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Course Outline
1. Introduction to AI Engineering
- What is AI Engineering?
- AI vs. Machine Learning vs. Deep Learning
- AI engineering lifecycle
- Applications of AI across industries
- Roles and responsibilities of an AI engineer
2. Foundations of Artificial Intelligence
- Core AI concepts and terminology
- Supervised, unsupervised, and reinforcement learning
- Neural networks and deep learning fundamentals
- Overview of generative AI and foundation models
- AI development ecosystems and frameworks
3. Python for AI Engineering
- Essential Python libraries for AI
- NumPy, Pandas, and Matplotlib
- Data manipulation and visualization
- Working with Jupyter Notebooks
- Writing reusable AI code
4. Data Preparation for AI
- Collecting and understanding datasets
- Data cleaning and preprocessing
- Feature engineering
- Feature scaling and normalization
- Splitting datasets into training, validation, and test sets
- Handling missing values and outliers
5. Machine Learning Fundamentals
- Regression algorithms
- Classification algorithms
- Clustering techniques
- Model training workflow
- Model evaluation metrics
- Preventing overfitting and underfitting
6. Building AI Models with TensorFlow and PyTorch
- Introduction to TensorFlow
- Introduction to PyTorch
- Creating neural networks
- Model training and validation
- Saving and loading models
- Comparing both frameworks
7. Natural Language Processing Fundamentals
- Text preprocessing
- Word embeddings
- Text classification
- Sentiment analysis
- Introduction to transformer models
- Practical NLP applications
8. AI in Software Development
- Integrating AI into existing applications
- Calling AI services through APIs
- Developing AI-powered applications
- AI-assisted software development tools
- Testing AI-enabled applications
9. AI Engineering Best Practices
- Project organization
- Version control with Git
- Experiment tracking
- Model versioning
- Documentation standards
- Reproducibility in AI projects
10. Deploying AI Models
- Model serialization
- Building inference services
- REST APIs for AI models
- Introduction to Docker for AI deployment
- Monitoring deployed models
- Model maintenance and updates
11. AI Data Engineering
- Data pipelines
- ETL processes
- Managing structured and unstructured data
- Data storage options
- Data quality management
- Preparing production-ready datasets
12. Responsible and Ethical AI
- AI bias and fairness
- Explainable AI (XAI)
- Privacy and data protection
- AI security considerations
- Responsible AI development
- Regulatory and governance considerations
13. AI Project Management
- AI project lifecycle
- Agile methodologies for AI projects
- Collaboration between technical and business teams
- Estimating AI projects
- Managing risks
- Measuring project success
14. Hands-on AI Engineering Workshop and Future Trends
- Setting up a complete AI development workflow
- Building an end-to-end machine learning project
- Training and evaluating a model using TensorFlow or PyTorch
- Deploying a simple AI application
- Current trends in AI Engineering
- Generative AI and Large Language Models (LLMs)
- MLOps and AI automation
- Career paths and continuous learning
- Summary, Q&A, and next steps
Requirements
- An understanding of basic programming concepts
- Experience with Python programming
- Familiarity with basic statistics and linear algebra
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.
