AI+ Cloud (AICL) Online

Voraussetzungen

• A foundational understanding of key concepts in both artificial intelligence and cloud computing
• Fundamental understanding of computer science concepts like programming, data structures, and algorithms
• Familiarity with cloud computing platforms like AWS, Azure, or GCP
• Basic knowledge of mathematics as it important for machine learning, which is a core component of AI+ Cloud program

Detaillierter Kursinhalt

Module 1: Fundamentals of Artificial Intelligence (AI) in Cloud

• 1.1 Introduction to AI and Its Application
• 1.2 Overview of Cloud Computing and Its Benefits
• 1.3 Benefits and Challenges of AI-Cloud Integration

Module 2: Introduction to Artificial Intelligence

• 2.1 Basic Concepts and Principles of AI
• 2.2 Machine Learning and Its Applications
• 2.3 Overview of Common AI Algorithms
• 2.4 Introduction to Python Programming for AI

Module 3: Fundamentals of Cloud Computing

• 3.1 Cloud Service Models
• 3.2 Cloud Deployment Models
• 3.3 Key Cloud Providers and Offerings (AWS, Azure, Google Cloud)

Module 4: AI Services in the Cloud

• 4.1 Integration of AI Services in Cloud Platform
• 4.2 Working with Pre-built Machine Learning Models
• 4.3 Introduction to Cloud-based AI tools

Module 5: AI Model Development in the Cloud

• 5.1 Building and Training Machine Learning Models
• 5.2 Model Optimization and Evaluation
• 5.3 Collaborative AI Development in a Cloud Environment

Module 6: Cloud Infrastructure for AI

• 6.1 Setting Up and Configuring Cloud Resources
• 6.2 Scalability and Performance Considerations
• 6.3 Data Storage and Management in the Cloud

Module 7: Deployment and Integration

• 7.1 Strategies for Deploying AI Models in the Cloud
• 7.2 Integration of AI Solutions with Existing Cloud-Based Applications
• 7.3 API Usage and Considerations

Module 8: Future Trends in AI+ Cloud Integration

• 8.1 Introduction to Future Trends
• 8.2 AI Trends Impacting Cloud Integration

Module 9: Capstone Project

• 9.1 Exercise 1: Diabetes Prediction Using Machine Learning
• 9.2 Exercise 2: Building & Deploying an Image Classification Web App with GCP AutoML Vision Edge, Tensorflow.js & GCP App Engine
• 9.3 Exercise 3: How to deploy your own ML model to GCP in 5 simple steps.
• 9.4 Exercise 4: Google Cloud Platform Custom Model Upload , REST API Inference and Model Version Monitoring
• 9.5 Exercise 5: Deploy Machine Learning Model in Google Cloud Platform Using Flask