Francesco Baldassarri - Deliver Data at Scale - Codemotion Amsterdam 2019 -
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IoT revolution is ended. Thanks to hardware improvement, building an intelligent ecosystem is easier than never before for both startups and large-scale enterprises. The real challenge is now to connect, process, store and analyze data: in the cloud, but also, at the edge. We’ll give a quick look on frameworks that aggregate dispersed devices data into a single global optimized system allowing to improve operational efficiency, to predict maintenance, to track asset in real-time, to secure cloud-connected devices and much more.
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Francesco Baldassarri - Deliver Data at Scale - Codemotion Amsterdam 2019 -
- 1. Deliver Data at Scale, at the Edge and in the Cloud Francesco Baldassarri Amsterdam | April 2-3, 2019
- 2. Nice to meet you! Francesco Baldassarri Developer Relations Manager and Technical Evangelist email: francesco@francesco.world Twitter/Telegram: @francesco_1985 it.linkedin.com/in/fbaldassarri
- 4. Deliver Data... Devices Fleet “16 Million units by 2020”
- 7. Deliver Data... Devices Fleet Gateways Data Data Cloud Infrastructure Storage Network Nodes Analytics AI / ML / DL Ingestion Functions Data Presentation Application CPUs / GPUs
- 9. ...at Scale... Cloud Infrastructure Storage Network Nodes ...requesting more and more cloud resources. CPUs / GPUs
- 10. ...at Scale... Cloud Infrastructure Storage CPUs / GPUs Network Nodes
- 11. ...at the Edge... Bringing onboard intelligence of Cloud to the IoT devices...
- 12. Devices Fleet Edge Techs Tracking Neural Network Deep Learning CPU / GPU / TPU ...at the Edge... ● ML inference at the Edge ● Dedicated TPUTM and VPU ● Local compute ● Run on Android Things and on various GNU/Linux-based OS ● Support enhanced security through hardware-root-of-trust ● Securely connected devices to Cloud providers ● Works seamlessly with cloud for hassle-free device provisioning ● Serverless scalability
- 13. Devices Fleet Edge Techs Tracking Neural Network Deep Learning CPU / GPU / TPU ...at the Edge... Intel's Myriad™ X VPU AAEON Up AI Edge Google’s TPU™
- 14. Devices Fleet Gateways LESS Data Cloud Infrastructure Storage Network Nodes Analytics AI / ML / DL Ingestion Functions Edge Techs Tracking Neural Network Deep Learning CPU / GPU / TPU LESS Data Data ...at the Edge... Presentation Application CPUs / GPUs
- 15. ...and in the Cloud Fetch Clean Prepare Train Model Evaluate Model Monitor / Prediction / Projection Tune Model Parameters Deploy Updated Model ★ continuous deploy/optimization of training model ★ update remotely OTA in real-time ★ on selected group of devices
- 16. Devices Fleet Gateways Data Cloud Infrastructure Storage Network Nodes Analytics AI / ML / DL Ingestion Presentation Functions Edge Techs Tracking Neural Network Deep Learning CPU / GPU / TPU Data Data Application ...and in the Cloud Update config & deploy new ML model Update config & deploy new ML model Review CPUs / GPUs
- 17. Devices Fleet Gateways Data Cloud Infrastructure Storage CPUs / GPUs Network Nodes Analytics AI / ML / DL Ingestion Presentation Functions Edge Techs Tracking Neural Network Deep Learning CPU / GPU / TPU Data Data Application ...and in the Cloud Control Update config & deploy new ML model Review
- 19. and Enjoy Codemotion Amsterdam That’s all, Folks! Thank You
Editor's Notes
- Abstract: IoT revolution is ended. Thanks to hardware improvement, building an intelligent ecosystem is easier than never before for both startups and large-scale enterprises. The real challenge is now to connect, process, store and analyze data: in the cloud, but also, at the edge. We’ll give a quick look on frameworks that aggregate dispersed devices data into a single global optimized system allowing to improve operational efficiency, to predict maintenance, to track asset in real-time, to secure cloud-connected devices and much more.
- Nice to meet you!
- In the abstract for this session I said something really strong: the IoT revolution is ended. Wohw. He’s mad, you probably thought. But IoT is now already part of our everyday life. We're surrounded by this technology every day. We are floating in globally dispersed devices. IoT is already a fact. So today we're not gonna be talking about the Internet of Things, that sounds pretty generic: we’ll talk about data; in particular about “Deliver Data”. Data allow us to deliver real-time predictive alert to our customers, to hunt for a parking spots we don't have to circle around the block three or four times or to optimize our energy consumption everyday/everywhere.
- Analysts expect IoT devices to reach 16 millions units by 2020,
- And What they do? They are generating more and more data day by day.
- Petabyte. Exabyte every day! Ok. The mission here is to deliver data efficiently. Obviously, you are doing that in the cloud: your personal cloud, Microsoft Azure, Amazon AWS or Google Cloud, it doesn’t matter at the moment.
- So, at the end, this is your typical scenario. In 2019. The Cloud Infrastructure has its physical layer, composed simply by Storage, a lot of CPUs and network switches. And by a service layer: composed by the various applications-as-a-service, infrastructure-as-a-service, platform-as-a-service, cloud functions, dataflow, data processing, etc… But now the problem is “How to scale in the future with this upcoming massive epic gargantuesque amount of data?”. Data Ingestion and Processing could become tricky.
- The easier reply is: act directly on the physical layer of the cloud infrastructure. Doesn’t matter is you are using scalable cloud functions, fully-managed cloud services or whatever… at the end, in the real world, you are...
- ...requesting more and more resources to the cloud provider. More space, more bandwidth, upscaling the processing needs. And, this what does it mean?
- Moneys, a lot of moneys spent! More resources is equal to adding more expenses. So, are we sure is this the best way to scale?
- Bring intelligence directly on the IoT devices at the Edge, that are connected to the Cloud.
- In addition: Discover how efficiently your devices operate, manage global assets, and carry out firmware updates on Cloud IoT platform, Predictive maintenance, Real-time asset tracking, Logistics & supply chain management (fleet management, inventory tracking, cargo integrity monitoring, and other business-critical functions), Build a comprehensive solution that spans across billions of sensors and edge devices, and bring a new level of intelligence and automation to entire homes, buildings, or cities. Securely connect a few or millions of your globally dispersed devices through protocol endpoints that use automatic load balancing and horizontal scaling to ensure smooth data ingestion under any condition.
- This devices allow onboard ML inference and supports multiple different onboard frameworks, including TensorFlow Lite, NN API, Caffe, ... Finally, the hardware returns to have a major role!
- So, this is our updated scenario since the introduction of AI-enabled IoT device at the Edge. In 2019. But it’s not ended here. There is an amazing additional feature we can take advantage of...
- But, the cloud remain critical even using this “Intelligence at the Edge” approach. 1) Acquire Data. 2) Clean the data to optimize the quantity and quality. Just select only the data that really matter for your scenario. Remove Noise. And simply trash the rest. 3) Prepare the data compressing in smaller bundle. 4) Train the model using the new new data. 5) Evaluate the model. 6) Make your projection/prediction. 7) Tune the model hyperparameters locally on your computer. 8) Deploy the refined model on the device at the edge. This can be done in a continuous looped approach. Keeping the Edge device always optimized remotely.
- Resuming
- Alternative Scenario
- If you are interested in going deepen into the topic, and if you want to get your hand dirty on this technology, you can find all the TPU and VPU development kit on Mouser.com.
- Codemotion Amsterdam 2019