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Course Outline
1. Introduction to AI Engineering
- What is AI Engineering?
- Distinguishing AI from Machine Learning and Deep Learning
- The AI engineering lifecycle
- AI applications across diverse industries
- Roles and responsibilities of an AI engineer
2. Foundations of Artificial Intelligence
- Core AI concepts and terminology
- Supervised, unsupervised, and reinforcement learning
- 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
- NumPy, Pandas, and Matplotlib
- Data manipulation and visualization techniques
- Working with Jupyter Notebooks
- Writing reusable AI code
4. Data Preparation for AI
- Collecting and analyzing datasets
- Data cleaning and preprocessing steps
- Feature engineering strategies
- 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
- Metrics for model evaluation
- 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 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
- Effective project organization
- Version control using Git
- Experiment tracking methodologies
- Model versioning techniques
- Adhering to documentation standards
- Ensuring reproducibility in AI projects
10. Deploying AI Models
- Model serialization processes
- Building inference services
- REST APIs for AI models
- Introduction to Docker for AI deployment
- Monitoring deployed models
- Updating and maintaining models
11. AI Data Engineering
- Data pipelines architecture
- ETL processes
- Managing structured and unstructured data
- Data storage options
- Data quality management strategies
- Preparing production-ready datasets
12. Responsible and Ethical AI
- Addressing AI bias and fairness
- Explainable AI (XAI) principles
- Privacy and data protection measures
- Security considerations in AI
- Principles of responsible AI development
- Regulatory and governance aspects
13. AI Project Management
- The AI project lifecycle
- Agile methodologies for AI projects
- Collaboration between technical and business teams
- Estimating AI projects
- Risk management strategies
- Measuring project success metrics
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 opportunities
- Summary, Q&A session, and next steps
Requirements
- Familiarity with basic programming concepts
- Hands-on experience with Python programming
- Knowledge of fundamental 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.
