Cisco Introduction to Artificial Intelligence (CIAI)
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.
Course Objectives
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
Prerequisites
The knowledge and skills that the learner should have before attending this course are as follows:
- Understanding of server design and architecture
Who Should Attend
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
Course Outline
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
Lab Outline:
- Lab 1: Deep Learning Framework Setup (TensorFlow and Jupyter Stack)
- Lab 2: Classifying Handwritten Digits and TensorFlow
- Lab 3: DL Chatbot – Training a Model to have a conversation with a Google Chatbot similar to Alexa or Siri
- Lab 4: ML Training a Machine to play “The Snake Game”
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