AI Engineering Fundamentals

• An understanding of basic programming concepts
• Experience with Python programming
• Familiarity with basic statistics and linear algebra

Audience

• AI engineers
• Software developers
• Data analysts

AI Engineering is the application of artificial intelligence techniques to solve complex problems in various domains.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level AI engineers and software developers who wish to gain a foundational understanding of AI engineering principles and practices.

By the end of this training, participants will be able to:

• Understand the core concepts and technologies behind AI and machine learning.
• Implement basic machine learning models using TensorFlow and PyTorch.
• Apply AI techniques to solve practical problems in software development.
• Manage and maintain AI projects using best practices in AI engineering.
• Recognize the ethical implications and responsibilities involved in developing AI systems.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction to AI Engineering

• What is AI engineering?
• The evolution of AI and its impact on engineering
• Key concepts and terminology in AI

Core AI Technologies

• Understanding machine learning
• Deep learning and neural networks
• Natural language processing (NLP)

AI Problem Solving

• Identifying problems suitable for AI solutions
• Data collection and preprocessing
• Model selection and training

AI in Software Development

• AI tools for developers
• Integrating AI into existing systems
• Version control and model management

AI and Data Engineering

• Big data technologies and their role in AI
• Data pipelines and ETL processes
• Data storage and management for AI

Ethical AI

• Understanding bias and fairness in AI systems
• Privacy and security in AI engineering
• Ethical considerations and best practices

AI Project Management

• Agile methodologies for AI projects
• Team roles and responsibilities
• Documentation and reporting

Hands-On AI Engineering

• Setting up your AI development environment
• Building and evaluating simple AI models
• Collaborative AI engineering projects

The Future of AI Engineering

• Emerging trends in AI
• Continuous learning and skill development
• Career opportunities in AI engineering

Summary and Next Steps