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Matter: a Unifying Standard for Home Devices or Hype?

In this short article I want to discuss the release of the recent standard, Matter, formally CHIP (Project Connected Home over IP). Will we have a consumer device communication standard that’s ideal for the smart home or are we looking at yet another standard that will further complicate IoT for the our smart appliances?

Matter was parented by the Connectivity Standards Alliance and release to the world this month. Version 1 is available now for review by manufacturers, developers, and anyone curious about this new means of device communications.

https://csa-iot.org/developer-resource/specifications-download-request/

The goal of this standard is to make interoperability among home devices easier. And because the standard has backing from Samsung, Google, Apple, and a large number of other organizations with interest in the home device market, there appears to be a fair chance the standard will stick.

The promise seems to be less hassle for the user. When you purchase a product that adheres to the Matter standard, you should be able to easily connect and integrate with other devices in your home using that standard.

But what about security? Even at this moment, I can connect to my neighbor’s TV from my iPad if I wanted to. How do we make sure that these devices are safe, even for those who aren’t the most technically skilled?

According to their security white paper, Matter follows five security tenets:

  • Comprehensive – they use a layered approach
  • Strong – they use well-tested, strong cryptography
  • Easy – security should improve ease of use, or decrease it
  • Resilient – protect, detect, recover
  • Agile – able to respond quickly to threats

How easily will this be adopted by the developers? I did take a look at the source code and libraries. It’s written in C++, so it should be fast. And there appear to be libraries for most of your common development boards, chipsets, and MCUs. I think that’s good sign. You can see the repo here:

https://github.com/project-chip/connectedhomeip

So is it a viable standard or is it hype?

Matter was a long time in the making. And it’s creation required manufacturing rivals like Google, Apple, and Samsung to sit across from each other at a table and agree on one direction to take device communication. The standard was just released this month and the official launch is on November 3rd. There are already a large group of early adopters prepared to launch products. Some of your appliances under the Christmas Tree this year might be Matter compatible.

I think it’s safe to say that Matter isn’t just hype. It looks to be a great way for devices to communicate easily on a home network using common IP methods and options like WiFi and a new protocol called Thread. Maybe this time next year we’ll see more IoT innovation in our homes because of better device interoperability.

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I Read and Summarized the 3rd Edition of IoT Signals So You Don’t Have to

A report built based on survey data taken by the company named, ‘hypothesis’. This is a combined effort between Microsoft and Hypothesis to capture the most current state of IoT from the view of business leaders in certain sectors; manufacturing, energy, mobility, and smart places.

The survey was multi-national and the report includes data captured from in-depth interviews.

Things to know about IoT in 2021

The following are high-level conclusions drawn from the interviews and survey data:

  • IoT continues to drive organizations toward a more productive future
  • COVID-19 has accelerated IoT strategies and fueled business growth
  • AI, Edge Computing, and Digital Twins are essential to advance IoT strategies
  • Although IoT projects are maturing, technological complexity persists
  • Organizations are keeping a close eye on data security

Who they talked to

  • Business decision makers, developers, and IT decision makers who work at enterprise-size companies with greater than 1k employees
    • 71% were familiar with IoT
    • 95% of those familiar, have influence and decision making power on IoT strategies
      • 10% of those familiar were not in adoption of IoT
      • 90% of those familiar were in adoption of IoT

Overall Research Learnings

Big Picture

This year, IoT continues to be widely adopted. 90% of organizations surveyed are IoT adopters. IoT projects can be categorized into four stages:

  • Learn
  • Trial / Proof of Concept
  • Purchase
  • Use

Of the 90%, at least 82% have a project that reached the “use” stage.

The state of most projects overall:

  • 29% in Learn
  • 25% in Trial / POC
  • 22% in Purchase
  • 25% in Use

IoT adoption and value globally (Australia, Italy, and the US lead as primary adopters)

  • 90% of the surveyed leaders in countries fitting criteria are adopters
  • 25% have projects in use
  • Average time to reach “use” is 12 months
  • 66% plan to use IoT more in the next 2 years

IoT Adoption and Value by Industry

  • Manufacturing
    • 91% of the surveyed leaders in countries fitting criteria are adopters
    • 26% have projects in use
    • Average time to reach “use” is 13 months
    • 68% plan to use IoT more in the next 2 years
  • Energy
    • 85% of the surveyed leaders in countries fitting criteria are adopters
    • 22% have projects in use
    • Average time to reach “use” is 15 months
    • 61% plan to use IoT more in the next 2 years
  • Mobility
    • 91% of the surveyed leaders in countries fitting criteria are adopters
    • 23% have projects in use
    • Average time to reach “use” is 14 months
    • 61% plan to use IoT more in the next 2 years
  • Smart Places
    • 94% of the surveyed leaders in countries fitting criteria are adopters
    • 24% have projects in use
    • Average time to reach “use” is 13 months
    • 69% plan to use IoT more in the next 2 years

Why Adopt IoT

Overall Top 5 reasons:

  • Quality Assurance : 43%
  • Cloud Security: 42%
  • Device and Asset Security: 40%
  • Operations Optimization: 40%
  • Employee Productivity: 35%

The report includes evidence that those companies who employ IoT to improve products and service see a higher increase in overall ROI.

