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Introducción a Microsoft Azure 2023

Introducción a Microsoft Azure e Inteligencia Artificial. Visión general de Servicios sobre Microsoft Azure e Inteligencia Artificial (AI)

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Microsoft Azure
Visión General
Mariano Kovo
MVP Data Platform
mkovo@Outlook.com
marianok@precisionit.com.ar
@Mariano_Kovo
• SQL Argentina Local Group Founder
• South Florida SQL Server User Group
• Technical Speaker
• SQL Saturdays
• Microsoft Most Valuable Professional
@Mariano_Kovo /marianokovo
marianok@precisionit.com.ar https://precisionit.com.ar/blogs/
marianokovo
Mariano Kovo
CEO & Co-Founder PrecisionIT
Microsoft MVP Data Platform
Computación en la Nube
Azure es Global
Que es Computación en la Nube?
Azure Infraestructura
Física
Geography
•
•
•
Region
•
•
Availability Zones
•
•
•
•
•
Geography
Region 2
Region 1
Availability
Zone 1
Availability
Zone 2
Availability
Zone 3
Data residency boundary
Introducción a Microsoft Azure 2023

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Introducción a Microsoft Azure 2023

  • 1. Microsoft Azure Visión General Mariano Kovo MVP Data Platform mkovo@Outlook.com marianok@precisionit.com.ar @Mariano_Kovo
  • 2. • SQL Argentina Local Group Founder • South Florida SQL Server User Group • Technical Speaker • SQL Saturdays • Microsoft Most Valuable Professional @Mariano_Kovo /marianokovo marianok@precisionit.com.ar https://precisionit.com.ar/blogs/ marianokovo Mariano Kovo CEO & Co-Founder PrecisionIT Microsoft MVP Data Platform
  • 3. Computación en la Nube Azure es Global
  • 4. Que es Computación en la Nube?
  • 5. Azure Infraestructura Física Geography • • • Region • • Availability Zones • • • • • Geography Region 2 Region 1 Availability Zone 1 Availability Zone 2 Availability Zone 3 Data residency boundary
  • 13. Dublin DB3,4,5 DUB 07 DUB 06 DUB 08 Future Campus works
  • 15. 3x Beast Gen 2 Processor 2 x 6 Core 2.1 GHz Memory 32 GiB Hard Drive 6 x 500 GB SSD None NIC 1 Gb/s Godzilla Processor 2 x 16 Core 2.0 GHz Memory 512 GiB Hard Drive None SSD 9 x 800 GB NIC 40 Gb/s Beast Processor 4 x 18 Core 2.5 GHz Memory 4096 GiB Hard Drive None SSD 4 x 2 TB NVMe, 1 x 960 GB SATA NIC 40 Gb/s Gen 6 Processor 2 x Skylake 24 Core 2.7GHz Memory 768GiB DDR4 Hard Drive None SSD 4 x 960 GB M.2 SSDs and 1 x 960 GB SATA NIC 40 Gb/s + FPGA Beast v2 Processor 8 x 28 Core 2.5 GHz Memory 12 TiB Hard Drive None SSD 4 x 2 TB NVMe, 1 x 960 GB SATA NIC 50 Gb/s Optimized Gen 7 Processor 2 x 26 core Cascade Lake Memory 192 GB DDR4 Hard Drive None SSD 5 x 960 GB M.2 NVMe NIC 50 Gb/s + FPGA Optimized Gen 6 Processor 2 x 24 core Skylake Lake Memory 192 GB DDR4 Hard Drive None SSD 4 x 960 GB M.2 NVMe NIC 40 Gb/s + FPGA Azure servers: General purpose 448 Cores 24TB RAM Mega Godzilla Beast Processor 16 x 28 Core 2.5 GHz Memory 24 TiB Hard Drive None SSD 4 x 960 GB SSD, 4 x 6.4 TiB PCIe NIC 50 Gb/s
  • 16. Liquid Cooling CPU trends GPU trends Nvidia V100 300W MSFT G50 Expansion 4kW Nvidia DGX-2, 10kW
  • 17. Liquid Cooling : : Olympus Micro- channel Cold Plates One phase immersion Air Cooled Olympus Two phase immersion
  • 19. Azure Networking overview • Software WAN • Subsea Cables • Terrestrial Fiber • National Clouds WAN backbone • SmartNIC/FPGA • SONiC DC hardware • Virtual Networks • Load Balancing • VPN Services • Firewall • DDoS Protection • DNS and Traffic Management Services Azure region ‘B’ Azure region ‘A’ • Internet Peering • ExpressRoute Edge and ExpressRoute • Acceleration for applications and content CDN CDN Edge ExpressRoute Microsoft WAN • DC Networks • Regional Networks • Optical Modules Intra-region Regional network Regional network Regional network Regional network Internet exchanges Carrier Cable • E2E monitoring (Network Watcher, Network Performance Monitoring) Last mile Consumers Enterprise, SMB, mobile Enterprise DC/ Corpnet
  • 20. United States United States Canada Mexico Venezuela Colombia Peru Bolivia Brazil Argentina Atlanta Ocean Algeria Mali Niger Nigeria Chad Libya Egypt Sudan Ethiopia Dr Congo Angola Zambia Nambia South Africa Greenland Svalbard Sweden Norway United Kingdom France Poland Ukraine Turkey Saudi Arabia Iran Kazakistan India Russia China Myanmar (Burma) Indian Ocean Indonesia Australia Pacific Ocean Pacific Ocean Data center Owned capacity Future capacity Leased capacity Edge site Azure inter-DC dark fiber backbone
  • 22. Evolución de las arquitecturas •Arquitecturas tradicionales •Arquitecturas Monolíticas •Arquitecturas distribuidas “Microservicios”
  • 23. Pago solo por lo utilizado No se tienen costos ocultos. No se requiere comprar servidores o licencias adicionales. Granularidad de pago por minuto. Créditos y descuentos de programas como MSDN (Desarrollo/Pruebas).
  • 27. Demo!
  • 32. Investigaciones recientes sobre IA 2010 - ImageNet : Banco de datos públicos con Imágenes (Etiquetadas)
  • 33. Investigacion es recientes sobre IA Año Paridad Humana 2015 Clasificación de Imágenes 2016 Reconocimiento de Voz Conversacional 2018 Traducción Automatica (Chino-a- Ingles) 2020 Etiquetado de Imágenes
  • 34. IA
  • 35. Bots
  • 36. Demo!
  • 38. Gracias! Links & Material • Portal Azure : https://portal.azure.com • Calculadora de Recursos: https://azure.microsoft.com/es-es/pricing/calculator/ • AI Demos: https://aidemos.microsoft.com/ • Presentación : https://precisionit.com.ar/blogs/marianokovo/presentacion- introduccion-a-microsoft-azure-2023/
  • 39. Microsoft Azure Visión General Mariano Kovo MVP Data Platform mkovo@Outlook.com marianok@precisionit.com.ar @Mariano_Kovo

