Introduction to Edge AI with HPE Ezmeral Data Fabric

In this blog, we will be talking about how technology has shifted from on-premises data centers to the cloud and from cloud to edge. Then, we will explain data fabric, introduce HPE Ezmeral Data Fabric and investigate its capabilities. Finally, we will talk about Edge AI with HPE Ezmeral Data Fabric.

Kirjoittaja: Sadaf Nazari

To see what Edge AI is, we need to take a deeper look at the history of data processing over time.

The evolutions of data-intensive workloads

On-premises data centers

Back in 2000, almost everything was running locally in on-premises data centers. This means that everything from management to maintenance was on the company’s shoulders. It was fine but over time, when everything was getting more dependent on the internet, businesses faced some challenges. Here are some of the most important ones:

Infrastructure inflexibility

Over time, many new services and technologies are released and it should be taken into consideration that there might be a need to update the infrastructure or apply some changes to the services. 

This can be challenging when it comes to hardware changes. The only solution seems to be purchasing the desirable hardware, then manual configuration. It can be worse if, at some point, we realize that the new changes are not beneficial. In this case, we have to start all over again! 

This inflexibility causes wasting money and energy.

How about scaling on demand

A good business invests a lot of money to satisfy its customers. It can be seen from different angles but one of the most important ones always has the capacity to respond to the clients as soon as possible. This rule is also applied to the digital world: even loyal customers might change their minds if they see that the servers are not responding due to reaching their maximum capacity.

Therefore, there should be an estimation of the demand. The challenging part of this estimation is when this demand goes very high on some days during the year and one should forecast it. This demand forecasting has many aspects and it is not limited to the digital traffic from clients to servers. Having a good estimation of the demand for a particular item in the inventory is highly valuable.

Black Friday is a good example of such a situation. 

There are two ways to cope with this unusual high demand: 

  1. Purchase extra hardware to ensure that there will be no delay in responding to the customers’ requests. This strategy seems to be safe, but it has some disadvantages. First, since the demand is high on only certain days, many resources are in idle mode for a long time. Second, the manual configuration of the newly purchased devices should be considered. All in all, it is not a wise decision financially.
  2. Ignore that demand and let customer experience the downtime and wait for servers to become available. As it is easy to guess, it is not good for the reputation of the business.

This inflexibility is hard to address, and it gets worst over time. 

Expansion 

One might want to expand the business geographically. Along with marketing, there are some technical challenges. 

The issue with the geographical expansion is the delay that is caused by the physical distance between the clients and servers. A good strategy is to distribute the data centers around the world and locate them somewhere closer to the customers.

The configuration of these new data centers along with the security, networking, and data management might be very hard.

Cloud Computing

Having the challenges of the on-premises data centers, the first evolution of data-intensive workloads happened around 2010 when third-party cloud providers such as Amazon Web Services and Microsoft Azure were introduced. 

They provided companies with the infrastructure/services with the pay-as-you-go approach. 

Cloud Computing solved many problems with on-premises approaches. 

Risto and Timo have a great blog post about “Cloud Data Transformation” and I recommend checking it out to know more about the advantages of Cloud Computing.

Edge Computing

Over time, more applications have been developed, and Cloud Computing seemed to be the proper solution for them, but around 2020 Edge Computing got more and more attention as the solution for a group of newly-introduced applications that were more challenging. 

The common feature of these applications was being time-sensitive.  Cloud computing might act poorly in such cases since the data transmission to the cloud is time-consuming itself. 

The basic idea of Edge Computing is to process data close to where it is produced. This decentralization has some benefits such as:

Reducing latency

As discussed earlier, the main advantage of Edge Computing is that it reduces the latency by eliminating the data transmission between its source and cloud.

Saving Network Bandwidth 

Since the data is being processed in Edge Nodes, the network bandwidth can be saved. This matters a lot when the stream of data needs to be processed.

Privacy-preserving

Another essential advantage of Edge Computing is that the data does not need to leave its source. Therefore, it can be used in some applications where sending data to the cloud/on-perm data centers is not aligned with regulations.

AI applications

Many real-world use cases in the industry were introduced along with the advances in Artificial Intelligence. 