Manufacturing Top 5:

  • Quality and compliance
  • Industrial automation
  • Production flow monitoring
  • Production planning and scheduling
  • Supply chain and logistics

Energy (Power and Utilities) Top 5:

  • Smart grid automation
  • Grid asset maintenance
  • Remote infrastructure maintenance
  • Smart metering
  • Workplace safety

Energy (Oil and Gas)

  • Workplace safety
  • Employee safety
  • Remote infrastructure maintenance
  • Emissions monitoring and reduction
  • Asset and predictive maintenance

Mobility

  • Inventory tracking and warehousing
  • Manufacturing operation efficiency
  • Surveillance and safety
  • Remote commands
  • Fleet management

Smart places

  • Productivity enablement and workplace analysis
  • Building safety
  • Predictive maintenance
  • Regulations and compliance management
  • Space management and optimization

Benefits of IoT

Top 5 benefits organizations are reaping from IoT

  • Increases in efficiency of operations
  • Improved safety conditions
  • Allows employees to be more productive
  • Allows for better optimization of tools and equipment
  • Reduces chance for human error

Common measures of success in IoT

  • Quality
  • Security
  • Production Efficiency
  • Reliability
  • Cost efficiency

Less common measures of success

  • More informed decision making
  • Direct impact on increased revenue
  • Sustainability
  • % of project deployed using IoT

Challenges of IoT

Top 5

  • Still implementing our current solution
  • Security risk isn’t worth it
  • Want to work out existing and future challenges before adding or using IoT more
  • Too complex to implement because of technology demands
  • Too complex to implement because of business transformation needed

Top 5 reasons POCs fail

  • High cost of scaling
  • Lack of necessary technology
  • Pilots demonstrate unclear business value or ROI
  • Too many platforms to test
  • Pilot takes too long to deploy

Top 5 security concerns

  • Ensuring data privacy
  • Ensuring network-level security
  • Security endpoints for each IoT device
  • Tracking and managing each IoT device
  • Making sure all existing software is updated

The report includes a section on best practices, and notes that despite security being a big concern, very few are implementing these best practices:

Top 5 best practices

  • Assume breaches at every level of IoT project
  • Analyze dataflows for anomalies and breaches
  • Define trust boundaries between components
  • Implement least privileged access
  • Monitoring 3rd party dependencies for common vulnerabilities

IoT Implementation Strategy

Most of the companies surveyed prefer to work with outsourced resources to implement their IoT strategy. They do prefer bespoke solutions.

Those who outsource see these positive benefits:

  • Increases efficiency of operations
  • Improves safety conditions
  • Reduces changes for human error

Those who do not outsource tend to hit these challenges:

  • Too complex to implement because of business transformation needed
  • Too long to implement
  • No buy-in from senior leadership

Sustainability

Companies with more near-term zero carbon footprint goals are more motivated to implement IoT to help than those who have a longer range target.

Impact of COVID-19

When asked if C-19 was an influence in investing:

  • 44% more investment
  • 41% stay the same
  • 7% less
  • 4% too early to tell

Emerging Technologies

Those who are adopting IoT are more likely to adopt other innovative technology associated with IoT:

  • Digital Twins
  • Edge Computing
  • AI at the Edge

This is collectively known as AI Edge

AI Implementation

84% Have a strategy:

  • 31% are implementing
  • 26% developed, but not implemented
  • 26% developing

16% do not have a strategy

  • 11% want to develop
  • 5% have no plans

79% of respondents claim that AI is a core or secondary component of their overall IoT strategy

Top 5 reasons for AI in IoT Adoption:

  • Predictive maintenance
  • Prescriptive maintenance
  • User experience
  • Visual image recognition and interpretation
  • Natural language recognition and processing

Top 5 Barriers to using AI within IoT

  • Too complex to scale
  • Lack of infrastructure
  • Lack of technical knowledge
  • Implementing AI would be too complex
  • Lack of trained personnel

