Microsoft Inspire 2021

This year, Microsoft Inspire for Microsoft Partners was packed with tons of new information in 3 days!  Mariner is the 2020 Microsoft Partner of the Year for IoT and a member of Intel's Partner Alliance -- all of which enables us to have access to the best resources, program and tools to help our customers succeed.

Mariner's CEO, Phil Morris was featured in a Breakout Session "Engaging the IoT Metaverse for Business Growth" with Lakecia Gunter from Microsoft and Brad Haczynski from Intel.  Click here to view the script and find out more about our new product, Spyglass Assembly Verification.

"Before Spyglass Assembly Verification, workers assembled truck bodies by following traditional ways, the way which it's been done for years, manually. With Spyglass Assembly Verification, we help them blend their digital engineering drawings with their bill of materials from ERP and brought that to the assembly line. Powered by deep learning, computer vision is now able to verify that the features of a given truck match the customized design." - Phil Morris

We're also proud to have earned a "Hero" video spot at Inspire. Check out the video by clicking on the image below:

https://youtu.be/YtXQOkeSTvI

Our Spyglass solutions are full-powered Deep Learning solutions that help you reduce your manufacturing cost of quality as you proceed on your Industry 4.0 journey.  Contact us to get the conversation started!


The Record - Issue 21 : Summer 2021

IoT as an Agent of Change

BY ELLY YATES-ROBERTS from The Record – Issue 21:Summer 2021

As business leaders look to new markets and government agencies leverage technology to engage citizens, where will IoT, and the tools it can power, take us next? Read this interesting article about Microsoft’s Rodney Clark sharing his thoughts on IoT.

While its roots may be firmly planted in manufacturing, the internet of things (IoT) has grown in significance across all types of enterprise, including those in the public sector.  Market research firm IDC predicts there will be 55.7 billion connected IoT devices by 2025, generating 73.1 zettabytes of data, growing from 18.3 zettabytes in 2019. It also forecasts worldwide spending on IoT to pass the $1 trillion mark in 2022.

Microsoft and its ecosystem of partners are working to further drive the pace of innovation of IoT and help industry leaders to reap the rewards of the technology.

“IoT is playing a critical and expanding role for businesses, especially as they continue to navi-gate new experiences and challenges as a result of the pandemic,” says Rodney Clark, corporate vice president of channel sales and channel chief at Microsoft. “We are seeing customers expand from simply connecting assets, such as manufacturing equipment, to connecting entire environments, including factories, the supply chain and distribution networks, to further optimize productivity, operations and security.”

Many partners are building on these opportunities by creating industry-specific solutions to accelerate emerging opportunities, ranging from worker safety and automation to retail data and analytics. “We are also seeing even greater acceleration on solutions that represent a convergence of the physical and digital worlds – leveraging Azure digital twins, HoloLens and solutions such as Mesh, to accelerate time to value,” says Clark.

Take Bentley Systems for example. The infra-structure engineering software firm has built software solutions on Microsoft Azure which leverage digital twins. They are used by professionals and organizations worldwide for the design, construction and operations of roads and bridges, rail and transit, water and waste-water, public works and utilities, buildings and campuses, and industrial facilities.

Hybrid working, edge computing and the cloud have become integral factors of the modern business, and Clark believes that organizations could benefit hugely from using IoT in conjunction with these. “Cloud and edge computing are coming together to create new opportunities for organizations around the world, and we’re seeing increased innovation through connected environments that place digital twins, mixed reality and autonomous systems at the core,” he says.  “We can apply modern software techniques like analytics, simulation, autonomous control and interactions to digital replicas of physical environments to achieve previously impossible benefits that span across sectors.”

“A retail store can ensure inventory is tracked and shelves are always stocked, a supply chain can track and reduce carbon emissions, and a city plan can simulate various growth proposals to ensure the locality is making the best use of energy sources. This convergence of physical and digital spaces creates an abundance of opportunities for new, transformative solutions.”

