Learn how you can use Data Box Edge with built-in FPGAs to deliver accelerated intelligence at the edge - all controlled and managed from Azure. We’ll demo step by step so you can clearly understand how to achieve this in your environment.
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Run AI powered apps on the edge with Azure Data Box Edge
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4. Azure intelligent Edge + Cloud taxonomy
Microcontroller
Azure Sphere
• Integrated Circuit designed to
govern a specific operation in
an embedded system
• Highly-secured, connected
MCU
• Azure Sphere Linux OS for
modern MCUs
• Included Azure IoT device SDK
IoT Devices
Azure IoT device SDK
• Endpoint devices such as
appliances, vehicles, or
factory machines that
connect, interact and
exchange data
• 1000+ devices
• 250+ partners
• All certified to work great
with Azure IoT Hub
Edge devices
Azure IoT Edge
• Devices that aggregate,
process & provide gateway
capabilities for IoT endpoints
• Deploy and manage Azure
Services in containers on any
IoT device
• AI, AzureML, Azure Stream
Analytics and more
Edge appliances
Azure Data Box Edge
• Integrated appliances that
provide a subset of cloud
edge roles, such as ML-
inferencing
• Data Box Edge: AI-Enabled,
Storage and compute Azure
Edge appliance
• Data Box: Offline, ruggedized
data transport, 100 TB—1 PB
Edge Stack
Azure Stack
• Scalable solutions that
provide a full cloud stack,
including IaaS and PaaS
capabilities
• Edge and disconnected
scenarios
• Regulatory requirements
• Cloud app model
on-premises
Hyperscale Cloud
Edge Regions
• First-party cloud regions
• Full range Hyperscale Cloud
services
• Tiered service availability:
Heroes > hubs > satellites
• Open source based services &
tools
Most specialization
Fewest services
Fewest form factors
Most services
Sensors + control Sensors to interactive Integrated platform Global scale processing
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6. Easily and quickly transfer data to Azure
to enable further compute and analytics,
or for archival purposes
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26. Mill5 is Microsoft Gold certified partner that helps clients in diverse
industries such as medical, energy, and retail to
develop solutions in the Cloud and on the Edge.
Demo
33. Azure Machine
Learning service
Azure Machine Learning service
Build and train models anywhere
Machine Learning CLI
VS Code ML extension
Notebooks
Open source Python IDEs
Local machine
Scale out with
Azure ML compute
Scale out with Apache Spark