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Course Outline
1. Introduction to AI Engineering
- Defining AI Engineering
- Differentiating AI, Machine Learning, and Deep Learning
- The AI engineering lifecycle
- Applications of AI across various industries
- Roles and responsibilities of an AI engineer
2. Foundations of Artificial Intelligence
- Core AI concepts and key terminology
- Supervised, unsupervised, and reinforcement learning paradigms
- Fundamentals of neural networks and deep learning
- Overview of generative AI and foundation models
- Ecosystems and frameworks for AI development
3. Python for AI Engineering
- Essential Python libraries for AI applications
- Using NumPy, Pandas, and Matplotlib
- Data manipulation and visualization techniques
- Working effectively with Jupyter Notebooks
- Writing reusable and modular AI code
4. Data Preparation for AI
- Collecting and understanding datasets
- Data cleaning and preprocessing strategies
- Techniques for feature engineering
- Feature scaling and normalization
- Dividing datasets into training, validation, and test sets
- Handling missing values and outliers
5. Machine Learning Fundamentals
- Regression algorithms
- Classification algorithms
- Clustering techniques
- The model training workflow
- Evaluating model performance metrics
- Strategies to prevent overfitting and underfitting
6. Building AI Models with TensorFlow and PyTorch
- Introduction to TensorFlow
- Introduction to PyTorch
- Constructing neural networks
- Model training and validation processes
- Saving and loading trained models
- Comparing TensorFlow and PyTorch frameworks
7. Natural Language Processing Fundamentals
- Text preprocessing methods
- Understanding word embeddings
- Text classification tasks
- Sentiment analysis
- Introduction to transformer models
- Practical applications in NLP
8. AI in Software Development
- Integrating AI capabilities into existing applications
- Interfacing with AI services via APIs
- Developing AI-powered applications
- Leveraging AI-assisted software development tools
- Testing strategies for AI-enabled applications
9. AI Engineering Best Practices
- Effective project organization
- Version control using Git
- Tracking experiments
- Model versioning strategies
- Documentation standards
- Ensuring reproducibility in AI projects
10. Deploying AI Models
- Model serialization techniques
- Building inference services
- Developing REST APIs for AI models
- Introduction to Docker for AI deployment
- Monitoring deployed models in production
- Maintenance and updates for model versions
11. AI Data Engineering
- Designing data pipelines
- Understanding ETL processes
- Managing structured and unstructured data
- Options for data storage
- Data quality management
- Preparing production-ready datasets
12. Responsible and Ethical AI
- Addressing AI bias and ensuring fairness
- Explainable AI (XAI)
- Privacy and data protection measures
- Security considerations in AI
- Principles of responsible AI development
- Regulatory and governance frameworks
13. AI Project Management
- The AI project lifecycle
- Applying agile methodologies to AI projects
- Fostering collaboration between technical and business teams
- Estimating effort for AI projects
- Risk management strategies
- Measuring project success
14. Hands-on AI Engineering Workshop and Future Trends
- Setting up a comprehensive AI development workflow
- Constructing an end-to-end machine learning project
- Training and evaluating models using TensorFlow or PyTorch
- Deploying a simple AI application
- Current trends in AI Engineering
- Generative AI and Large Language Models (LLMs)
- MLOps and automation in AI
- Career paths and continuous learning opportunities
- Summary, Q&A, and next steps
Requirements
- Familiarity with basic programming concepts
- Practical experience with Python programming
- Knowledge of fundamental statistics and linear algebra
Target Audience
- AI engineers
- Software developers
- Data analysts
14 Hours
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 2600 € + VAT*
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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.
