AI+ Cloud™
- Description
- Curriculum
- FAQ
- Reviews
Description
AT-110
30 hours (5 Days)
50 MCQs, 90 Minutes
70% (35/50)
About Certification
- Cloud-AI Fusion: Learn to integrate AI into scalable cloud environments
- Advanced Infrastructure: Master CI/CD, cloud AI models, and deployment strategies
- Capstone Project: Gain hands-on experience with real-world applications
- Future-Ready Skills: Prepares professionals to lead AI-powered cloud innovation
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.
What Will You Learn?
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: You will learn to apply AI and cloud computing concepts to real-world problems, enhancing their 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.
Industry Opportunities
Cloud AI Integration Specialist: Focuses on integrating AI tools into cloud systems, optimizing cloud performance, scalability, and security.
AI Cloud Architect: Designs AI-powered cloud infrastructure, creating scalable, efficient, and secure cloud environments for organizations.
Cloud Automation Expert: Implements AI-driven automation tools for managing cloud infrastructure, reducing manual intervention and improving operational efficiency.
AI Cloud Data Scientist: Uses AI algorithms and data analytics to analyze cloud-based data, providing insights for better decision-making and resource management.
Cloud Security AI Specialist: AI technologies are applied to enhance cloud security, detecting anomalies, predicting threats, and ensuring robust protection of cloud.
At a Glance Overview
| Program Name | AI+ Cloud™ |
|---|---|
| Included | Instructor-led OR Self-paced course + Official exam + Digital badge |
| Duration |
|
| Prerequisites | Key concepts in both AI, Fundamental understanding of computer science, Familiarity with cloud computing platforms like AWS, Azure, or GCP |
| Exam Format | 50 questions, 70% passing, 90 minutes, online proctored exam |
| Delivery | Online labs, projects, case studies |
| Outcome | Industry-recognized credential + hands-on experience |
Exam Blueprint
| Module | Line Item | Percentage |
|---|---|---|
| Fundamentals of Artificial Intelligence (AI) and Cloud | Fundamentals of Artificial Intelligence (AI) and Cloud – 5% | 5% |
| Introduction to Artificial Intelligence | Introduction to Artificial Intelligence – 7% | 7% |
| Fundamentals of Cloud Computing | Fundamentals of Cloud Computing – 8% | 8% |
| AI Services in the Cloud | AI Services in the Cloud – 10% | 10% |
| AI Model Development in the Cloud | AI Model Development in the Cloud- 15% | 15% |
| Cloud Infrastructure for AI | Cloud Infrastructure for AI – 15% | 15% |
| Deployment and Integration | Deployment and Integration – 15% | 15% |
| Future Trends in AI + Cloud Integration | Future Trends in AI + Cloud Integration – 20% | 20% |
| Capstone | Capstone – 5% | 5% |
Salary Insights
| Median Salary | $80,383 |
|---|---|
| With AI Skills | $141,310 |
| Difference (%) | 76 |
| Role Based Course Hours |