Editor's Notes

  1. Inicio de Azure en 2008 Private Preview Lanzado en 2010 como Windows Azure Renombrado a Microsoft Azure en 2014
  2. Potencia de Diseño Térmica Vs Temperatura
  3. Client/Servidor Un Solo servidor con todos los components APIs / Cloud Functions
  4. Preview Version 1.7 (2017-04-20)
  5. Originally, computers were invented by Charles Babbage to operate on numbers following a well-defined procedure - an algorithm. Modern computers, even though significantly more advanced than the original model proposed in the 19th century, still follow the same idea of controlled computations. Thus it is possible to program a computer to do something if we know the exact sequence of steps that we need to do in order to achieve the goal. Weak AI vs. Strong AI The task of solving a specific human-like problem, such as determining a person's age from a photo, can be called Weak AI, because we are creating a system for only one task, and not a system that can solve many tasks, such as can be done by a human being. Of course, developing a generally intelligent computer system is also extremely interesting from many points of view, including for students of the philosophy of consciousness. Such system would be called Strong AI, or Artificial General Intelligence (AGI).
  6. Top-down Approach (Razonamiento simbólico) Un enfoque de arriba hacia abajo modela la forma en que una persona razona para resolver un problema. Implica extraer conocimiento de un ser humano y representarlo en una forma legible por computadora. También necesitamos desarrollar una forma de modelar el razonamiento dentro de una computadora. Bottom-up Approach (Redes neuronales) Un enfoque de abajo hacia arriba modela la estructura de un cerebro humano, que consiste en una gran cantidad de unidades simples llamadas neuronas. Cada neurona actúa como un promedio ponderado de sus entradas, y podemos entrenar una red de neuronas para resolver problemas útiles proporcionando datos de entrenamiento.
  7. Un gran crecimiento en la investigación de redes neuronales comenzó alrededor de 2010, cuando grandes conjuntos de datos públicos estuvieron disponibles. Una enorme colección de imágenes llamada ImageNet, que contiene alrededor de 14 millones de imágenes anotadas, dio origen al ImageNet Large Scale Visual Recognition Challenge.
  8. In 2012, Convolutional Neural Networks were first used in image classification, which led to a significant drop in classification errors (from almost 30% to 16.4%). In 2015, ResNet architecture from Microsoft Research achieved human-level accuracy.