With IoT becoming more commonplace, businesses and public sector organizations must address the challenge of managing and using the rapidly increasing amount of data that is available to them. This is where other technologies, like those related to the cloud, can work hand-in-hand with IoT.

Microsoft Azure and Teams are two products that not only fit into Microsoft’s Industry Cloud strategy seamlessly, but have also seen immense growth. In the third quarter of FY21, Microsoft saw Azure revenue grow by 50 percent and Teams usage continuing to rise, by 26 percent. “This speaks to both their impact and role in ensuring business continuity over the past year,” says Clark.

“IoT is playing a critical and expanding role for businesses, especially as they continue to navigate new experiences and challenges as a result of the pandemic”

Microsoft currently has specialized clouds for healthcare, retail, financial services, manufacturing and non-profit. “We believe that digital transformation can benefit any industry and our Industry Clouds allow customers to hit the ground running with industry-specific solutions, whether they are in early stages of their cloud journey or increasing their investment in cloud,” says Clark. “Post-Covid-19, in particular, we are finding a lot of companies that made that initial leap to the cloud are starting to ask themselves what’s next and what more they can be doing.”

Microsoft’s partners can be key to this next step, as Clark says: “They are a fundamental part of our Industry Cloud strategy since they are on the ground working day-to-day with customers and creating solutions, whether that’s through customized applications, analytics templates or collaboration models.”

While business continuity has been the main focus over the past 18 months, organizations are also increasingly focusing on operating more sustainably.

“Microsoft is deeply committed to sustainability and we are working closely with our customers and partners to drive change,” says Clark. “We collaborate with our partner ecosystem to develop solutions that enable customers to achieve business and sustainability goals. From reducing energy consumption, streamlined processes, waste management and much more, there are plenty of sustainable partner solutions available that are using technology such as Microsoft Azure, Microsoft 365, Dynamics 365, Power BI, Power Platform, data and artificial intelligence (AI), to help organizations achieve more through their sustainability goals.”

And IoT, data and the cloud also have an important role to play here.  “A great example of this is the work being done by Accenture, Avanade and Microsoft,” says Clark.

The three organizations are combining their expertise in cloud, data, AI, IoT, digital twins and digital transformation to help utility and energy companies in the UK transform their energy system and lower the cost of decarbonizing electricity, with the goal of achieving a net-zero target for carbon emissions by 2050.

To do this, the businesses will encourage the use of open industry data to provide secure and accessible information that will drive efficiency, support cross-industry innovation and improve asset performance. Renewable energy developer and operator SSE Renewables is working with the companies to reimagine its own operations.

“The scale of the net-zero challenge is so great and the significance of achieving it so important, we need all-hands-on-deck,” says Rachel McEwen, SSE Renewables’ chief sustainability officer, in a Microsoft article about the partnership. “The energy system – electricity in particular – must be completely decarbonized very quickly, so that trickier sectors like heat and transport can reach zero carbon emissions.”

“We collaborate with our partner ecosystem to develop solutions that enable customers to achieve business and sustainability goals”

“The answer to all the technological, market and regulatory challenges that result cannot possibly come from a single organization or sector. Partnerships, like the one between Microsoft and Accenture, are essential in bringing together an electricity utility like SSE with business and digital technology transformation specialists.”

Partner perspective

 

The cover story first appeared in The Record – Issue 21:Summer 2021 on Page 38. Interested in seeing the original article or the whole magazine? Click the cover below.

 


WEBINAR: The Efficiency Edge

June 22, 2021 | 12:00pm ET

This event has ended, but you can watch the recording here:

https://youtu.be/eru3xndPq1c

COVID-19 has accelerated the adoption of digital technology and transformed businesses forever. Hear four expert panelists discuss strategies and business practices on how to stay competitive in this new business and economic environment.

Our panelists will be discussing the importance of training and technological efficiency for businesses as they begin to deal with the realities of a post-COVID world, directly relevant to how companies will catch up on back orders, manage manufacturing and supply chain efficiencies and much more.

