[Music] hi everyone we're going to get started thank you so much for joining us uh here at ignite I know it's uh a little bit close to the end of the day and so I appreciate you staying for for one last session uh this session is accelerate industrial transformation with Azure iot operations my name is Cam vibrat I lead product management for the Azure iot team here at Microsoft and here's Sean hi I'm Sean parm I'm group product manager for Azure iot operations awesome so uh we've got a really uh exciting session today and the thing I want to start with is an announcement we are super excited to uh to be here today to announce General availability of azure iot operations this this is a big big step forward for us we announced this in public preview a year ago and here we are a year later at ignite with Azure iot operations now generally available so I want to start by just saying thank you to all of our customers and partners who were part of the preview we got incredibly great feedback it was super helpful in shaping how the product would evolve uh and getting that vocal vocal feedback from customers who are using the preview from Partners like like mayborn and wolf who are here in the in the audience today uh has really just helped improve the product and get us to a really great offering uh for General availability we're really energized to see how this capability is really giving our customers and partners a framework that they can use to scale and repeat industrial business outcomes and so I'm going to uh dive in today's SE to today's session and we're going to open with a little bit of a view into how industrial customers are thinking about the opportunity to use data to change their businesses so AI is creating opportun ities across every industrial sector and our company mission is to empower everyone to take advantage of technology to transform and to improve in manufacturing we see organizations looking to leverage data from multiple plants to optimize energy and resource usage while maintaining really high quality standards in consumer goods organizations are looking to accelerate datadriven testing to ensure consistent predictable product quality across the glob Globe while reducing operating expenses in energy organizations are looking to boost flexibility using data that supports use cases across the energy grid improving asset performance increasing availability and reducing cost and in life sciences we see folks really looking to ramp up new lines efficiently to minimize downtime and to use AI driven recommendations that are underpinned by operational data manufacturing is an incredibly data intensive industry and this data can be powerful in driving transformation on so many fronts whether it's productivity or cost efficiency or throughput but the key challenge with this opportunity is having the right analytics and insights that can use the data to inform better decision-making and AI can help close this Gap AI offers every organization the opportunity to drive change whether that's in the design and Engineering for better products whether it's transforming the factories and plants in the production process assisting Frontline workers to keep equipment and operations running well or just improving the efficiency and sustainability of the operation that's underway and a Common Thread in all of these Endeavors is how does an organization Forge a Competitive Edge through technology to drive efficiency and enhance customer experiences the data backs this up on this slide you can see some some some results from McKenzie research with manufacturers and there are real measurable improvements that can be realized with AI enabled data and automation an MIT survey of industrial leaders uh indicated that over 60% are experimenting with AI Solutions and over 30% are deploying AI in their production processes let's take a look at one of these customers in more detail so Chevron is a multinational company active in over 180 countries and their goals are to improve sustainability to minimize environmental impact by monitoring the health of their uh energy infrastructure to reduce downtime and extend the lifespan of their Machinery with a scalable architecture that works for all different types of facilities and equipment and to optimize cost and increase and to improve efficiency with remote monitoring of their operations and reducing the need to have a technician present all the time in case some might go wrong but their situation also has some very real uh factors that they have to tangle with they often operate critical infrastructure in harsh conditions where data access and con and connectivity for uh where data access and connectivity could be challenging and data has typically been siloed in in locations and as a result they have to Res they they have to rely on manual checks and maintenance for systems they want to improve their operations using a comprehensive edget to Cloud platform for connectivity monitoring and operations they're looking for stable reliable and secure infrastructure with consistent management tools no matter where the infrastructure is deployed they're looking to support modern application development deployment and management they want to integrate data from different environments into a unified platform so that they can take advantage of flexible AI analytics and and automated controls that can be managed with a single pane of glass and throughout all of these pieces security is Paramount and really must be driven at every layer of the stack and this is what they're using Azure Arc and Azure iot operations to solve they want to be able to create predictive models for equipment maintenance to reduce uh the need for manual inspection to increase safety and to help them with their environmental goals more broadly they want to democratize access to data and get more value from it with more resilience and more scalability