AI Adoption and Value by Industry

Total:

  • 84% – have an AI strategy
  • 31% – Implementing
  • 26% – developed
  • 26% – developing
  • 79% – use AI in IoT solution

Manufacturing:

  • 84% – have an AI strategy
  • 31% – Implementing
  • 23% – developed
  • 30% – developing
  • 83% – use AI in IoT solution

Energy

  • 90% – have an AI strategy
  • 26% – Implementing
  • 28% – developed
  • 36% – developing
  • 89% – use AI in IoT solution

Mobility

  • 81% – have an AI strategy
  • 36% – Implementing
  • 25% – developed
  • 20% – developing
  • 85% – use AI in IoT solution

Smart Places

  • 88% – have an AI strategy
  • 39% – Implementing
  • 28% – developed
  • 21% – developing
  • 75% – use AI in IoT solution

Edge Computing

Edge Computing Implementation Progress

79% have a strategy:

  • 29% implementing
  • 26% developed but not implemented
  • 24% developing

21% do not have a strategy:

  • 15% want to develop
  • 6% have not plans

81% Edge Computing as a core or secondary component:

  • 42% Core
  • 39% Secondary
  • 18% Considering, not yet adopted
  • 1% not considering

Top 5 Reasons to adopt Edge Computing

  • Cloud security
  • Device and asset security
  • Quality assurance
  • Securing the physical environment
  • Operations Optimization

Top 5 barriers to adoption

  • Lack of architectural guidance
  • Lack of trained personnel
  • Lack of infrastructure
  • Difficulty managing security
  • Lack of clarity on edge hardware choices

Edge Computing Adoption and Value by Industry

Total:

  • 79% – Have Edge Computing strategy
  • 29% – implementing
  • 26% – developed
  • 24% – developing
  • 81% – Use Edge Computing in IoT Solutions

Manufacturing:

  • 83% – Have Edge Computing strategy
  • 37% – implementing
  • 28% – developed
  • 18% – developing
  • 77% – Use Edge Computing in IoT Solutions

Energy:

  • 85% – Have Edge Computing strategy
  • 38% – implementing
  • 25% – developed
  • 23% – developing
  • 85% – Use Edge Computing in IoT Solutions

Mobility:

  • 85% – Have Edge Computing strategy
  • 18% – implementing
  • 30% – developed
  • 37% – developing
  • 88% – Use Edge Computing in IoT Solutions

Smart Places:

  • 85% – Have Edge Computing strategy
  • 29% – implementing
  • 26% – developed
  • 30% – developing
  • 83% – Use Edge Computing in IoT Solutions

Digital Twins

77% have a strategy:

  • 24% implementing
  • 29% developed, but not implemented
  • 24% developing

23% do not have a strategy:

  • 14% want to develop
  • 9% have no plans

81% Use and Impact of DT on IoT Solutions

  • 41% feature as core component
  • 40% feature as secondary component
  • 18% considering, but not yet adopted
  • 1% not considering

Top 5 benefits of using DT within IoT:

  • Improve overall quality
  • Increase revenue
  • Reduce operations costs
  • Enhance warranty cost and services
  • Reduce time to market for a new product

Top 5 barriers:

  • Challenges managing the value of data collected
  • Complexity of systems needed to handle digital twins
  • Integration challenges
  • Lack of trained personnel
  • Challenges modeling the environment

Digital Twins Adoption and Value by Industry

Total:

  • 77% – have a DT strategy
  • 24% – implementing
  • 29% – developed
  • 24% – developing
  • 81% – use DT in IoT solution

Manufacturing:

  • 79% – have a DT strategy
  • 31% – implementing
  • 25% – developed
  • 23% – developing
  • 86% – use DT in IoT solution

Energy:

  • 79% – have a DT strategy
  • 26% – implementing
  • 29% – developed
  • 24% – developing
  • 82% – use DT in IoT solution

Mobility:

  • 76% – have a DT strategy
  • 15% – implementing
  • 39% – developed
  • 23% – developing
  • 77% – use DT in IoT solution

Smart Places

  • 82% – have a DT strategy
  • 27% – implementing
  • 35% – developed
  • 22% – developing
  • 85% – use DT in IoT solution

By Industry

Smart Places

94% IoT Adopters

  • 27% – learn
  • 25% – POC
  • 25% – Purchase
  • 24% – Use

Top Benefits:

  • Increase the efficiency of operations
  • Improves safety conditions
  • Allows for better optimization of tools and equipment

Top 5 reasons for adoption:

  • Productivity enablement
  • Building safety
  • Predictive maintenance
  • Space management and optimization
  • Regulation and compliance management