Hosted By:

 

 

 

 

 


Customer Story: Sage Automotive Interiors

Sage Automotive increases efficiency and production using Mariner’s Spyglass Visual Inspection and Microsoft Azure to monitor defects

"There's just like a million benefits, honestly, to having a map of every roll of fabric we have that shows us where every defect is and the false images. It is so advantageous for us because we're able to run it through a frame faster and trust that it will only stop on the true defects. It is increasing efficiency, production rates and quality and standards within the plant. With all this data, we are able to improve across the board.”
Sky Williams, Process Improvement Specialists / Sage Automotive Interiors

Product quality situation threatens margins

Sage Automotive Interiors, headquartered in Greenville, South Carolina, is a global manufacturer of high-performance automotive interior fabrics and has been operating for more than 70 years. They needed to solve a quality problem with its material. Sage worked with Microsoft partner Mariner to deploy Spyglass Visual Inspection (SVI), an AI, IoT, and deep-learning solution that utilizes Microsoft Azure IoT Hub and other Azure services. Sage started with a proof of concept, and the digital transformation leader at the company was pleasantly surprised at how easy it was to build the first deep-learning model, then scale with Azure. Using Mariner’s Spyglass Visual Inspection product, Sage is now able to run its production lines faster, increasing efficiency and quality standards across the board.

In 2019, a sales manager at Sage discovered a problem with material quality.

Ashton Paoletti, Director of Information Technology - Americas at Sage, said, “Some interior fabrics are lower-cost, higher-volume, lower-margin product, so any quality issues would eat into what little margin there was. The sales manager was previously a plant manager and knew we had a camera system in place and asked me to figure out if there was a way we could do something more with the images it produces to help improve quality.”

Paoletti reached out to his Microsoft representative, who facilitated an introduction with Mariner.

Based in Charlotte, North Carolina, Mariner delivers Deep Learning-powered solutions that help manufacturers reduce their costs of quality both internally and externally. Spyglass Visual Inspection, its flagship product, provides enhanced defect detection in situations that traditional machine vision systems have trouble accurately handling, and also offers Spyglass Assembly Verification, a solution that ingests engineering drawings and BoMs from a customer's ERP and then uses Deep Learning and visual inspection to ensure that highly complicated or customized articles are properly assembled. In a testament to its successful work, Mariner won the Microsoft 2020 IoT Partner of the Year award.

When Mariner spoke with Paoletti, Mariner suggested their Deep Learning approach. But this was Paoletti’s and Sage’s first Deep Learning project, and with the travails of their existing camera system, Paoletti said, “If I'd gone to them (the CEO and vice president of manufacturing) and said, ‘Hey, we should try this, because it might work,’ they would've said, ‘There's no way that's going to work.’”

Consequently, Paoletti asked Microsoft to fund a proof of concept in which Mariner built an initial deep-learning vision model from sample images. Paoletti said, “I was actually pleasantly surprised with how well building that first model went, how successful it was, and how easy it was to deploy and scale with Azure.”

After the POC results were presented in a regular manufacturing review, Sage’s VP of manufacturing was convinced, and he persuaded the CEO to approve a three-year, multi-site Spyglass Visual Inspection subscription.

Defect detection powered by Azure services

Spyglass Visual Inspection, available in the Azure Marketplace, provides real-time defect detection. It uses Azure NC-series virtual machines for data science, along with Azure Blob storage, Azure SQL Database, Azure IoT Edge, Azure IoT Hub, and Azure Container Registry for other functions. Mariner selected these technologies for their alignment to Spyglass Visual Inspection’s product roadmap drivers of confidentiality, scalability, and simplicity.

Spyglass Visual Inspection is comprised of edge and cloud components, and it utilizes an Azure IoT Edge server installed on-premises. Azure Blob storage is used for storing images and image metadata, while Azure SQL Database is used to report performance. Azure IoT Hub connects the Azure IoT Edge server to the cloud, and Azure Container Registry is used to manage the container lifecycle from cloud to edge.

