Course Overview
The AI+ Cloud™ certification program targets developers and IT professionals aspiring to excel in cloud computing integrated with artificial intelligence. The curriculum offers an in-depth exploration of AI and cloud computing, encompassing advanced cloud infrastructure and AI model deployment. Participants gain practical insights into cloud-based AI applications, culminating in an interactive capstone project. With these skills, graduates are primed to navigate the dynamic AI+ Cloud™ integration landscape, equipped to design and implement AI solutions seamlessly within cloud environments for sustained success.
Prerequisites
- 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
Course Objectives
- AI Model Development
- Students learn to construct, train, and optimize machine learning models utilizing cloud-based tools and services. This involves learning to choose methods, preprocess data, and optimize models.
- Mastering cloud AI model deployment
- Learners will master cloud AI model deployment and integration into existing systems and workflows. Learn deployment pipelines, version control, and CI/CD procedures to seamlessly integrate AI solutions into production environments.
- Problem-Solving in AI and Cloud
- Participants will learn to apply AI and cloud computing concepts to real-world problems will improve problem-solving skills.
- Optimization Techniques
- Emphasizing AI model development and cloud deployment, learners will learn to optimize AI models and processes for performance, scalability, and cost.
Outline: AI+ Cloud (AICL)
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