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Duration 14 hours
Course Outline
1. Introduction to AI Engineering
- Defining AI Engineering
- Differentiating AI, Machine Learning, and Deep Learning
- The AI engineering lifecycle
- Industries leveraging AI applications
- Key roles and responsibilities of an AI engineer
2. Foundations of Artificial Intelligence
- Essential AI concepts and terminology
- Supervised, unsupervised, and reinforcement learning paradigms
- Basics of neural networks and deep learning
- Introduction to generative AI and foundation models
- The AI development ecosystem and available frameworks
3. Python for AI Engineering
- Key Python libraries essential for AI
- Utilizing NumPy, Pandas, and Matplotlib
- Data manipulation and visualization techniques
- Working effectively with Jupyter Notebooks
- Writing reusable code for AI solutions
4. Data Preparation for AI
- Gathering and interpreting datasets
- Data cleaning and preprocessing procedures
- Feature engineering strategies
- Applying feature scaling and normalization
- Partitioning datasets into training, validation, and test sets
- Addressing missing values and outliers
5. Machine Learning Fundamentals
- Regression algorithms
- Classification algorithms
- Clustering methods
- The model training workflow
- Evaluation metrics for models
- 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
- Persistence: saving and loading models
- Comparing TensorFlow and PyTorch frameworks
7. Natural Language Processing Fundamentals
- Text preprocessing steps
- Understanding word embeddings
- Text classification techniques
- Performing sentiment analysis
- Introduction to transformer architectures
- Practical applications of NLP
8. AI in Software Development
- Integrating AI into existing software applications
- Interfacing with AI services via APIs
- Building AI-driven applications
- Leveraging AI-assisted software development tools
- Testing strategies for AI-enabled applications
9. AI Engineering Best Practices
- Structuring AI projects effectively
- Version control using Git
- Tracking experiments
- Versioning models
- Maintaining documentation standards
- Ensuring reproducibility in AI projects
10. Deploying AI Models
- Serializing models for deployment
- Creating inference services
- Exposing AI models via REST APIs
- Using Docker for AI deployment
- Monitoring deployed models
- Maintaining and updating models
11. AI Data Engineering
- Designing data pipelines
- ETL processes
- Handling structured and unstructured data
- Evaluating data storage options
- Managing data quality
- Preparing datasets for production environments
12. Responsible and Ethical AI
- Addressing AI bias and fairness
- Explainable AI (XAI)
- Data privacy and protection
- Security considerations for AI
- Principles of responsible AI development
- Regulatory and governance frameworks
13. AI Project Management
- The AI project lifecycle
- Applying Agile methodologies to AI
- Fostering collaboration between technical and business teams
- Estimating scope and effort for AI projects
- Managing project risks
- Defining metrics for 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 assessing models 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 lifelong learning
- Summary, Q&A, and subsequent steps
Requirements
- Knowledge of fundamental programming principles
- Proficiency in Python programming
- Basic understanding of 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.