Working with the vision model

Sky Williams, Process Improvement Specialist at Sage, was given the responsibility of integrating Spyglass Visual Inspection into Sage’s production and saw it as an opportunity to help her team of graders.

“If they are able to use a system that helps them get more yards of material, but also do their job better, they will have more confidence in their job,” Williams said. “My goal is for them to have an easier job and be more productive, thus increasing their pay and having a happier and more sustainable life.”

Williams spent many hours training the vision model by correcting or confirming its defect classification results to improve its performance. Now, instead of spending 100 percent of her time on inspection, Williams regularly adds new training data and spends most of her time “verifying the AI system is functioning the way it was last week and the same as it was functioning yesterday.”

Originally, the graders looked at the fabric as it went by. After initially being intimidated by using a computer screen with images of defects, they are now comfortable with it – a timely transition, as the upgraded line runs too fast for the human eye to detect defects.

Mapping rolls of fabric and tracking defects

Paoletti’s advice for those who would follow a similar path: “It’s not magic. There has to be some sweat equity put into making this work, and I certainly didn't have an appreciation for how much effort it would take for this to be successful. It's not just something you can come in and just casually think, ‘I will go look at these rolls today.’ You can tell Sky is really interested in making this successful.”

Williams said, “It is a labor pain for something new that, in the end, has good results. There's just like a million benefits, honestly, to having a map of every roll of fabric we have that shows us where every defect is and the false images. It is so advantageous for us because we're able to run it through a frame faster and trust that it will only stop on the true defects. It is increasing efficiency, production rates and quality and standards within the plant. With all this data, we are able to improve across the board.”

Williams and Mariner are collaborating on Spyglass Visual Inspection improvements to further increase the graders’ productivity. The VP of manufacturing has also approved an upgrade for root-cause analysis combining Spyglass Visual Inspection’s image analysis with Spyglass Connected Factory’s virtual production manager to notify process leads responsible for adverse trends in real time.

View the original customer story on Microsoft's site here.


FREE 30-Day Proof of Value

We know that many manufacturers are reluctant to move on Industry 4 or Digital Transformation for two simple reasons: They just don't know whether it will work, and if it does, whether there's any value in it.

If that sounds like you, and if you're suffering from high quality costs because of poor defect detection, then we have the solution for you. Deep Learning-powered SVI can dramatically improve detect detection and elimination in situations where traditional machine vision struggles -- and we can prove it. In our FREE 30-Day proof of value, we'll build you your own AI model, and then use it to show you just how big of a difference we can make in your quality control and assurance efforts.

It's a program that Microsoft has said should be industry standard -- and you can learn more about our free 30 Day PoV Offer right here.

 


NED Webinar: Reducing the Cost of Quality

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Click “HERE” to view the recording.
Find out more about our 30-Day PoV offer

Please join Mariner as we present the following New Equipment Digest webinar:

Wednesday, May 5, 2021
12:00PM – 1:00PM EDT

REGISTER

Quality is costly, and manufacturers are faced with balancing two competing curves: Pay for quality upfront, or potentially suffer the high costs of escapes generating rejects, returns, and ill will among customers.

But the costs of upfront quality can also be high, in terms of slowed lines, unneeded scrap or rework, and other factors.

What if you could reduce the costs of quality upfront? The entire set of equations gets shifted: Not only are those upfront costs reduced, but so are the customer-driven quality costs of escapes and bad sentiment.

Machine vision systems offer the promise of helping to reduce quality costs by detecting defects on the line – but in many cases are not as effective as they could be, and often still require lines to be run at reduced speed even as they miss some defects and misreport other, non-defect transient features like dust as defects.

Mariner’s Spyglass Visual Inspection solution (SVI) uses Deep Learning AI powered by Intel technologies to overcome these problems inherent to traditional machine vision system, and thus dramatically reduce manufacturers’ cost of quality.

