AI+ Cloud™
AI+ Cloud™
Transform Cloud Computing with Cutting-Edge AI integration
- 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.
- Course Introduction Preview
- 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
- 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
- 3.1 Cloud Service Models
- 3.2 Cloud Deployment Models
- 3.3 Key Cloud Providers and Offerings (AWS, Azure, Google 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
- 5.1 Building and Training Machine Learning Models
- 5.2 Model Optimization and Evaluation
- 5.3 Collaborative AI Development in a Cloud Environment
- 6.1 Setting Up and Configuring Cloud Resources
- 6.2 Scalability and Performance Considerations
- 6.3 Data Storage and Management in the Cloud
- 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
- 8.1 Introduction to Future Trends
- 8.2 AI Trends Impacting Cloud Integration
- 9.1 Applying AI and Cloud Concepts to Solve a Real-world Problem
- 1. Understanding AI Agents
- 2. Case Studies
- 3. Hands-On Practice with AI Agents
TensorFlow
SHAP (SHapley Additive exPlanations)
Amazon S3
AWS SageMaker
Passing Score : 70% (35/50)
Number of Multiple Choice Question : 50 MCQs, 90 Minutes
Number of exams required for certification : 1
Exam Blueprint
- Fundamentals of Artificial Intelligence (AI) and Cloud – 5%
- Introduction to Artificial Intelligence – 7%
- Fundamentals of Cloud Computing – 8%
- AI Services in the Cloud – 10%
- AI Model Development in the Cloud- 15%
- Cloud Infrastructure for AI – 15%
- Deployment and Integration – 15%
- Future Trends in AI + Cloud Integration – 20%
- Capstone – 5%
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.
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.
FAQ
How is the course structured?
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.
Who should enroll in this certification?
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.
What practical skills will I gain from this course?
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.
How does this certification benefit my career?
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.
What kind of projects will I work on during the course?
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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