There are two options for deploying the models: Cloud-based AI and Edge AI. There is also another categorization for training the model (centralized and decentralized) but it is beyond the scope of this blog.

Cloud-based AI

With this approach, everything happens in the cloud, from data gathering to training and deploying the model.

Cloud-based AI has many advantages, such as being cost-saving. It would be much cheaper to use cloud infrastructure for training a model rather than purchasing the physical GPU-enabled computers.

The workflow of such an application is that after the model is deployed, new unseen data from the business unit (or wherever the source of data is) will be sent to the cloud, the decision will be made there and it will be sent back to the business unit.

Edge AI

As you might have guessed, Edge AI addresses the time-sensitivity issue. This time, the data gathering and training of the model steps still happen in the cloud, but the model will be deployed on the edge nodes. This change in the workflow not only saves the network bandwidth but also reduces the latency. 

Edge AI opens the doors to many real-time AI-driven applications in the industry. Here are some examples: 

  • Autonomous Vehicles
  • Traffic Management Systems
  • Healthcare systems
  • Digital Twins

Data Fabric

So far, we have discussed a bit about the concepts of Cloud/Edge computing, but as always, the story is different in real-world applications.

We talked about the benefits of cloud computing but it is important to ask these questions ourselves:

  • What would be the architecture of having such services in the Cloud/Edge?
  • What is the process of migration from on-prem to cloud? What are the challenges? How can we solve them? 
  • How can we manage and access data in a unified manner to avoid data silos?
  • How can we orchestrate distributed servers or edge nodes in an optimized and secure way?
  • How about monitoring and visualization?

Many companies came up with their own solutions for the above questions with manual work but there is a need for a better way for a business to focus on creating values, rather than dealing with these issues. This is when Data Fabric comes into the game. 

Data Fabric is an approach for managing data in an organization. Its architecture consists of a set of services that make accessing data easier regardless of its location (on-prem, cloud, edge). This architecture is flexible, secure, and adaptive.

Data Fabric can reduce the integration time, the maintenance time, and the deployment time. 

Next, we will be talking about the HPE Ezmeral Data Fabric (Data Fabric is offered as a solution by many enterprises and the comparison between them is beyond the scope of this blog).

HPE Ezmeral Data Fabric

HPE Ezmeral Data Fabric is an Edge to Cloud solution that supports industry-standard APIs such as REST, S3, POSIX, and NFS. It also has an ecosystem package that contains many open-source tools such as Apache Spark and allows you to do data analysis. 

You can find more information about the benefits of using HPE Ezmeral Data Fabric here.

As you can see, there is an eye-catching part named “Data Fabric Event Stream”. This is the key feature that allows us to develop Edge AI applications with the HPE Ezmeral Data Fabric.

Edge AI with HPE Ezmeral Data Fabric – application

An Edge AI application should contain at least one platform for orchestrating the broker cluster such as Kafka, some tools such as Apache Spark, and a data store. This might not be as easy as it seems, especially in large-scale applications when we have millions of sensors, thousands of edge sites, and the cloud. 

Fortunately, with HPE Ezmeral Data Fabric Event Stream, this task can be done much easier. We will go through it by demonstrating a simple application that we developed. 

Once you set up the cluster, the only thing you need to do is to install the client on the edge nodes, connect them to the cluster (by a simple line maprlogin command), and then enable the services that you want to use. 

For the event stream, it is already there, and again it just needs a single command for creating a stream and then creating topics in it.

For the publisher (also called producer), you need to just send the data from any source to the broker, and for the subscriber (also called consumer) the story is the same.

For using open-source tools such as Apache Spark (or in our case Spark Structure Streaming), you just need to install them on the mapr client, and the connection between the client and the cluster will be automatically established. So you can run a script in edge nodes and access data in the cluster.

Storing data is again as simple as the previous ones. The table creation can be done with a single command, and storing it is also straightforward.

Conclusion

To sum up, Edge AI has a promising future, and leveraging it with different tools such as Data Fabric can be a game changer.

Thank you for reading this blog! I would also like to invite you to our talk about the benefits of Edge Computing in Pori on 23/09/2022!

