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9 Essential Azure IoT Tools: The Ultimate Suite [2018 Case Studies]

Today, more than 4.4 Zettabytes of accumulated digital data exists worldwide. With such a large increase in the data we produce, more and more organisations are discovering new innovative ways to collect, use and analyse it, thus enabling them to make incredible breakthroughs in their industries.

From advances in robotics, to assisting in protection of endangered animals (see case studies below) the power of the IoT has huge real world applications.

Microsoft Azure offers users a host of varied tools to help them integrate IoT, manage Big Data and explore machine learning capabilities. The list of Azure IoT tools is expansive, each offering unique solutions that are available across both cloud and on-premises platforms.

To help you out, we’ve listed 9 essential Azure IoT tools, all of which will enable you to make your data science projects more efficient and highly scalable.


9 Azure IoT Tools to Supersize your Data’s Potential

1. Azure IoT Hub

With the ability to connect, monitor and manage billions of IoT devices, Azure IoT Hub is a tool that gives you control over all your IoT assets.

  • Establish bidirectional communication with billions of IoT device.
  • Enhance IoT security.
  • Maintain and manage devices remotely using the cloud.

Notable Case Studies:

  • Honeywell: The home and building technology allows users to track their thermostats, lock doors and turn off lights all from their phones.
  • Avatorion: The technology developers use IoT to deploy robots to entertain lonely children in hospitals.

Azure-tools-iot(Source Microsoft Docs)

2. AI Toolkit for Azure IoT Edge

The AI Toolkit for Azure IoT Edge is a collection of scripts, code, and deployable containers

that will help you to get started with AI and IoT. The kit shows you how to package deep learning models in Azure IoT Edge-compatible Docker containers.

  • Package pre-built models such as image classification and predictive maintenance.
  • Bring the power of the cloud to places with limited or no connectivity.
  • Customise data and source codes.

3. Azure IoT Suite

Azure IoT Hub is a core platform-as-a-service (PaaS) feature in the Azure IoT Suite. This enterprise-grade collection of preconfigured solutions speeds up your ability to create custom IoT solutions.

Azure IoT Suite integrates with your existing systems and devices, allowing you to get the most out of your data sources. The suite captures and analyses untapped device data, improving results across your business quickly, with the potential for scale.

  • Fast and easy to set up; deploy in minutes thanks to preconfigured solutions that implement common IoT scenarios.
  • Easy to modify, the microservice code is open source so you can tailor it to your business needs.
  • Supports a broad set of operating systems and protocols.

Notable Case Studies

  • Keith & Koep; The computer systems manufacturers have used the IoT suite to automate cocktail mixing, producing flawless drinks in seconds.
  • Transport for London; London’s main transport provider uses the IoT suite to provide free Wi-Fi to their customers onboard trains.

contoso azure iot tools.png(Source: Microsoft Azure IoT Suite)

4. Data Lake Analytics

Process Big Data jobs quickly with the Data Lake Analytics tool. The tool simplifies analysis, handling diverse workload categories such as machine learning and image processing.

  • Process data on demand, thanks to no infrastructure to manage.
  • Scale on-demand.
  • Easily debug and optimise your Big Data problems.

Notable Case Studies

  • Blue Frog; Using Data Lake Analytics, the robotics company were able to analyse and report their capabilities easily, eventually creating an at home robot soon to be on the market.
  • Evoqua; The water technology company used advanced analytics to transform field-equipment data into valuable insights.

5. Time Series Insights

A platform for storing and managing time-series data, Time Series Insights is a fully managed analytics, visualisation and storage service for Azure.

  • Able to analyse billions of events in seconds, conduct root-cause analysis and compare multiple assets.
  • Allows you to gain deeper insight into your sensor data by spotting key trends.
  • Works quickly, with no upfront data preparation.

6. Logic Apps

The logic tool allows you to build powerful integration solutions efficiently. This Azure tool also creates business processes and workflows visually without the need to write a single line of code.

  • Reduces integration challenges with out-of-the-box connectors.
  • Able to connect and integrate data from the cloud to on-premises.
  • Features a large ecosystem of software and cloud-based connectors, including Office 365 and Dropbox.

Notable Case Studies

  • Marc Jacobs; The fashion house was able to manage their complex supply chain using the Azure platform and integrated Logic Apps.
  • Targetbase; Helps retail companies better engage with their customers using Logic Apps.


(Source Microsoft Docs)

Machine learning Tools for IoT

Machine learning algorithms propel the possibilities of IoT even further, by automating data analysis and delivering powerful insights at greater speeds. These algorithms can even predict future market trends and target potential customers.

7. Machine Learning Workbench

The Machine Learning Workbench is a desktop client for Windows and Mac, the Microsoft Workbench acts as the control panel for your development lifecycle and is the ideal place to start experimenting with machine learning.


  • Features built-in data preparation; learns your steps as you perform them.
  • Integrated with Jupyter Notebooks, PyCharm and Visual Studio Code.
  • Able to automatically transform your data, fully optimising it for machine learning algorithms.

Notable Case Studies

  • Fundworks; The finance provider used the Machine Learning Workbench to track, analyse and manage thousands of daily transactions.
  • Snow Leopard Trust; The charity used the Machine Learning Workbench to automate the classification of  millions of snow leopards images . The process now takes minutes rather than hours.

8. Microsoft Machine Learning Library Spark

MML Spark provides Azure tools that let you create highly scalable models for large image and text datasets. It uses deep learning and data science tools for Apache Spark. All models created using Azure Machine Learning can now be deployed to any IoT gateways and devices with the Azure IoT Edge runtime.

  • Scalable analytics.
  • Easily integrates with Spark ML pipelines Microsoft Cognitive Toolkit.
  • Simplifies common tasks for model building.


(Source Microsoft blogs)

9. Machine Learning Model Management

Azure’s model management tool allows you to manage, deploy and unlock insights from your machine learning models.  Using this tool users can build Docker-based container images that can be run on IoT devices.

  • Features model data collection; tracks inputs and predictions and monitors data drifts in production.
  • Automatically retrains models when their performance degrades.
  • Provides automated workflows for packaging and deploying Machine Learning containers as REST APIs.

Smart tools need Smarter Strategies

As the demand for Big Data analysis grows and IoT networks get smarter, organisations in every industry will need to prepare for its integration in their business. For example, in the retail banking sector alone, Big Data will be responsible for more than £4 billion in cumulative risk management efficiency benefits.

Screen Shot 2018-02-14 at 12.20.06.png

Using the nine essential Azure IoT tools above offers you the opportunity to create innovative solutions and revolutionise the way you handle key processes.

Here they are again for you one more time:

9 Azure IoT Tools

  1. Azure IoT hub
  2. AI toolkit for Azure IoT edge
  3. Azure IoT suite
  4. Data Lake Analytics
  5. Time Series Insights
  6. Logic Apps
  7. Machine Learning Workbench
  8. MML Spark
  9. Machine learning model management


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