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Course Outline
1. Introduction to AI Engineering
- Defining AI Engineering
- Distinguishing AI, Machine Learning, and Deep Learning
- The AI engineering lifecycle
- Industry-wide applications of AI
- The role and responsibilities of an AI engineer
2. Foundations of Artificial Intelligence
- Essential AI concepts and terminology
- Supervised, unsupervised, and reinforcement learning
- Basics of neural networks and deep learning
- An overview of generative AI and foundation models
- AI development ecosystems and frameworks
3. Python for AI Engineering
- Key Python libraries for AI
- Working with NumPy, Pandas, and Matplotlib
- Data manipulation and visualization techniques
- Utilizing Jupyter Notebooks
- Writing reusable AI code
4. Data Preparation for AI
- Collecting and interpreting datasets
- Data cleaning and preprocessing
- Feature engineering strategies
- Scaling and normalization of features
- Partitioning datasets for training, validation, and testing
- Managing missing values and outliers
5. Machine Learning Fundamentals
- Regression algorithms
- Classification algorithms
- Clustering techniques
- The model training workflow
- Key model evaluation metrics
- Mitigating overfitting and underfitting
6. Building AI Models with TensorFlow and PyTorch
- Getting started with TensorFlow
- Getting started with PyTorch
- Constructing neural networks
- Training and validating models
- Saving and loading model artifacts
- Comparing TensorFlow and PyTorch frameworks
7. Natural Language Processing Fundamentals
- Text preprocessing methods
- Word embeddings
- Text classification tasks
- Sentiment analysis
- An introduction to transformer models
- Practical NLP use cases
8. AI in Software Development
- Integrating AI into existing applications
- Interacting with AI services via APIs
- Creating AI-powered applications
- Using AI-assisted software development tools
- Testing AI-enabled applications
9. AI Engineering Best Practices
- Effective project organization
- Version control using Git
- Experiment tracking
- Model versioning
- Documentation standards
- Ensuring reproducibility in AI projects
10. Deploying AI Models
- Model serialization
- Building inference services
- Implementing REST APIs for AI models
- Using Docker for AI deployment
- Monitoring deployed models
- Maintaining and updating models
11. AI Data Engineering
- Building data pipelines
- ETL processes
- Handling structured and unstructured data
- Data storage solutions
- 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
- Principles of responsible AI development
- Regulatory and governance frameworks
13. AI Project Management
- The AI project lifecycle
- Applying Agile methodologies to AI projects
- Collaborating between technical and business teams
- Estimating AI project scope
- Managing risks
- Measuring project success
14. Hands-on AI Engineering Workshop and Future Trends
- Establishing a complete AI development workflow
- Executing an end-to-end machine learning project
- Training and evaluating a model with TensorFlow or PyTorch
- Deploying a basic AI application
- Emerging trends in AI Engineering
- Generative AI and Large Language Models (LLMs)
- MLOps and AI automation
- Career trajectories and continuous learning
- Summary, Q&A, and subsequent steps
Requirements
- A solid grasp of fundamental programming concepts.
- Practical experience with Python programming.
- Basic knowledge of 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.