now most most OT environments have similar complexities with data in silos that really limit making it that really that really limit insights and make it hard to scale success each site has its own unique teams its own equipment its own processes and practices and the data is siloed in a way that it doesn't always flow to the people and places where it's needed this kind of technical sprawl that has collected year-over-year from different point solutions that have been put in place really makes it impractical to deploy a solution that works and then to have that solution scale when they want to apply it to New use cases across multiple lines in a site across a diver diversity of geographic locations that's a challenge and without a clear and easy path to integrating and scaling a lot of customers get blocked uh on getting to the next level of digital transformation using AI so the first step to fixing this is taking a unified approach to data and management with a cohesive standard set of tools that can be used across cloud and Edge and what this does is it enables customers to use AI models and capabilities at all levels of their data and infrastructure and quickly scale and replicate those capabilities however they need to so when we think about organizations like Chevron and what they need to accomplish their goals we look at two pieces we look at the management side and the data side and on the management side the challenges around managing devices policies managing security at scale along with applications and cloud service ver version management these things are well known on the data side organizations often struggle with AI projects because the data that's fueling those projects isn't properly processed and contextualized as it feeds in to the to the analytics and the training that's underway and organizations still push through through this because they see that democratizing data and really building a data culture can make a huge difference for them in their bottom line so with an Adaptive Cloud approach we've really worked hard to directly connect Edge and Cloud so Edge configurations work just like what you have in the cloud just like your cloud services with a common set of management tools we've also provided data tools on the edge for data contextualization that happens close to the data source and direct line ingest into Data lakes and this all works with the same management and security principles that you see on the control plane side and in the cloud so having one set of tools across cloud and Edge from global hyperscale resources in the cloud all the way down to local resources and equipment on the shop floor in operational facilities this really helps organizations lower cost you can use the same skills the same resources that are proven in the cloud across all of your environments and this lets organizations really focus on building and scaling Solutions a lot more quickly so let's take a look at how the Adaptive Cloud approach really works to enable industrial transformation across the Enterprise so we start with compatibility and awareness of what's already in your operational environment this is your ecosystem of partner Solutions of infrastructure Brown field devices that you already have running your critical operations right now then what we do is we introduce components that connect your existing assets for data processing at the edge and a data pipeline that runs across solutions from Edge to cloud and Azure iot operations is that is is the first of that those components it is built to easily integrate into your operational environments to process and Route data on the edge and land signals into the Microsoft Cloud for analytics and insights it's built as a set of kubernetes services which provides resilience and the ability to deploy your own applications side by side with Azure iot operations and then looking at Global needs in the cloud having operational data flow to the cloud really enables Global visibility and Global benchmarking and you can use that to generate understanding that you can bring to every level of the organization from the shop floor to the boardroom and everywhere in between and this gets surfaced to the people in your organization using tools they use every day like fabric powerbi co-pilot and M365 we've built Azure iot operations using azure's adaptive Cloud approach and what that means is that an organization's skills and capabilities using Azure in the cloud apply to easily managing iot operations on the edge you can manage monitor and secure it with the services that you're used to using the Azure CLI Azure monitor Defender Sentinel and this all runs on Azure Arc enabled infrastructure uh including Azure local which we announced earlier this week so to dive into this in a little bit more detail I'm going to hand off to Sean go thanks gam all right so picking up using AI a iot operations reduces the complexity to build the overall end-end Solutions it integrates easily into industrial environments on the edge with the support of things like opcua mqtt and otel it works with fabric to power content wrench data experiences with analytics and Ai and this makes it easier for insights to drive actions to close feedback loops and to enable automation for iot operations we focused on three things first is as a data plane integrated from Edge to Cloud second is a foundation for AI on the edge and third is that all resources and capabilities on the edge can be Cloud managed these things together build a complete P platform for the edge that is directly connected and integrated with the overall Cloud Azure iot operations comes with a number of built-in features that can be expanded by partners and customers so we've built it from the ground up to not only provide immediate value but also to be able to be expanded by a large ecosystem of Partners isvs Industrial Automation companies to really build