Top 5 challenges

  • Still implementing current solution
  • Security risk isn’t worth it
  • Too complex to implement because of the need for business transformation
  • Want to work out exiting and future challenges before adding or using
  • Too complex to implement because of technology demands

Manufacturing

91% IoT Adopters

  • 27% – Learn
  • 26% – POC
  • 21% – Purchase
  • 26% – Use

Top Benefits

  • Increases the efficiency of operations
  • Increases production capacity
  • Reduces chance for human error

Top 5 Reasons for Adoption:

  • Quality and compliance
  • Industrial automation
  • Production flow monitoring
  • Production planning and scheduling
  • Supply chain and logistics

Top 5 Challenges to using IoT more

  • Still implementing current solution
  • Too complex to implement because of technology demands
  • Security risk isn’t worth it
  • Want to work out challenges before adding or using IoT more
  • Don’t have human resources to implement or manage

Mobility

91% IoT Adopters:

  • 30% – Learn
  • 26% – POC
  • 21% – Purchase
  • 23% – Use

Top benefits of IoT:

  • Increase efficiency of operations
  • Allows employees to be more productive
  • Improves safety conditions and increases production capacity

Top 5 Reasons for Adoption

  • Inventory tracking and warehousing
  • Manufacturing operations efficiency
  • Surveillance and safety
  • Remote commands
  • Fleet management

Top 5 challenges to using IoT More:

  • Want to work out challenges before adding or using IoT more
  • Too complex to implement because of technology demands
  • Still implementing our current solutions
  • Security risk isn’t worth it
  • Too complex to implement because of business transformation needed

Energy

80% IoT Adopters (Power and Utilities)

  • 28% – Learn
  • 26% – POC
  • 23% – Purchase
  • 23% – Use

Top Benefits

  • Increase the efficiency of operations
  • Increases production capacity
  • Allows employees to be more productive

Top 5 reasons for adoption:

  • Smart grid automation
  • Grid asset maintenance
  • Remote infrastructure maintenance
  • Smart metering
  • Workplace safety

Top Challenges:

  • Too complex because of technology demands
  • Security risk isn’t worth it
  • don’t have human resources to implement and manage

94% IoT Adopters (Oil & Gas)

  • 28% – Learn
  • 27% – POC
  • 24% – Purchase
  • 20% – Use

Top Benefits

  • Increase customer satisfaction
  • Improves business decision-making
  • Increases production capacity and the efficiency of operations

Top 5 reasons for adoption:

  • Workplace safety
  • Employee satisfaction
  • Remote infrastructure maintenance
  • Emissions monitoring and reduciton
  • Asset and predictive maintenance

Top challenges:

  • Lack of technical knowledge
  • Don’t know enough
  • Too complex to implement because of business transformation needed

Final Thoughts

Things worth noting:

  • IoT is not going away, in fact more money, time, and investment goes into it each year
  • Most organizations are looking to add AI, Edge Computing, and Digital Twins to their solutions
  • May organizations are outsourcing their IoT work and seeing more benefits because of this
  • Top challenges are around knowledge, skill, security, and implementation at scale

The original report

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Operationalize Machine Learning for Manufacturing

The “Smart” manufacturing revolution is here. It’s proved its effectiveness in Predictive Maintenance, Quality Control, Logistics and Inventory, Product Development, Cybersecurity, and paired with IoT it expands into Robotics, Control Systems, and Digital Twins.

Manufacturers who want to build a data science team and operationalize machine learning models for must build an MLOPs practice. And by must, I mean this is essential, not optional. I’ve seen what happens when a team is built of unskilled engineers and attempts to take shortcuts on the journey to put machine learning into production without MLOPs and Responsible AI.

Why do you need MLOPs?

First, I think it’s important to understand the benefits of machine learning to manufacturers. I’ve already touched on some of the areas where Machine Learning is helpful. But let’s talk about the value that MLOPs brings to your organization.

  1. It helps to reduce the risk of data science by making the work of scientists more transparent, trackable, and understandable. When coupled with a Responsible AI governance policy, MLOPs can help manufacturing leaders understand the risks of the models running in their environments.
  2. Generate greater, long-term value. Modern software development has taught us that development doesn’t end with production. Systems must be monitored to ensure they are meeting the needs of the business, even as that business shifts and changes under market pressures. Machine Learning is no different. In fact, models must be reviewed and maintained in some type of regular cadence. Unlike software, that is relatively static in nature, an ML model is code with data. As data changes, so must the model. It is an evolving system and must be observed and maintained even after production deployment.
  3. Scaling is one of the big problems of ML. It’s fairly easy to monitor one or two models in production manually, but once you scale to hundreds, even thousands of models, you will need systems in place to support the required maintenance of models.
  4. Maintenance is another thing to consider. Models must be updated. Sometimes many times a day. This means a delivery pipeline from initial hypothesis to production that is fast, scalable, and includes all the required tests and metrics gathering systems needed to monitor and control the lifecycle of the model.