Join Intel’s Global Sales Manager for IoT Philip van de Mortel, Mariner’s EVP of Product Engineering Peter Darragh, and Mariner’s VP of Data Science Stephen Welch for this webinar and learn about:

  • The cost of quality
  • Why AI has so far disappointed many manufacturers
  • How Deep Learning can achieve upwards of 30X improvements over traditional machine vision systems

Register today!

 

NOTICE TO ALL PUBLIC SECTOR OR STATE-OWNED ENTITY EMPLOYEES – Federal [including Military], State, Local and Public Education

This is a Microsoft partner event. Should items of value (e.g. food, promotional items) be disbursed to event participants, these items will be available at no charge to attendees. Please check with your ethics policies before accepting items of value.


Spyglass Visual Inspection By the Numbers

Intelligently minimize defects and reduce costs

Spyglass Visual Inspection harnesses the power of AI, IIoT, and image recognition to help manufacturers improve product quality while significantly reducing the costs associated with manufacturing flaws.

Effectively addressing quality concerns is critical in manufacturing

AI helps drive improved defect detection and better business outcomes:

  • 10-15% of total operating costs often associated with poor quality product (Forbes, 2018)
  • 1/3 of manufacturing executives now identify AI-driven technologies
    as crucial to driving customer satisfaction (Forbes, 2018)
  • $3.7 Trillion - the value that McKinsey forecasts AI-powered “smart factories” will generate by 2025.

What is Spyglass Visual Inspection?

Spyglass Visual Inspection is a rapid time-to-value QA optimization solution for manufacturers of any scale. It is designed to:

  • Quickly and accurately detect defects so that action can be taken to reduce waste and improve customer satisfaction
  • Drive continuous quality improvement by enabling greater visibility with a bird’s eye view of product quality across multiple lines or facilities so you can proactively improve processes.
  • Use predictive analytics to proactively improve quality processes and perform root cause analysis
  • Implement and ramp-up quickly ensuring a rapid return on your investment
  • Augment your existing vision system (if you have one) to gain additional ROI on that investment
  • Use a lean approach to implementing AI and IIoT so that you control costs and gain value at every stage.

Global glass manufacturer saves over $1M quarterly with Spyglass Visual Inspection

A global automotive glass manufacturer is using Spyglass Visual Inspection today as their comprehensive platform for defect detection, prediction, and analysis. Their challenge was that they needed more accurate defect detection in their glass cutting process to reduce false positives from their vision system that resulted in high monetary losses of $30/unit over 40 production lines. They were looking for a solution that would use custom vision, image recognition, and machine learning to more accurately detect defects at high speed and in large volume. With Spyglass Visual Inspection, they are already achieving over $1 million in quarterly savings.

Where can I learn more?

You can review our presentation, solution overview, and customer case studies. We'd love to connect to learn more about your quality initiatives and how we could help. Connect with us here.

How do I get started? The easiest way is wIth a no-risk, Spyglass Visual Inspection 30-Day Proof of Value.

Every manufacturer is different and every defect detection requirement is unique. It's critical to determine quickly that Spyglass Visual Inspection is the right fit to meet your quality goals and match your operating conditions. We'll start with a risk-free Proof of Value engagement to get you up and running with image formation and labeling -- and we'll even loan you the hardware and software, if you need it -- and then our data scientists will use Deep Learning AI to train the machine learning model accordingly. For most customers, the Proof of Value stage can be completed in 30 days -- and if we can't demonstrate success, you're under no obligation to proceed. Then, we'll Operationalize the solution in your factories using Spyglass as the platform to implement your customized visual inspection solution. Finally, we will Maintain and Improve the Machine Learning Model. On a quarterly basis, we will meet with your quality teams and help the model learn from any mistakes it has made. In this way, the accuracy will continue to improve over time.

Ready to find out more? Connect with us here.


Maximize existing QA vision systems with Deep Learning AI

The costs of poor quality are high

Quality assurance matters to manufacturers. The reputation and bottom line of a company can be adversely affected if defective products are released. If a defect is not detected, and the flawed product is not removed early in the production process, the damage can be costly – and the higher the unit value, the higher those costs will be. Indeed, poor quality potentially contributes to cost in a variety of ways:

  • Re-work costs
  • Production inefficiencies
  • Wasted materials
  • Expensive and embarrassing recalls

And worst of all, dissatisfied customers can demand returns.