on and and on this platform from an infrastructure point of view since ashr it operations is built on the Adaptive Cloud approach it inherits a large set of capabilities such as AI enhanced Central management with security and health monitoring and cross compan identities and authorizations this really allows and empowers the systems that are built on and around AO to scale effectively and efficiently the ga release includes features such as the ability to collect data from your machines with a connector for opcua as well as additional uh connectors that are coming soon we enable data handling via highly available and a highly scalable MTD broker on the edge we efficiently store messages and store encoding and schema details in a schema registry we flow data to Azure and can transform that and contextualize that inside of data flows on the edge so that as that data transits through Azure iot operations it can be made relevant to not only local workloads but also to uh cloud-based data stores and we Empower OT roles to create pipelines and to those assets and those and enable those transformations in a way that utilizes their expertise to drive that re relevancy and to maybe correct or fix or filter capabilities right on the edge and we manage the Ed Edge data configurations via something called the Azure device registry which is what allows us to store all of these configurations in the cloud and make them available to other Cloud Developers we're also happy to uh to announce in public preview the ability to connect cameras and to process media with uh both oniv and media connectors this is a an immediate Next Step expansion like I said in public preview now and because Azure iot operations runs in art connected kubernetes clusters we also get a number of features that we inherit such as Azure key Vault Secret store extensions that allows us to sync with Azure key vault in the uh Cloud the Azure container storage which provides connections to Azure blob and to fabric and the Azure art Gateway connection to Azure art all right so now let's dive into the demo so first of all this is a picture of our Houston integration lab um you can see that we've built up a uh a a set of machines inside of a a cell something we call our our fluid processing cell with our partner Rockwell uh that also runs Allen Bradley plc's and we're using Azure iot operations to collect the sensor data and the uh the machine data uh that allows us to process things like temperatures pressures and flow rates to stream that and to process that in real time so let's take a look at how Azure iot operations collects this data in this environment and lands it in fabric so that we can take action on that and and and do things from it or with that all right now let me switch over give me just a second all right this looks good okay so this is something we call our operations experience it is targeted for OT P personnel as well as local it folks that may not be using the Azure portal they may be interested in working on things that are happening in the factory itself and so this is a dedicated Cloud hosted interface for those folks and it connects into all of these same kinds of azure identity and uh authorization capabilities that we're familiar with uh you can see that this screen is an overview which provides not only an overview of of that uh that particular instance of azure iot operations but also assets and things that are that we've configured and set up and are working with so the first thing we want to look into is something we call our ass endpoints this is where we uh Define the ability to connect into those assets and the devices and machines that ultimately provide the data for those assets you can see a couple that have been defined here one is a kepware endpoint one is a uh an Optics endpoint from Rockwell Automation uh which is the one that we'll use when we uh uh when we go through the you know through the data um you can see the asset itself that comes from that endpoint that connectivity end point we've created it here uh called the uh the Optics asset which drives that cell that you saw in the picture on the intro inside the asset itself asset itself you can see tags that the OT operator has defined or basically collected in order to define the overall asset that we're interested in looking into and you can see here we've done things like look at pumps or pull data from pumps from uh cooler set points d drive speed frequency voltage current Etc these are all things that the organization is interested in pulling out of this out of this cell this this plant this like I said this fluid processing cell that we want to make available to uh uh to the cloud and and for other processing on the edge so now we're now that those tags are selected and and are being pulled into the system let's go over to data flows so a data flow is literally that data flowing from that asset those asset tags and here what we've defined are a couple of data flows that are pointed at the cloud let's dive into one of those you can see this data flow now simply starting from a source with a transform in the middle and then the data endpoint data flow endpoint which is in the cloud to vent Hub in fabric you can see in this transform we've created a transport called a new property so what a new property is is that it gives the ability of the operator to say I want to add some information to this you know to this flow that's occurring so that I can provide more context provide some additional information that the Enterprise might want or I might want to deal with inside the cloud but that's not the only thing I can do you can see that there are a number of different transforms that are possible from performing fairly complex compute operations to filtering data right because we may not want to send everything to the cloud right we want to filter that at the edge and and and then send uh those relevant