Development & Engineering · Self-paced
AI+ Cloud™
Master AI in the Cloud: Architecture, Deployment, and Scalable Infrastructure.
Executive summary
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.
Built for these roles
Before you start
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.
One-time price
$280
40 hours, self-paced. Lifetime access, certificate included.
Certification exam included (limited attempts).
Secure checkout via Stripe. Instant access after payment.
Curriculum
What you'll cover.
40 hours of self-paced content. Work through it in order, on your schedule.
Module 1: Fundamentals of Artificial Intelligence (AI) and 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 Platforms
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: Hands on Examples
9.1 Applying AI and Cloud Concepts to Solve a Real-world Problem
Ready to get certified?
Start today, learn at your own pace, and add a globally recognised credential to your name.
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