannot open shared object file: No such file or directory“. 

The fix was simple enough, just install the building libssl1.02 using the following command:

sudo apt-get install libssl1.0.2

Test by running the iotedge command:

iotedge

azureiotedgeCapture.PNG

If that works successfully, restart the iotedge service:

service iotedge edge restart

Verify that it is running by checking the service status:

service iotedge edge status

azureiotedgeCapture

MVP Renewal

Proudly, I will be entering my third year as a Microsoft MVP. This will be under the Microsoft Azure category again. Moving forward, I look forward to doing a large amount of work and training with Azure Edge and Azure ML. Specifically, I look forward to working on the Scry Unlimited and other projects I find throughout the year. To contact me for your project, please visit the contact page.

As a start, on 7/16/2019 I will be presenting AI on the Edge at the Azure in the ATL user group. Following that up I will be speaking at events around the country and hopefully internationally again. In addition to my normal speaking on Mobile, Cloud, and Edge; I will be adding Machine Learning and Artificial Intelligence specifically focusing on the integration with Edge and Mobile computing.

Finally, I am still putting together events in Atlanta. If you would like to participate in any of the following events, just follow their links or message me on Twitter:

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:

  1. Prepare the image
  2. Classify the image

Prepare an image for prediction

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.

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