More information can be found here.

Sadaf Nazari.

Your AI partner can make or break you!

Industries have resorted to use AI partner services to fuel their AI aspirations and quickly bring their product and services to market. Choosing the right partner is challenging and this blog lists a few pointers that industries can utilize in their decision making process.

Kirjoittaja: Karthik Sindhya

 

Large investments in AI clearly indicate industries have embraced the value of AI. Such a high AI adoption rate has induced a severe lack of talented data scientists, data engineers and machine learning engineers. Moreover, with the availability of alternative options, high paying jobs and numerous benefits, it is clearly an employee’s market.

Market has a plethora of AI consulting companies ready to fill in the role of AI partners with leading industries. Among such companies, on one end are the traditional IT services companies, who have evolved to provide AI services and on the other end are the AI start-up companies who have backgrounds from academia with a research focus striving to deliver the top specialists to industries.

Considering that a company is willing to venture into AI with an AI partner. In this blog I shall enumerate what are the essentials that one can look for before deciding to pick their preferred AI partner.

AI knowledge and experience:  AI is evolving fast with new technologies developed by both industries and academia. Use cases in AI also span multiple areas within a single company. Most cases usually fall in following domains: Computer vision, Computer audition, Natural language processing, Interpersonally intelligent machines, routing, and motion and robotics. It is natural to look for AI partners with specialists in the above areas.

It is worth remembering that most AI use cases do not require AI specialists or super specialists and generalists with wide AI experience could well handle the cases.

Also specialising in AI alone does not suffice to successfully bring the case to production. The art of handling industrial AI use cases is not trivial and novice AI specialists and those that are freshly out of University need oversight. Hence companies have to be careful with such AI specialists with only academic experience or little industrial experience.

Domain experience: Many AI techniques are applicable across cases in multiple domains. Hence it is not always necessary to seek such consultants with domain expertise and often it is an overkill with additional expert costs. Additionally, too much domain knowledge can also restrict our thinking in some ways. However, there are exceptions when domain knowledge might be helpful, especially when limited data are available.

A domain agnostic AI consultant can create and deliver AI models in multiple domains in collaboration with company domain experts.

Thus making them available for such projects would be important for the company.

Problem solving approach This is probably the most important attribute when evaluating an AI partner. Company cases can be categorised in one of the following silo’s:

  • Open sea: Though uncommon, it is possible to see few such scenarios, when the companies are at an early stage of their AI strategy. This is attractive for many AI consultants who have the freedom to carve out an AI strategy and succeeding steps to boost the AI capabilities for their clients. With such freedom comes great responsibility and AI partners for such scenarios must be carefully chosen who have a long standing position within the industry as a trusted partner.
  • Straits: This is most common when the use case is at least coarsely defined and suitable ML technologies are to be chosen and take the AI use case to production.  Such cases often don’t need high grade AI researchers/scientists but any generalist data scientist and engineer with the experience of working in an agile way can be a perfect match. 
  • Stormy seas: This is possibly the hardest case, where the use case is not clearly defined and also no ready solution is available. The use case definition is easy to be defined with data and AI strategists, but research and development of new technologies requires AI specialists/scientists. Hence special emphasis should be focused on checking the presence of such specialists. It is worth noting that AI specialists availability alone does not guarantee that there is a guaranteed conversion to production. 

Data security: Data is the fuel for growth for many companies. It is quite natural that companies are extremely careful with safeguarding the data and their use. Thus when choosing an AI partner it is important to look and ask for data security measures that are currently practised with the AI partner candidate organisation. In my experience it is quite common that AI specialists do not have data security training. If the company does not emphasise on ethics and security the data is most likely stored by partners all over the internet, (i.e. personal dropbox and onedrive accounts) including their private laptops.

Data platform skills: AI technologies are usually built on data. It is quite common that companies have multiple databases and do not have a clear data strategy. AI partners with inbuilt experience in data engineering shall go well, else a separate partner would be needed.

Design thinking: Design thinking is rarely considered a priority expertise when it comes to AI partnering and development. However this is probably the hidden gem beyond every successful deployment of AI use case. AI design thinking adopts a human centric approach, where the user is at the centre of the entire development process and her/his wishes are the most important. The adoption of the AI products would significantly increase when the users problems are accounted for, including AI ethics.

