| Category | AI Cloud, AI Technical, All Courses, Available Now, Cloud Architect, English, Language |
|---|---|
| Program Name | AI+ Architect™ |
| Duration | Instructor-Led:5 days (live or virtual) | Self-Paced:30 hours (5 Days) |
| Prerequisites | A foundational knowledge on neural networks, including their optimization and architecture for applications. Ability to evaluate models using various performance metrics to ensure accuracy and reliability. Willingness to know about AI infrastructure and deployment processes to implement and maintain AI systems effectively. |
| Exam Format | 50 questions, 70% passing, 90 minutes, online proctored exam |
Learners will be able to develop end-to-end AI solutions, encompassing the entire workflow from data preprocessing and model building to deployment and monitoring. This includes integrating AI models into larger systems and applications, ensuring they work seamlessly within existing infrastructures.
Learners will gain hands-on experience in implementing various neural network architectures from scratch using programming frameworks like TensorFlow or PyTorch. This includes creating, training, and debugging models for different applications.
Learners will be equipped with the ability to conduct AI research, enabling them to stay at the forefront of AI developments. This includes identifying research gaps, proposing novel solutions, and critically evaluating current AI methodologies to drive innovation in the field.
Learners will explore advanced concepts in generative AI models and engage in research-based AI design. This includes developing innovative AI solutions and understanding the latest advancements in AI research, preparing them for cutting-edge applications and further research opportunities.
Course Introduction Preview
1.1 Introduction to Neural Networks
1.2 Neural Network Architecture
1.3 Hands-on: Implement a Basic Neural Network
2.1 Hyperparameter Tuning
2.2 Optimization Algorithms
2.3 Regularization Techniques
2.4 Hands-on: Hyperparameter Tuning and Optimization
3.1 Key NLP Concepts
3.2 NLP-Specific Architectures
3.3 Hands-on: Implementing an NLP Model
4.1 Key Computer Vision Concepts
4.2 Computer Vision-Specific Architectures
4.3 Hands-on: Building a Computer Vision Model
5.1 Model Evaluation Techniques
5.2 Improving Model Performance
5.3 Hands-on: Evaluating and Optimizing AI Models
6.1 Infrastructure for AI Development
6.2 Deployment Strategies
6.3 Hands-on: Deploying an AI Model
7.1 Ethical Considerations in AI
7.2 Best Practices for Responsible AI Design
7.3 Hands-on: Analyzing Ethical Considerations in AI
8.1 Overview of Generative AI Models
8.2 Generative AI Applications in Various Domains
8.3 Hands-on: Exploring Generative AI Models
9.1 AI Research Techniques
9.2 Cutting-Edge AI Design
9.3 Hands-on: Analyzing AI Research Papers
10.1 Capstone Project Presentation
10.2 Course Review and Future Directions
10.3 Hands-on: Capstone Project Development
1. Understanding AI Agents
2. Case Studies
3. Hands-On Practice with AI Agents
Specializes in designing AI models, neural networks, and intelligent systems for diverse applications, including NLP and computer vision.
Leads the integration of AI into complex systems, ensuring the deployment of scalable and efficient AI solutions across various platforms.
Designs and implements AI-powered cloud infrastructures, focusing on the seamless integration of AI models.
Engages in the development of new AI models and architectures, conducting cutting-edge research.
Focuses on the implementation and integration of AI components into existing systems, ensuring that AI-driven solutions.
The certification lasts 40 hours, typically completed over 5 days, providing an intensive learning experience.
You will learn advanced neural network techniques, model optimization, NLP and computer vision architectures, AI deployment infrastructure, and ethical AI design.
This course is ideal for AI architects, engineers, software developers, and professionals seeking to master AI architectures and neural networks.
A foundational understanding of AI and neural networks is recommended but not required, as the course starts with core concepts.
Participants will be equipped with both theoretical and practical knowledge to design, optimize, and implement AI architectures.
70%
50 multiple-choice/multiple-response questions
