Course Outline
Course Outline Training Proposal
Day 1 - Foundations of AI and Python for Data Workflows
• Landscape overview of artificial intelligence and machine learning
• The role of AI in contemporary data engineering
• Refreshing Python fundamentals for AI applications
• Data manipulation using pandas and NumPy
• Introduction to API interactions and JSON data processing
• Mini exercise: dataset loading and transformation
Day 2 - Machine Learning Essentials for Practitioners
• Concepts of supervised and unsupervised learning
• Techniques for feature engineering and data preparation
• Basics of model training with scikit-learn
• Model evaluation methods and performance metrics
• Introductory concepts in model deployment
• Hands-on session: developing a simple predictive model
Day 3 - Introduction to LLMs and Prompt Engineering
• Understanding large language model architecture and functionality
• Tokenization, context windows, and inherent limitations
• Principles and techniques for effective prompt design
• Zero-shot and few-shot prompting methods
• Strategies for prompt evaluation and iterative refinement
• Practical prompt engineering exercises
Day 4 - Developing AI Applications with LLMs
• Utilizing LLM APIs within Python
• Concepts of structured outputs and function calling
• Creating chat-based and task-oriented applications
• Introduction to retrieval-augmented generation
• Integrating LLMs with external data sources
• Mini project: building a basic AI assistant
Day 5 - Deploying AI Solutions in Production
• Architecting scalable AI workflows
• Embedding AI into data pipelines
• Monitoring and enhancing model performance
• Cost optimization and strategic API usage
• Security and responsible AI considerations
• Capstone project: constructing an end-to-end AI solution
Testimonials (2)
The trainer was very available to answer all te kind of question I did
Caterina - Stamtech
Course - Developing APIs with Python and FastAPI
Trainer develops training based on participant's pace