The problem with traditional machine vision systems

To mitigate these costs, many manufacturers install cameras to monitor their products as they move along their production lines.

However, the data obtained may not always be useful – or more appropriately said, the data is useful, but existing machine vision systems may not be able to accurately assess it at full production speeds. That’s because too many variables make product defect analysis and prediction difficult. Furthermore, manufacturers need to perform their root cause analyses across a manufacturing process that has complex variables in order to determine which combinations of variables create high-quality products versus those that create inferior products. But to achieve this precision, the manufacturer needs to aggregate data across multiple systems to return a comprehensive view.

Legacy vision systems typically lack the accuracy, speed, and analytic capabilities required to fulfill manufacturers’ QA wish lists – and again, that’s because manufacturing processes can be incredibly complex, and older vision systems are often unable to consistently and accurately identify small flaws that may have a large impact on customer satisfaction. To further aggravate the situation, false positives (i.e., falsely detecting defects that aren’t actually present) can bog down production schedules.

On a larger level, the inability to aggregate data from multiple production lines or factories to determine the cause of variations in quality across multiple sites also prevents a holistic view of operational efficiency.

From the top down, then, many manufacturers find the current state of machine vision driven QA to fall far short of its potential for reducing the costs of quality.

Integrating legacy systems and AI on Azure

To mitigate these and other problems, our Spyglass Visual Inspection solution uses Deep Learning AI to achieve visibility over the entire line, which catches defects more quickly and more accurately than existing machine vision systems. Furthermore, because of its alerting and root-cause analysis capabilities, Spyglass Visual Inspection also helps to prevent defects before they ever arise.

Spyglass Visual Inspection is an easily implemented, rapid time-to-value QA solution that can reduce costs associated with product defects and increase customer satisfaction.

It works with images from any vision system, so companies who already have systems in place can leverage them for additional return-on-investment (ROI). By using cameras and other devices already in use on the production floor, the solution takes a lean approach to implementing new and emerging technologies like IoT, Deep Learning AI, and computer vision. This ensures that manufacturers control costs and achieve value at every stage of production and are truly able to reduce their cost of quality.

 

This figure outlines the architecture of the solution. Data from existing systems is placed at the front. Edge computing provides on-premises processing and real-time, AI-driven decision-making. The data then moves to Azure, where it is further processed. AI is again applied in a variety of ways that iteratively improve the system, and the results can be viewed using Power BI for even further insights into the system.

Benefits of Spyglass Visual Inspection

Spyglass Visual Inspection harnesses the power of Deep Learning AI, IoT, machine vision, and Azure. The result is that manufacturers minimize defects and reduce costs through advanced analytics. For the manufacturer, the benefits that matter are:

  • Rapid ROI: Easy implementation and ramp-up enables immediate process improvements and a rapid return on your investment.
  • Greater visibility: Predictive analytics and root cause analysis drive quality improvements across multiple lines or sites.
  • Leverages existing vision systems: Extracts more value from existing industrial cameras and devices by augmenting them with AI-driven real-time insights.
  • Fully transactable on the Azure Commercial Marketplace: No lengthy delays with procurement departments – the transaction can take place entirely on Microsoft paper, fast-tracking the above benefits for manufacturers.

All of these benefits combine, of course, into one overarching, easily-understood benefit: Spyglass Visual Inspection reduces a manufacturer’s cost of quality.

