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.

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.

 

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

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.

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

Module Arch

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:

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.

Securing SSH in Azure

On a recent project I inherited an Azure IaaS setup that managed Linux VMs by connecting via SSH from public IPs. I figured while we did a vNet migration we might as well secure the SSH pipeline.

Disable SSH Arcfour and CBC Ciphers

Arcfour is compatible with RC4 encryption and has issues with weak keys, which should be avoided. See RFC 4353 for more information here.

The SSH server located on the remote host also allows cipher block chaining (CBC) ciphers to be used to establish a Secure Shell (SSH) connection, leaving encrypted content vulnerable to a plaintext recovery attack. SSH is a cryptographic network protocol that allows encrypted connections between machines to be established. These connections can be used for remote login by an end user, or to encrypt network services. SSH leverages various encryption algorithms to make these connections, including ciphers that employ cipher block chaining.

The plaintext recovery attack can return up to thirty two bits of plaintext with a probability of 2-18 or fourteen bits of plain text with a probability of 2-14. This exposure is caused by the way CBC ciphers verify the message authentication code (MAC) for a block. Each block’s MAC is created by a combination of an unencrypted sequence number and an encrypted section containing the packet length, padding length, payload, and padding. With the length of the message encrypted the receiver of the packet needs to decrypt the first block of the message in order to obtain the length of the message to know how much data to read. As the location of the message length is static among all messages, the first four bytes will always be decrypted by a recipient. An attacker can take advantage of this by submitting an encrypted block, one byte at a time, directly to a waiting recipient. The recipient will automatically decrypt the first four bytes received as it the length is required to process the message’s MAC. Bytes controlled by an attacker can then be submitted until a MAC error is encountered, which will close the connection. Note as this attack will lead to the SSH connection to be closed, iterative attacks of this nature will be difficult to carry out against a target system.

Establishing an SSH connection using CBC mode ciphers can result in the exposure of plaintext messages, which are derived from an encrypted SSH connection. Depending on the data being transmitted, an attacker may be able to recover session identifiers, passwords, and any other data passed between the client and server.

Disable Arcfour ciphers in the SSH configuration. These ciphers are now disabled by default in some OpenSSH installations. All CBC mode ciphers should also be disabled on the target SSH server. In the place of CBC, SSH connections should be created using ciphers that utilize CTR (Counter) mode or GCM (Galois/Counter Mode), which are resistant to the plaintext recovery attack.

Disable SSH Weak MAC Algorithms

The SSH server is configured to allow cipher suites that include weak message authentication code (“MAC”) algorithms. Examples of weak MAC algorithms include MD5 and other known-weak hashes, and/or the use of 96-bit or shorter keys. The SSH protocol uses a MAC to ensure message integrity by hashing the encrypted message, and then sending both the message and the output of the MAC hash function to the recipient. The recipient then generates their hash of the message and related content and compares it to the received hash value. If the values match, there is a reasonable guarantee that the message is received “as is” and has not been tampered with in transit.

If the SSH server is configured to accept weak or otherwise vulnerable MAC algorithms, an attacker may be able to crack them in a reasonable timeframe. This has two potential effects:

  • The attacker may figure out the shared secret between the client and the server thereby allowing them to read sensitive data being exchanged. 
  • The attacker may be able to tamper with the data in-transit by injecting their own packets or modifying existing packet data sent within the SSH stream.

Disable all 96-bit HMAC algorithms, MD5-based HMAC algorithms, and all CBC mode ciphers configured for SSH on the server. The sshd_config file should only contain the following options as far as supported MAC algorithms are concerned:

  • hmac-sha2-512
  • hmac-sha2-512-etm@openssh.com
  • hmac-sha2-256
  • hmac-sha2-256-etm@openssh.com
  • hmac-ripemd160-etm@openssh.com
  • umac-128-etm@openssh.com
  • umac-128-etm@openssh.com
  • hmac-ripemd160
  • umac-128@openssh.com
  • umac-128@openssh.com 

In addition, all CBC mode ciphers should be replaced with their CTR mode counterparts.

Testing

To test, run the following command:

nmap -sS -sV -p 22 –script ssh2-enum-algos [TARGET IP]