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Course Outline
1. Introduction to AI Engineering
- Defining AI Engineering
- Distinguishing between AI, Machine Learning, and Deep Learning
- The AI engineering lifecycle
- Cross-industry applications of AI
- The roles and duties of an AI engineer
2. Foundations of Artificial Intelligence
- Essential AI concepts and terminology
- Supervised, unsupervised, and reinforcement learning
- Basics of neural networks and deep learning
- An overview of generative AI and foundation models
- AI development ecosystems and frameworks
3. Python for AI Engineering
- Key Python libraries for AI
- NumPy, Pandas, and Matplotlib
- Data manipulation and visualization techniques
- Utilizing Jupyter Notebooks
- Writing reusable code for AI applications
4. Data Preparation for AI
- Collecting and interpreting datasets
- Data cleaning and preprocessing methods
- Feature engineering
- Feature scaling and normalization
- Dividing datasets into training, validation, and test sets
- Addressing missing values and outliers
5. Machine Learning Fundamentals
- Regression algorithms
- Classification algorithms
- Clustering methods
- The model training workflow
- Metrics for model evaluation
- Mitigating overfitting and underfitting
6. Building AI Models with TensorFlow and PyTorch
- Getting started with TensorFlow
- Getting started with PyTorch
- Constructing neural networks
- Training and validating models
- Saving and loading model files
- Comparison of TensorFlow and PyTorch frameworks
7. Natural Language Processing Fundamentals
- Text preprocessing
- Word embeddings
- Text classification
- Sentiment analysis
- Introductory concepts of transformer models
- Practical applications of NLP
8. AI in Software Development
- Integrating AI into existing applications
- Interacting with AI services via APIs
- Developing applications powered by AI
- Using AI-assisted tools in software development
- Testing applications with AI capabilities
9. AI Engineering Best Practices
- Organizing AI projects
- Version control using Git
- Tracking experiments
- Managing model versions
- Documentation standards
- Ensuring reproducibility in AI projects
10. Deploying AI Models
- Model serialization
- Creating inference services
- Implementing REST APIs for AI models
- Using Docker for AI deployment
- Monitoring live models
- Maintaining and updating models
11. AI Data Engineering
- Data pipelines
- ETL processes
- Managing structured and unstructured data
- Data storage solutions
- Data quality management
- Preparing datasets for production
12. Responsible and Ethical AI
- AI bias and fairness
- Explainable AI (XAI)
- Privacy and data protection
- AI security aspects
- Principles of responsible AI development
- Regulatory and governance factors
13. AI Project Management
- The AI project lifecycle
- Applying Agile methodologies to AI
- Collaboration between technical and business teams
- Estimation techniques for AI projects
- Risk management
- Defining metrics for project success
14. Hands-on AI Engineering Workshop and Future Trends
- Establishing a complete AI development workflow
- Creating an end-to-end machine learning project
- Training and evaluating models with TensorFlow or PyTorch
- Deploying a basic AI application
- Emerging trends in AI Engineering
- Generative AI and Large Language Models (LLMs)
- MLOps and AI automation
- Career trajectories and continuous learning
- Wrap-up, Q&A, and subsequent steps
Requirements
- A solid understanding of fundamental programming concepts
- Proficiency in Python programming
- Basic familiarity with statistics and linear algebra
Intended Audience
- AI engineers
- Software developers
- Data analysts
14 Hours
Testimonials (2)
The extensive selection of tools presented
Miruna Buzduga - Aeronamic Eastern Europe
Course - AI Enablement Training for Engineers
Step by step training with a lot of exercises. It was like a workshop and I am very glad about that.
