Cisco Introduction to Artificial Intelligence (CIAI)

 

Résumé du cours

In this 2-day course, Cisco Introduction to Artificial Intelligence (CIAI) v1.0, we will introduce the learner to the Artificial Intelligence, Machine Learning, and Deep Learning essentials in addition to compute platforms such as Cisco UCS, through a combination of lecture and hands-on labs. Artificial Intelligence (AI) and Machine Learning (ML) are opening up new ways for enterprises to solve complex problems, but they will also have a profound effect on the underlying infrastructure and processes of IT. AI/ML is a dominant trend in the enterprise with the ubiquity of large amounts of observed data, the rise of distributed computing frameworks and the prevalence of large hardware-accelerated computing infrastructure became essential.

Moyens Pédagogiques :
  • Réalisation de la formation par un formateur agréé par l’éditeur
  • Formation réalisable en présentiel ou en distanciel
  • Mise à disposition de labs distants/plateforme de lab pour chacun des participants (si applicable à la formation)
  • Distribution de supports de cours officiels en langue anglaise pour chacun des participants
    • Il est nécessaire d'avoir une connaissance de l'anglais technique écrit pour la compréhension des supports de cours
  • Accessibilité aux Personnes en Situation de Handicap – nous contacter
Moyens d'évaluation :
  • Évaluations formatives pendant la formation, à travers les travaux pratiques réalisés sur les labs à l’issue de chaque module
  • Évaluation sous forme de questionnaire à l’issue de la formation

A qui s'adresse cette formation

The primary audience for this course is as follows:

  • Cisco Integrators/Partners
  • Consulting Systems Engineers
  • Technical Solutions Architects
  • Data Center network professionals (including designers, Administrators, and Engineers), and anyone interested in AI/ML/DL

Pré-requis

The knowledge and skills that the learner should have before attending this course are as follows:

  • Understanding of server design and architecture

Objectifs

Upon completing this course, the learner will be able to meet these overall objectives:

  • Understanding Big Data and Data Science concepts
  • List and describe the concepts, major features, algorithms, and benefits of AI/ML/DL
  • Use AI/ML/DL techniques, such as Neural Networks
  • Get familiar with Data Science and Infrastructure AI Tools and software
  • Describe the Cisco AI/ML/DL Computing Solutions Portfolio alignments

Contenu

Data and AI/ML/DL Fundamentals

  • Introduction to Big Data
  • Introduction to Data Science
  • Introduction to Data Engineering
  • Introduction to Artificial Intelligence (AI)
  • Introduction to Machine Learning (ML)
  • Introduction to Deep Learning (DL)
  • AI/ML/DL Use Cases

Artificial Intelligence (AI)

  • AI Concept, Methods, and Techniques
  • Key AI Challenges (Customer and Provider)
  • AI Business Drives
  • Evolution of AI Algorithms

Machine Learning (ML)

  • Machine Learning (ML) Algorithms
  • Supervised Learning
  • Unsupervised Learning

Deep Learning (DL)

  • Deep Learning Project Phases
  • Custom AI Deep Learning Workflow
  • Deep Learning (DL) Algorithms

Neural Networks

  • Neural Networks Fundamentals
  • Neural Architecture Search (NAS)
  • Cisco Neural Architecture Construction (NAC)

NLP / NLU

  • Natural Language Processing Basics
  • NLP / NLU Techniques
  • NLP / NLU Deployments

Kubernetes

  • What is Kubernetes
  • Introduction to Containers
  • Container Orchestration Engines
  • Kubernetes Basics
  • KubeFlow for AI

AI Server Requirements

  • GPU
  • Modern GPU Server Architecture
  • Storage Requirements

Data Science and Infrastructure AI Tools

  • Big Data with AI/ML/DL
  • Kubeflow
  • SkyMind SKIL
  • Cloudera Data Science Workbench
  • DL Frameworks > Handwritten Math
  • Kubernetes
  • Demo: Classifying Handwritten Digits with TensorFlow

Prix & Delivery methods

Formation en ligne

Durée 2 jours

Prix (Hors Taxe)
  • 2 250,– €

Actuellement aucune session planifiée