- modules: 9
- Examination: 1
- Examination Time: 50 MCQs, 90 Minutes
- Passing Score: 70%
About Certification
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.
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
Certification Modules
- 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 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 Flask
Certification outcome
Upon successful completion of the AI+ Cloud certification, participants will adeptly merge cloud computing and artificial intelligence. They'll gain hands-on experience in developing, deploying, and managing machine learning tasks across leading cloud platforms. Mastery includes optimizing AI model performance, enforcing security protocols, and meeting compliance standards. This course aims to furnish you with skills essential for coveted certification, elevating your career profile. Demonstrating proficiency at the intersection of cloud computing and AI, this certification underscores your ability to deliver practical solutions, affirming expertise in seamlessly integrating these innovative technologies.