Azure services

Spyglass Visual Inspection is powered by Microsoft Azure. It leverages the following Azure services:

  • Azure IoT Edge ingests images from industrial cameras on the production line and runs cloud AI algorithms locally.
  • Azure IoT Hub receives images, meta data from images, and results from the defect detection analysis on the Edge.
  • Azure Stream Analytics enables users to create dashboards that offer deep insights into the types and causes of defects that are occurring across a massive number of variables.
  • Azure Data Lake Storage/Blob Storage stores the data. Because heterogeneous data from multiple streams can be stored, additional data types can be added to image-based analysis.
  • Azure SQL Database is used to store the business rules that define what a good or bad product is and what alerts should be generated in the analytics.
  • Azure Functions/Service Bus generates rules that trigger alerts so you can capture the most meaningful data for business users.
  • Power BI provides interactive dashboards that make data easy to access and understand, so users can make analytics-driven decisions.
  • Power Apps creates additional applications for manufacturers to act on the data and insights they have received.

Recommended next steps

If you want to learn more about Spyglass Visual Inspection -- how it works, and the results that manufacturers are achieving – be sure and visit our Deep Learning / Machine Vision resources page for eBooks, infographics, video, and more.  You can also find Spyglass Visual Inspection on Microsoft's Azure Marketplace and AppSource.

Ready to take the next step? You can also ask for a risk-free fit assessment to see if Spyglass Visual Inspection is right for your facilities and products, or feel free to contact us with any questions you might have.

This article was originally published at https://azure.microsoft.com/en-in/blog/maximize-existing-vision-systems-in-quality-assurance-with-cognitive-ai/ by Diego Tamburini, Principal Manufacturing Industry Lead, Azure Industry Experiences Team, and is updated and republished here with his kind permission.


Microsoft Azure and Intel IoT Join Forces

Intel and Microsoft have formed a partnership and have launched a joint website to support their collaboration and inform customers on its benefits.

They are working together to deliver intelligent, highly scalable IoT products and services without unnecessary complexity. This collaboration supports a fast-growing ecosystem of more than two dozen partners who offer a range of end-to-end solutions for specific industries and use cases. The goal is to help their growing ecosystem of IoT partners bring ever-advancing innovation and insights to customers, allowing them to make improvements to their business, increase operational efficiency, and create new revenue streams.

Mariner is using edge-to-cloud IoT technology from Intel and Microsoft to enhance customer experiences and tackle new challenges with secure, scalable solutions. Click on the Mariner solution below to find out about how these intelligent tools can create better business outcomes in any industry with production lines.

Mariner is Microsoft’s 2020 Worldwide IoT Partner of the year and member of Intel’s IoT Solutions Alliance.  Mariner's Spyglass Visual Inspection and Spyglass Connected Factory solutions are also marketplace ready with Microsoft and Intel.

 


Insight.tech Article on Machine Learning

By Erica Stevens for Insight.tech

A Guaranteed Model for Machine Learning

On the factory floor, wasted resources stack up fast for every real or imagined defect. When a good part is mistakenly labeled flawed, there’s lost time, efficiency, and machine effort. And when a defective part goes unnoticed and becomes the end customer’s problem? The potential consequences are even more severe.  <Read More at Insight.tech>

 

See Erica's interview with Mariner's EVP of Product Development, Peter Darragh on our product Spyglass Visual InspectionMicrosoft and Intel marketplace ready!


Vision Systems Design Webinar: Leveraging Deep Learning and AI Applications

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Click "HERE" to view the recording

Please join Mariner as we present the following Vision Systems Design webinar:

Leveraging Deep Learning and AI Applications in Manufacturing
Tuesday, December 15, 2020
12:00PM – 1:00PM EDT 

REGISTER

Manufacturers like you are successfully using artificial intelligence and deep learning in their operations today.

While these technologies help production processes reach new heights, one must carefully evaluate options before making any decisions.

Join us on December 15th to hear real-world case studies about how a chemical factory, a glass factory, and a fabric factory reduced their costs and increased their quality, and the role AI and Deep Learning played in those successes.

The webcast will cover Cloud limitations, the latest on the edge, hybrid edge/cloud setups for Industry 4.0, and how Intel and Microsoft technologies can help make it all come together. The webcast will conclude with a Q&A.

Sign up now to keep yourself on the cutting edge of machine vision technology.