Blowed marketing: Usually AI partner marketing slides boast about successful AI projects. Companies must be careful interpreting this number, as often major portions of these projects are just proof of concepts which have not seen the light of day for various reasons. Companies should ask for the percentage of those projects that have entered into production or at least entered a minimum viable product stage.

Above we highlight a few points that one must look for in an AI partner, however what is important over all the above is the market perception of the candidate partner, and as a buyer you believe there is a culture fit, they understand your values, terms of cooperation, and their ability to co-define the value proposition of the AI case. Also AI consultants should stand up for their choices and not shy away from pointing to the infeasibility and lack of technologies/data to achieve desired goals set for AI use cases fearing the collapse of their sales. 

Finding the right partner is not that difficult, if you wish to understand Solita’s position on the above pointers and looking for an AI partner don’t hesitate to contact us.

Author: Karthik Sindhya, PhD, AI strategist, Data Science, AI & Analytics,
Tel. +358 40 5020418, karthik.sindhya@solita.fi

Microchips and fleet management

The ultimate duo for smart product at scale

Kirjoittaja: Risto Saari Solution Architect

We have seen how cloud based manufacturing has taken a huge step forward and you can find insights listed in our blog post The Industrial Revolution 6.0. Cloud based manufacturing is already here and extends IoT to the production floor. You could define a connected factory as a manufacturing facility that uses digital technology to allow seamless sharing of information between people, machines, and sensors.

if you haven’t read it yet there is great article Globalisation and digitalisation converge to transform the industrial landscape.

There is still much more than factories. Looking around you will notice a lot of smart products such as smart TVs, elevators, traffic light control systems, fitness trackers, smart waste bins and electric bikes. In order to control and monitor the fleet of devices we need rock solid fleet management capabilities that we will cover in another blog post.

This movement towards digital technologies, autonomous systems and robotics will require the most advanced semiconductors to come up with even more high-performance, low power consumption,  low-cost, microcontrollers in order to carry complicated actions and operations at Edge. Rise in the Internet of Things and growing demand for automation across end-user industries is fueling growth in the global microcontroller market.

As Software has eaten the world and every product is a data product there will only be SaaS Companies.

Devices at the field must be robust to connectivity issues, in some cases withdraw -30 ~ 70°C operating temperatures, build on resilience and be able to work in isolation most of the time. Data is secured on device, it stays there and only relevant information is ingested to other systems. Machine-to-machine is a crucial part of the solutions and it’s nothing new like explained in blog post M2M has been here for decades.

Microchip powered smart products

Very fine example of world class engineering is Oura Ring.  On this scale it’s typical to have Dual-core​ ​arm-processor:​ ​ARM​ ​Cortex​ ​based​ ​ultra​ ​low​ ​power​ ​MCU with limited ​memory​ ​to​ ​store​ ​data​ ​up​ ​to​ ​6​ ​weeks. Even at this  size it’s packed with sensors such as infrared​ ​PPG​ ​(Photoplethysmography) sensor, body​ ​temperature​ ​sensor, 3D​ ​accelerometer​ ​and​ ​gyroscope.

Smart watches are using e.g. Exynos W920, a wearable processor made with the 5nm node, will pack two Arm Cortex-A55 cores and an Arm Mali-G68 GPU. Even on this small size it includes 4G LTE modem and a GNSS L1 sensor to track speed, distance, and elevation when watch wearers are outdoors.

Taking a mobile phone from your pocket it can be powered by the Qualcomm Snapdragon 888 capable of producing 1.8 – 3 GHz 8 cores with 3 MB Cortex-X1.

Another example is Tesla famous of having Self-Driving Chip for autonomous driving chip designed by Tesla the FSD Chip incorporates 3 quad-core Cortex-A72 clusters for a total of 12 CPUs operating at 2.2 GHz, a Mali G71 MP12 GPU operating 1 GHz, 2 neural processing units operating at 2 GHz, and various other hardware accelerators. infotainment systems can be built on the  seriously powerful AMD Ryzen APU powered by RDNA2 graphics so you play The Witcher 3 and Cyberpunk 2077 when waiting inside of your car.

