Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
1. Introduction to AI Engineering
- Defining AI Engineering
- Distinguishing between AI, Machine Learning, and Deep Learning
- The AI engineering lifecycle
- Industry applications of AI
- Roles and responsibilities of an AI engineer
2. Foundations of Artificial Intelligence
- Key AI concepts and terminology
- Supervised, unsupervised, and reinforcement learning approaches
- Basics 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
- NumPy, Pandas, and Matplotlib
- Data manipulation and visualization techniques
- Working effectively with Jupyter Notebooks
- Writing modular and reusable AI code
4. Data Preparation for AI
- Collecting and comprehending datasets
- Data cleaning and preprocessing steps
- Feature engineering strategies
- Feature scaling and normalization
- Splitting datasets into training, validation, and test sets
- Managing missing values and outliers
5. Machine Learning Fundamentals
- Regression algorithms
- Classification algorithms
- Clustering techniques
- Model training workflow
- Evaluating model performance metrics
- Mitigating overfitting and underfitting
6. Building AI Models with TensorFlow and PyTorch
- Introduction to TensorFlow
- Introduction to PyTorch
- Creating neural networks
- Training and validating models
- Saving and loading models
- Comparing both frameworks
7. Natural Language Processing Fundamentals
- Text preprocessing techniques
- Word embeddings
- Text classification methods
- Sentiment analysis
- Introduction to transformer models
- Practical NLP applications
8. AI in Software Development
- Integrating AI into existing applications
- Leveraging AI services via APIs
- Developing AI-powered applications
- AI-assisted software development tools
- Testing AI-enabled applications
9. AI Engineering Best Practices
- Project organization structures
- Version control using Git
- Experiment tracking
- Model versioning
- Documentation standards
- Ensuring reproducibility in AI projects
10. Deploying AI Models
- Model serialization techniques
- 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
- Addressing AI bias and fairness
- Explainable AI (XAI)
- Privacy and data protection measures
- AI security considerations
- Responsible AI development practices
- Regulatory and governance considerations
13. AI Project Management
- The AI project lifecycle
- Agile methodologies for AI projects
- Fostering 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 comprehensive 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
- Familiarity with fundamental programming concepts
- Practical experience with Python programming
- Understanding of basic statistics and linear algebra
Target 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.
