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AI+ Cloud™

AT-110

Transform Cloud Computing with Cutting-Edge AI integration

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 cloudbased 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.

Certification Duration: 40 hours (5 Days)

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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

Modules

9

Examination

1

50 MCQs

90 Minutes

Passing Score

70%

Certification Modules

  1. Course Introduction Preview
  1. 1.1 Introduction to AI and Its Application
  2. 1.2 Overview of Cloud Computing and Its Benefits
  3. 1.3 Benefits and Challenges of AI-Cloud Integration
  1. 2.1 Basic Concepts and Principles of AI
  2. 2.2 Machine Learning and Its Applications
  3. 2.3 Overview of Common AI Algorithms
  4. 2.4 Introduction to Python Programming for AI
  1. 3.1 Cloud Service Models
  2. 3.2 Cloud Deployment Models
  3. 3.3 Key Cloud Providers and Offerings (AWS, Azure, Google Cloud)
  1. 4.1 Integration of AI Services in Cloud Platform
  2. 4.2 Working with Pre-built Machine Learning Models
  3. 4.3 Introduction to Cloud-based AI tools
  1. 5.1 Building and Training Machine Learning Models
  2. 5.2 Model Optimization and Evaluation
  3. 5.3 Collaborative AI Development in a Cloud Environment
  1. 6.1 Setting Up and Configuring Cloud Resources
  2. 6.2 Scalability and Performance Considerations
  3. 6.3 Data Storage and Management in the Cloud
  1. 7.1 Strategies for Deploying AI Models in the Cloud
  2. 7.2 Integration of AI Solutions with Existing Cloud-Based Applications
  3. 7.3 API Usage and Considerations
  1. 8.1 Introduction to Future Trends
  2. 8.2 AI Trends Impacting Cloud Integration
  1. 9.1 Applying AI and Cloud Concepts to Solve a Real-world Problem

Certification Modules

  1. Course Introduction Preview
  1. 1.1 Introduction to AI and Its Application
  2. 1.2 Overview of Cloud Computing and Its Benefits
  3. 1.3 Benefits and Challenges of AI-Cloud Integration
  1. 2.1 Basic Concepts and Principles of AI
  2. 2.2 Machine Learning and Its Applications
  3. 2.3 Overview of Common AI Algorithms
  4. 2.4 Introduction to Python Programming for AI
  1. 3.1 Cloud Service Models
  2. 3.2 Cloud Deployment Models
  3. 3.3 Key Cloud Providers and Offerings (AWS, Azure, Google Cloud)
  1. 4.1 Integration of AI Services in Cloud Platform
  2. 4.2 Working with Pre-built Machine Learning Models
  3. 4.3 Introduction to Cloud-based AI tools
  1. 5.1 Building and Training Machine Learning Models
  2. 5.2 Model Optimization and Evaluation
  3. 5.3 Collaborative AI Development in a Cloud Environment
  1. 6.1 Setting Up and Configuring Cloud Resources
  2. 6.2 Scalability and Performance Considerations
  3. 6.3 Data Storage and Management in the Cloud
  1. 7.1 Strategies for Deploying AI Models in the Cloud
  2. 7.2 Integration of AI Solutions with Existing Cloud-Based Applications
  3. 7.3 API Usage and Considerations
  1. 8.1 Introduction to Future Trends
  2. 8.2 AI Trends Impacting Cloud Integration
  1. 9.1 Applying AI and Cloud Concepts to Solve a Real-world Problem

Tools

TensorFlow

SHAP (SHapley Additive exPlanations)

Amazon S3

AWS SageMaker

Exam Objectives

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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.

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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.

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Problem-Solving in AI and Cloud

Participants will learn to apply AI and cloud computing concepts to real-world problems, enhancing their problem-solving skills.

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Optimization Techniques

Emphasizing AI model development and cloud deployment, learners will learn to optimize AI models and processes for performance, scalability, and cost.

Career Opportunities Post-Certification

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Median Salaries

$80,383
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With AI Skills

$1,41,310
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% Difference

76
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Solutions Executive

Strategically design and implement AI solutions using cloud services to optimize performance and scalability across various applications and industries.

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Cloud Services Developer

Integrate machine learning models and scale AI applications on cloud platforms for efficient deployment and management.

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Cloud Integration Specialists

Ensure seamless interoperability and optimal performance by integrating AI technologies with cloud services in complex environments.

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Cloud Solutions Developers

Leverage machine learning and cloud infrastructure to create customized AI solutions that enhance business operations and drive innovation.

AI & Blockchain Experts

Profiles and Credentials

Cynthia Wade

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Frequently Asked Questions

The course includes a mix of theoretical knowledge and practical applications, culminating in an interactive capstone project. This structure ensures that participants gain both conceptual understanding and hands-on experience.

This course is ideal for developers, IT professionals, and anyone with a foundational understanding of AI and cloud computing who wants to enhance their skills in integrating AI with cloud platforms like AWS, Azure, or Google Cloud.

Participants will learn to develop, deploy, and manage AI models on leading cloud platforms. Skills include optimizing AI model performance, ensuring security, meeting compliance standards, and applying AI and cloud concepts to solve real-world problems.

This certification enhances your professional profile by demonstrating proficiency in integrating AI with cloud computing. It equips you with in-demand skills, giving you a competitive edge in the job market and opening doors to lucrative career opportunities.

The certification includes an interactive capstone project where participants apply their knowledge to design and implement AI solutions within cloud environments. This project is designed to simulate real-world scenarios and challenges.

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