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Duration 14 hours
Course Outline
1. Introduction to AI Engineering
- Defining AI Engineering
- Distinguishing AI from Machine Learning and Deep Learning
- The AI engineering lifecycle
- AI applications across diverse industries
- 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
- Exploring AI development ecosystems and frameworks
3. Python for AI Engineering
- Key Python libraries for AI development
- Utilizing NumPy, Pandas, and Matplotlib
- Data manipulation and visualization techniques
- Working effectively with Jupyter Notebooks
- Writing scalable and reusable AI code
4. Data Preparation for AI
- Acquiring and interpreting datasets
- Data cleaning and preprocessing workflows
- Feature engineering strategies
- Applying feature scaling and normalization
- Partitioning datasets for training, validation, and testing
- Addressing missing values and outliers
5. Machine Learning Fundamentals
- Regression algorithms
- Classification algorithms
- Clustering methods
- The model training process
- Evaluating model performance 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
- Persisting and retrieving models
- Comparative analysis of both frameworks
7. Natural Language Processing Fundamentals
- Text preprocessing pipelines
- Understanding word embeddings
- Performing text classification
- Implementing sentiment analysis
- An introduction to transformer models
- Practical NLP use cases
8. AI in Software Development
- Integrating AI into existing application stacks
- Interacting with AI services via APIs
- Creating AI-driven applications
- Leveraging AI-assisted development tools
- Testing strategies for AI-enabled software
9. AI Engineering Best Practices
- Organizing AI projects effectively
- Version control using Git
- Tracking experiments
- Implementing model versioning
- Establishing documentation standards
- Ensuring reproducibility in AI initiatives
10. Deploying AI Models
- Serializing models
- Developing inference services
- Exposing REST APIs for AI models
- Introduction to Docker for AI deployment
- Monitoring live models
- Maintaining and updating deployed models
11. AI Data Engineering
- Designing data pipelines
- ETL processes and workflows
- Managing structured and unstructured data
- Choosing appropriate data storage solutions
- Ensuring data quality
- Preparing datasets for production environments
12. Responsible and Ethical AI
- Addressing AI bias and fairness
- Explainable AI (XAI) principles
- Privacy and data protection measures
- Security considerations for AI
- Practicing responsible AI development
- Navigating regulatory and governance landscapes
13. AI Project Management
- The AI project lifecycle
- Applying Agile methodologies to AI
- Fostering collaboration between technical and business units
- Estimating AI project scope and effort
- Risk management strategies
- Defining and 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 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 progression and continuous learning
- Wrap-up, Q&A, and recommended next steps
Requirements
- A working knowledge of fundamental programming concepts
- Practical experience with Python programming
- Basic familiarity with 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.