signals up to the up to the cloud we can rename as well as again providing new uh new properties to provide that context so what you really see here is that this is a truly a no code environment that is targeted for folks that are expert in what they do but that don't want to you know don't need to dive into causing you know uh you know things like code to be written in order to cause that to occur all right so that's pretty much the overview from a how do you get started from an Azure iot operations to connect to the asset get the data flowing do the processing and then send that off to the cloud so now let's jump over into a more typical it Cloud environment which is the Azure portal so inside the Azure portal the resources inside of azure or for Azure iot operations not only the the instance itself but the services and capabilities that surround Azure iot operation that are running in that connected art connected kubernetes cluster are visible and available to that cloud manager to do whatever it is that they would do just like they would do with cloud-based resources but these are based on the edge so we can not only see things like the Azure iot uh instance you can see the assets that we've defined here's that a you know the Optics asset and we can dive into these resources again just like any other CL resource we would so let's dive into the Azure iot operations instance and now you can see the fact that actually oh got lost here let me back up sorry there we go okay here we go um so you can see a couple of of capabilities uh components that are built into to Azure iot operations the mqt broker and the data flows let's look into the data flows uh instance and you can see the same two data flows from uh the Azure portal that we Define inside of azure iot operations that the uh OT and the local it folks uh set up okay so now let's jump over into fabric where we can see that same data flow showing up so I'll Point down here to the uh to the venous the um in fabric which is capturing that flow of data that's coming from Azure iot operations and is heading into a powerbi dashboard so this is a fabric view that shows how it is that we connect these things from the event house all the way through to the the dashboard that we're interested in looking at so let's jump over the dashboard so this is a powerbi dashboard same powerbi that everyone is uh familiar with and the same you know same sets of tooling same kind of uh of common experiences you can see not only the really cool charts and graphs right that that we've created off of this data you can also see because we've created a table the the tags that are coming off of that asset flow directly into the vent house and then into uh uh inside a fabric and then into the the powerbi uh dashboard so the same tags that we talked about earlier that that OT Persona went in defined captured maybe transformed or provided some context around that's now being seen directly inside of the the powerbi U uh dashboard and we're using those to create the rest of the uh uh the rest of the the graphs and and uh analysis okay so with that what we're going to do now is jump back and uh and show you some other capabilities so cam you want to take that you go back to the other screen for a second there we go so so that was great and so now you saw how Azure iot operation ations is set up to go collect data from the physical environment in this case from the fluid processing cell that we have in Houston and so what we're going to do now is we're going to take a look at how that data can power a more immersive experience uh for process monitoring using using the data that's flowing from from the the fluid processing cell and so what we've done to begin with is we've created a 3D model of that cell and this is the same model that was used to actually construct this was based on the same 3D information the cad models that were used to actually construct the physical cell in Houston except now we're rendering it in 3D uh using the Nvidia Omniverse capabilities that are uh hosted on on AKs and in in Azure and having this in 3D is incredibly powerful because it enables people to actually visualize and explore the process that's underway with a level of freedom and resolution that you just can't get from looking at it through cameras that are posted around the facility you can move around you can have Dynamic uh the dynamic ability to explore the scene in a way that's that's really powerful uh if you're trying to look at something that's remote that's happening far away um and then what we can do is we can actually bring that power bi view into the scene and so what you see uh on the left is a set of uh a set of uh charts in the dashboard uh and some data that's coming off of that that cell in the same way but what's great about this is that you can connect what's in the powerbi view to the objects that are in the three in the 3D scene so you have UI elements that are on the dashboard and they're driving interactions with what's on the screen so you don't have to guess what pump one or pump two is you can click on the pumps and see which one it is in the UI you can click on it in the UI and have it turn blue uh in the 3D scene and so if you imagine a remote uh expert helping a local technician figure out what to go do in a situation having this type of visualization on a tablet or on a remote view can be incredibly powerful just in terms of getting them to the right information to the right place to the right device quickly enough so they can get their job done and get back and get the operation up and running uh in an efficient way uh and what's even better than this is that the data can trigger actions in the scene itself and so in this case what we have is one of the pumps is in an alarm State and so it has turned red in the 3D model so there's no guesswork around which pump I need to go look at and what I need to go uh uh