Artificial Intelligence – where machines are smarter

Just a few years ago, to be able to execute machine learning models at Edge on a fleet of devices was a tricky job due to lack of processing power, hardware restrictions and just pure amount of software work to be done. Very often the imitation is the amount of flash and ram available to store more complex models on a particular device. Running AI algorithms locally on a hardware device using edge computing where the AI algorithms are based on the data that are created on the device without requiring any connection is a clear bonus. This allows you to process data with the device in less than a few milliseconds which gives you real-time information.

Figure 1. Illustrative comparison how many ‘cycles’ a microprocessor can do (MHz)

The pure power of computing power is always a factor of many things like the Apple M1 demonstrated how to make it much cheaper and still gain the same performance compared to other choices. So far, it’s the most powerful mobile CPU in existence so long as your software runs natively on its ARM-based architecture. Depending on the AI application and device category, there are various hardware options for performing AI edge processing like CPUs, GPUs, ASICs, FPGAs and SoC accelerators.

Price range for microcontroller board with flexible digital interfaces will start around 4$ with very limited ML cabalities . Nowadays mobile phones are actually very powerful to run heavy compute operations thanks to purpose designed super boosted microchips.

GPU-Accelerated Cloud Services

Amazon Elastic Cloud Compute (EC2) is a great example where P4d instances AWS is paving the way for another bold decade of accelerated computing powered with the latest NVIDIA A100 Tensor Core GPU. The p4d comes with dual socket Intel Cascade Lake 8275CL processors totaling 96 vCPUs at 3.0 GHz with 1.1 TB of RAM and 8 TB of NVMe local storage. P4d also comes with 8 x 40 GB NVIDIA Tesla A100 GPUs with NVSwitch and 400 Gbps Elastic Fabric Adapter (EFA) enabled networking. In practice this means you do not have to take coffee breaks so much and wait for nothing  when executing Machine Learning (ML), High Performance Computing (HPC), and analytics. You can find more on P4d from AWS.

 

Top 3 benefits of using Edge for computing

There are clear benefits why you should be aware of Edge computing:

1. Reduced costs where costs for data communication and bandwidth costs will be reduced as fewer data will be transmitted.

2. Improved security when you are processing data locally, the problem can be avoided with streaming without uploading a lot of data to the cloud.

3. Highly responsive where devices are able to process data really fast compared to centralized IoT models.

 

Convergence of AI and Industrial IoT Solutions

According to a Gartner report, “By 2027, machine learning in the form of deep learning will be included in over 65 percent of edge use cases, up from less than 10 percent in 2021.” Typically these solutions have not fallen into Enterprise IT  – at least not yet. It’s expected Edge Management becomes an IT focus by utilizing IT resources to optimize cost.

Take a look on Solita AI Masterclass for Executives how we can help you to bring business cases in life and you might be interested taking control of your fleet with our kickstart. Let’s stay fresh minded !

Factory Floor and Edge computing

Kirjoittaja: Marko Siitonen IoT Architect

Happened last time

In the first part of this blog series I discussed the industry 4.0 phenomenon: Smart and Connected Factory, what benefits it brings, what is IT/OT convergence and gave a short intro about Solita’s Connected Factory Kickstart. 

This part is more focused on the data at Factory floor and how AWS services can help in ingesting the data from factory machinery.

Access the data and gain benefits from Edge computing

So what is the data at the Factory floor? It is generated by machinery systems using many sensors and actuators. See the following picture where on the left there is a traditional ISA-95 pyramid for factory data integrating each layer with the next. The right side represents new thinking where we can ingest data from each layer and take advantage of IT/OT convergence using AWS edge and cloud services.

PLC (Programmable Logic Controller) typically has dedicated modules for inputs and dedicated modules for outputs. An input module can detect the status of input signals like switches and an output module controls devices such as relays and motors.

Sensors are typically connected to PLC’s. To access the data and use it in other systems, PLC’s can be connected to an OPC-UA server. The server can provide access to the data. One traditional use case is to connect PLC to factory SCADA systems for high level supervision of machines and processes. OPC-UA defines a generic object model and each object can be associated with data type, timestamp, data quality and current value and they can have a hierarchy. Every kind of device, function, and system information can be described using this meta model.

