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Duration 14 hours
Course Outline
1. Introduction to AI Engineering
- Definition and scope of Artificial Intelligence Engineering
- Distinguishing between AI, Machine Learning, and Deep Learning
- The AI engineering lifecycle and operational workflow
- Strategic applications of AI in public sector and cross-industry operations
- Professional roles, duties, and responsibilities for AI engineers
2. Foundations of Artificial Intelligence
- Foundational AI concepts and standardized terminology
- Paradigms of learning: supervised, unsupervised, and reinforcement
- Neural network architecture and deep learning principles
- Overview of generative AI systems and foundational models
- Technical ecosystems, tools, and development frameworks for AI
3. Python for AI Engineering
- Core Python libraries essential for AI implementation
- Utilizing NumPy, Pandas, and Matplotlib for analysis
- Techniques for data manipulation and visual representation
- Utilization of Jupyter Notebooks for interactive development
- Developing modular and reusable AI code structures
4. Data Preparation for AI
- Acquisition and assessment of dataset integrity
- Data cleansing and preprocessing protocols
- Strategic feature engineering for model optimization
- Application of feature scaling and normalization techniques
- Partitioning datasets for training, validation, and testing
- Protocols for addressing missing values and statistical outliers
5. Machine Learning Fundamentals
- Implementation of regression algorithms
- Application of classification algorithms
- Techniques for clustering and segmentation
- Standardized model training workflows
- Assessment of model performance using key metrics
- Strategies to mitigate overfitting and underfitting
6. Building AI Models with TensorFlow and PyTorch
- Overview of the TensorFlow framework
- Overview of the PyTorch framework
- Construction of neural network architectures
- Processes for model training and performance validation
- Procedures for saving, loading, and persisting models
- Comparative analysis of TensorFlow and PyTorch capabilities
7. Natural Language Processing Fundamentals
- Standard procedures for text preprocessing
- Development and application of word embeddings
- Implementation of text classification systems
- Application of sentiment analysis techniques
- Introduction to transformer-based AI models
- Practical deployment of NLP solutions in operational contexts
8. AI in Software Development
- Strategic integration of AI into legacy and current applications
- Utilization of AI services via API interfaces
- Development of AI-driven application architectures
- Leveraging AI-assisted tools in the software development lifecycle
- Quality assurance and testing for AI-enabled systems
9. AI Engineering Best Practices
- Best practices for project structure and organization
- Utilization of Git for version control and collaboration
- Protocols for experiment tracking and traceability
- Standards for model versioning and management
- Compliance with documentation and reporting standards
- Ensuring reproducibility and transparency in AI projects
10. Deploying AI Models
- Techniques for model serialization and packaging
- Development of robust inference services
- Implementation of REST APIs for model access
- Application of Docker for containerized AI deployment
- Continuous monitoring of production AI models
- Maintenance strategies and update management for deployed models
11. AI Data Engineering
- Design and implementation of data pipelines
- Execution of ETL (Extract, Transform, Load) processes
- Management of structured and unstructured data sources
- Selection and management of data storage solutions
- Implementation of data quality assurance controls
- Preparation of production-grade datasets for AI consumption
12. Responsible and Ethical AI
- Mitigation of AI bias and promotion of algorithmic fairness
- Application of Explainable AI (XAI) frameworks
- Safeguards for privacy and data protection compliance
- Addressing AI security vulnerabilities and threats
- Adherence to responsible AI development standards
- Navigating regulatory requirements and governance frameworks for government
13. AI Project Management
- Comprehensive overview of the AI project lifecycle
- Application of Agile methodologies in AI initiatives
- Fostering effective collaboration between technical and business stakeholders
- Accurate estimation of AI project scope and resources
- Strategies for identifying and managing project risks
- Defining and measuring key indicators of project success
14. Hands-on AI Engineering Workshop and Future Trends
- Establishing a comprehensive AI development workflow
- Execution of an end-to-end machine learning project
- Training and evaluating models using TensorFlow or PyTorch
- Deployment of a functional AI application prototype
- Analysis of emerging trends in AI Engineering
- Integration of Generative AI and Large Language Models (LLMs)
- Adoption of MLOps and AI automation strategies
- Professional development pathways and continuous learning
- Conclusion, Q&A session, and strategic next steps
Requirements
- Foundational knowledge of basic programming concepts
- Practical experience with Python programming languages
- Working familiarity with basic statistics and linear algebra
Target Audience
- AI Engineers
- Software Developers
- Data Analysts
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.