The Machine Learning Lifecycle and Why It’s Challenging

I’m not going to dive into the Machine Learning Lifecycle in this article, but I will point out a few areas where that make it challenging to a manufacturer’s traditional IT department.

  • Data is a dependency. Very few ML models are of any value without a powerful data system to provide data for the learning portion of machine learning. Manufacturers often have data. Lots of data, in fact. Many manufacturers have been collecting telemetry data from operational technology for many years. But often that data is locked away in proprietary systems or behind multiple firewalls. There are tools to work with systems on-premises. Many data scientists are happy building experiments on their laptop. But this doesn’t scale to larger datasets or working with streaming data. Ideally, some type of lambda architecture will be needed to gather, prepare, and process the data for the machine learning process. Often, it’s preferred that this system be in the cloud where it’s accessible to entire teams and data engineers and leverage the compute power of the cloud to build and operationalize ML models.
  • Domain language issues. Data science brings a whole new set of tools and ideas to the traditional IT department. Data scientists are rarely software developers. They work in Notebooks and use programming languages like R, Python, and Julia. If you’re IT department isn’t used to the tooling and the processes of working with these tools, transferring from a Jupyter Notebook to an inferencing endpoint hosted on Kubernetes might be a challenge for your IT department.
  • Again, data scientists are not programmers. There are some who can program, but the code they tend to write is in support of training and testing ML models. It’s not generally written to be robust enough to standup to the type of software requirements demanded from a microservice. Your data scientists will likely need support from engineers who understand the deployment side of data science. You will need a team of people to help support your ML journey.

MLOPs and DevOps

MLOPs and DevOps do have a lot in common. They both focus on some of the same things, so if you already have a robust DevOps process in place in your organization, taking on MLOPs will be less of a challenge. However, if your IT department has never heard of DevOps, you might be in for a harder journey than expected.

  • DevOps is a proponent of automation. The idea is to reduce the friction from software development to production deployment by adding systems that automate the manual steps of the software deployment process. MLOPs has these same goals.
  • DevOps is a teams practice built on trust within the teams. This includes increased collaboration, communication, and an overall understanding from all the teams of the service life-cycle. Developers understand the requirements and rigors of operations and do their best to bake in what’s needed to support operations members of their team. Operations understand the business value of new features and systems and know how to monitor telemetry and systems to ensure risks are minimized. MLOPs has some of these same ideas, with an even greater emphasis on monitoring the model in production.
  • Both MLOPs and DevOps prioritize the concept of continuous deployment, experimentation, observability, and resilient systems.

The big difference between these two systems:

  • DevOps tends to deliver code
  • MLOPs delivers code and data

Use MLOPs to Reduce Risk

MLOPs can help protect you from risks associated with Machine Learning. As a practice, MLOPs encourages many of the same things that DevOps does, like teamwork, open and honest visibility into work, standard operating procedures, and a break in dependencies from traditional siloed IT operations.

  • Personnel dependency risks. What if your data scientist leaves the organization? Can you hire someone to take over that role?
  • Model accuracy can reduce over time. Without the systems in place to monitor models and the system in place to quickly deploy new models, you might find that you’re systems are compromised because they are delivering bad predictions or they are responding incorrectly to certain events.
  • What if there is a high dependency on the model and it’s not available? When systems are dependent on the accuracy of a model and that model isn’t available, could it bring production to halt? Or worse, put people’s lives at risk? These are all considerations that must be made when evaluating putting an ML Model into production.

Taking on MLOPs

MLOPS is not free. There are operational costs associated with the practice. This is not a cost you can avoid. Too much depends on the accuracy of your models, especially in a production environment to take on the risks without the assurances that a good MLOPs practice brings to manufacturers.

Here are the important things to remember:

  • Pushing a model to production is not a final step. It’s one step in the life-cycle of a model.
  • Monitor the model performance and make sure it’s meeting the accuracy requirements expected
  • Use Responsible AI practices
    • Intentionality
    • Accountability
    • Fairness
    • Privacy
    • Security
  • Again, MLOPs is not optional, nice to have, or an afterthought. If you are releasing models to production, you must start an MLOPs practice.