AICLAHAI CertsAH-AICL1.0<ul> <li><strong>AI Model Development</strong><ul> <li>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.</li></ul></li><li><strong>Mastering cloud AI model deployment</strong><ul> <li>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.</li></ul></li><li><strong>Problem-Solving in AI and Cloud</strong><ul> <li>Participants will learn to apply AI and cloud computing concepts to real-world problems will improve problem-solving skills.</li></ul></li><li><strong>Optimization Techniques</strong><ul> <li>Emphasizing AI model development and cloud deployment, learners will learn to optimize AI models and processes for performance, scalability, and cost.</li></ul></li></ul><ul> <li>A foundational understanding of key concepts in both artificial intelligence and cloud computing</li><li>Fundamental understanding of computer science concepts like programming, data structures, and algorithms</li><li>Familiarity with cloud computing platforms like AWS, Azure, or GCP</li><li>Basic knowledge of mathematics as it important for machine learning, which is a core component of AI+ Cloud program</li></ul><h4>Module 1: Fundamentals of Artificial Intelligence (AI) in Cloud</h4><ul> <li>1.1 Introduction to AI and Its Application</li><li>1.2 Overview of Cloud Computing and Its Benefits</li><li>1.3 Benefits and Challenges of AI-Cloud Integration</li></ul><h4>Module 2: Introduction to Artificial Intelligence</h4><ul> <li>2.1 Basic Concepts and Principles of AI</li><li>2.2 Machine Learning and Its Applications</li><li>2.3 Overview of Common AI Algorithms</li><li>2.4 Introduction to Python Programming for AI</li></ul><h4>Module 3: Fundamentals of Cloud Computing</h4><ul> <li>3.1 Cloud Service Models</li><li>3.2 Cloud Deployment Models</li><li>3.3 Key Cloud Providers and Offerings (AWS, Azure, Google Cloud)</li></ul><h4>Module 4: AI Services in the Cloud</h4><ul> <li>4.1 Integration of AI Services in Cloud Platform</li><li>4.2 Working with Pre-built Machine Learning Models</li><li>4.3 Introduction to Cloud-based AI tools</li></ul><h4>Module 5: AI Model Development in the Cloud</h4><ul> <li>5.1 Building and Training Machine Learning Models</li><li>5.2 Model Optimization and Evaluation</li><li>5.3 Collaborative AI Development in a Cloud Environment</li></ul><h4>Module 6: Cloud Infrastructure for AI</h4><ul> <li>6.1 Setting Up and Configuring Cloud Resources</li><li>6.2 Scalability and Performance Considerations</li><li>6.3 Data Storage and Management in the Cloud</li></ul><h4>Module 7: Deployment and Integration</h4><ul> <li>7.1 Strategies for Deploying AI Models in the Cloud</li><li>7.2 Integration of AI Solutions with Existing Cloud-Based Applications</li><li>7.3 API Usage and Considerations</li></ul><h4>Module 8: Future Trends in AI+ Cloud Integration</h4><ul> <li>8.1 Introduction to Future Trends</li><li>8.2 AI Trends Impacting Cloud Integration</li></ul><h4>Module 9: Capstone Project</h4><ul> <li>9.1 Exercise 1: Diabetes Prediction Using Machine Learning</li><li>9.2 Exercise 2: Building &amp; Deploying an Image Classification Web App with GCP AutoML Vision Edge, Tensorflow.js &amp; GCP App Engine</li><li>9.3 Exercise 3: How to deploy your own ML model to GCP in 5 simple steps.</li><li>9.4 Exercise 4: Google Cloud Platform Custom Model Upload , REST API Inference and Model Version Monitoring</li><li>9.5 Exercise 5: Deploy Machine Learning Model in Google Cloud Platform Using Flask</li></ul>- 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.- 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 programModule 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 Flask5 Tage3450.003450.003450.003995.003450.005515.003450.00