 

AWS services that ease data access at the factory

AWS Greengrass is an open source edge software which integrates to AWS Cloud. It enables local processing, messaging, Machine Learning (ML) inference, device mesh and many pre-baked software components for speed up application development. 

AWS Sitewise is a cloud service for collecting and analyzing data from factory environments. It provides Greengrass compatible edge components for example for data collection from OPC-UA server and for streaming data to AWS Sitewise. Sitewise has a built in time-series database, data modeling capabilities, API layer and portal, which can be deployed and run at the edge as well (which is amazing!). 

The AWS Sitewise asset and data modeling is for making a virtual presentation of industrial equipment or process. Data model supports hierarchies, metrics and real time calculations, for example for calculating OEE (Overall Equipment Effectiveness). Each asset is enforced to use data mode that validates incoming data and schema.

Why industrial use cases with AWS?

I prefer more hands-on work than reading Gartner papers; anyhow AWS has been named as a Leader for the eleventh consecutive year and has secured the highest and furthest position on the ability to execute and completeness of vision axes in the 2021 Magic Quadrant for Cloud Infrastructure and Platform Services. It’s very nice to see how AWS is taking industrial solutions seriously and packaging those to a model that is easy to take in use for building digital services to the factory floor and cloud.  

 1. AWS Sitewise – The power of data model, ingest, analyze and visualize

Sitewise packages nice features which I feel are the greatest are the data and asset modeling, near real time metric calculations (even on edge), visualization and build in time series database. Sitewise is nicely supported by CloudFormation, so you can automate the deployment and even build data models according to your OPC-UA data model automatically (Meta driven Industry standard data model). The Fact that there are edge processing and monitoring capabilities with a portal available makes the Sitewise a really competitive package.

2. AWS Greengrass – Edge computing and secure cloud integration

Speeds up edge application development with public components, like the OPC-UA collector, StreamManager and Kinesis Firehose publisher. The latest Greengrass version 2.x has evolved and has lots of great features. You can provision and run a solution on real hardware or simulate on an EC2 instance or Docker, as you wish. One way to provision Greengrass devices to AWS cloud is to use IoT Fleet Provisioning, where certificates for the device are created on the first connection attempt to the cloud. Applications are easy to deploy from cloud IoT Core to edge Greengrass instances. You can also run serverless AWS Lambdas at the edge, which is really superb! All in all, the complete Greengrass 2.0 package will speed up development.

3. Cloud and Edge – Extra layer of Security

SItewise and Greengrass use AWS IoT Core security features, like certificate based authentication, IoT policies, TLS 1.2 on transport and device defender, which brings the security to a new level. It’s also possible to use custom Certificate Authorities (CA) to issue edge device certificates. Custom CA’s can be stored in AWS CloudHSM and AWS Certificate Manager. Now I can really say that security is our best friend.

4. Agile integration to other solutions

Easy way for integrating data to other solutions is to use Sitewise Edge and Cloud APIs. If you deploy Sitewise to the edge the API is usable there as well, and you can use the data for other factory systems, like MES (Manufacturing Execution System). At least I think this will combine the IT and OT worlds like never before.

5. AutoML for Edge computing

AutoML is for people like me and citizen data scientists, something that will speed up business insights when creating a lot of notebooks or python code is not needed anymore.

These AutoML services are used to organize, track and compare Machine Learning training. When auto deploy is turned on the best model from the experiment is deployed to the endpoint and the best model is automatically selected using the Bandit algorithm. Besides these Amazon SageMaker model monitor will continuously monitor the quality of your machine learning models in real time and I can focus on talking with people and not only machines. 

 

Stay tuned for more

I think that AWS is making it easier to combine cloud workloads with edge computing. Stay tuned for the next blog post where we dig more into the cloud side of this, including Sitewise, Asset and data model, visualization and alarms. And please take a look at the “Predictive maintenance data kickstart” if you haven’t yet:

https://www.solita.fi/en/solita-connected/