examine and then the data is also summarized you can kind of see it in blue in the in the in the dashboard there it's summarized by the co-pilot in powerbi using natural language and so rather than having a technician look at a table of numbers and try to kind of assess out what's happening and leaving room for error and and to just take up time in that process they get a really succinct summary they get to see the name of the pump that triggered the alarm what time the pressure started increasing what thresholds uh were exceeded they know exactly where to go to start troubleshooting and this kind of thing is incredibly powerful when it comes to helping with the types of problems we talked about earlier with accelerating uh efficiency and and improving uh the ability to go support an operation using data and Ai and so the best thing about this is all of it was built using off-the-shelf components that can be managed with this standard Azure toolbox from Cloud to edge you can build and deploy use cases like this with unprecedented unprecedented repeatability and scale and this is the real power that we have with the Adaptive Cloud approach so demos are awesome but I also want to talk about the customers and partners uh and that we've been working with and what they're doing uh with Azure iot operations uh and so here are a few uh organizations that have participated uh in our previews and are using iot operations to drive change in their in their physical operations husqavarna is leveraging azure iot operations to build a datadriven global supply chain that enhances efficiency and reduces cost and what this will do is it'll really enable them to improve processes and to deliver Goods more effectively to their customers groupo bimbo plans to extend the topof factory visibility that they've built on our existing Azure iot portfolio from 55 to 400 plants with Azure iot operations and what they're doing is they're incorporating Advanced Ai and and co-pilot capabilities for maintenance and process uh optimization SKF is enhancing its industrial iot environment with the Adaptive Cloud approach and Azure iot operations to support AI on the shop floor so they can improve equipment Effectiveness so they can enhance their security posture and what this does is it really helps them focus on quality reduce scrap and waste and increase yields and Eco petrol is standardizing its industrial data acquisition processes with Azure iot operations so they can streamline infrastructure reduce costs and enhance operational efficiency and this helps them Advance towards lower energy consumption and reduce carbon emissions using flexible and secure Edge platform capabilities and advanced Ai and then we're so excited to have so many partners working on Azure iot operations with us we have uh systems integrators and and Industrial iot Solutions providers who are driving long-term customer success with accelerators and Marketplace Solutions we have technology providers that are enhancing connectivity data modeling and analytics capabilities by integrating their Solutions with Azure iot operations and Azure Arc and I'll highlight three examples so dxc has a solution called intelligent boost in intelligent boost which is a Next Generation Ai and iot analytics platform that leverages Microsoft's Pas and AI cloud services avanade is introducing new consulting services on the Azure Marketplace that offers hands-on experience and support for depl deploying and managing uh iot data from Edge to cloud and Rockwell Automation you saw them in our demo earlier uh they're partnering with us to accelerate the factory of the future with easy to deploy Factory modered ization solutions that take an Adaptive Cloud approach customers can use Factory talk Optics with Azure iot operations for EDG to Cloud connectivity and contextualization of data and this is just the beginning we're working with more and more Partners every day to expand how Azure ey opportun Azure iot operations can help adopt and scale AI in your operational environments now we've talked a lot about the new product offering today and we're very excited about Azure iot operations but I do want to take a moment uh to acknowledge that we still have additional iot services that have been in market for quite some time uh for many years now are widely used by millions of customers uh widely used uh uh around the globe uh and connected to by millions of devices uh and we continue to support customers with their ongoing use of these Services we continue to double down on how we maintain the health security and operational stability of these services to ensure that customers long-term needs continue to be met by our in-market products as well as new products that we're introducing like Azure iot operations so you can get started today uh we have an expert Meetup uh upstairs in the hub uh and that'll continue through tomorrow there's some really great jump start uh projects on Azure jumpstart on Arc jumpstart uh that help you get started with deploying Azure iot operations building AI powered Solutions building the visualization types of solutions that uh that you saw in the demo today uh solutions that look at conversational AI interacting uh with data coming off of azure iot operations and even more and then connect with a partner we have great partners that we talked about uh earlier uh that are really standing ready to bring their expertise and their understanding of your physical operations and the equipment and the industries that you work in to help you adopt and scale your AI Solutions in your operation uh with that I'm going to say thank you very much thanks Sean for the awesome demo uh and thank you for joining our session today andun thanks and then we have a mic here in case uh folks want to ask questions question a little bit of time uh left over for that all right all right I think people want dinner that's fine awesome e e