Course Outline
Course Outline Training Proposal
Day 1 - AI and Python Foundations for Data Workflows
• A comprehensive overview of the AI and machine learning landscape
• The significance of AI in contemporary data engineering
• A refresher on Python fundamentals for AI applications
• Managing data with pandas and NumPy
• Basics of API integration and JSON data management
• Practical exercise: Loading and transforming datasets
Day 2 - Core Machine Learning Concepts for Practitioners
• Principles of supervised and unsupervised learning
• Techniques for feature engineering and data preparation
• Fundamentals of model training using scikit-learn
• Assessing model performance and evaluation metrics
• Introductory concepts on model deployment
• Hands-on session: Building a basic predictive model
Day 3 - Understanding LLMs and Prompt Engineering
• How large language models function internally
• Tokenization, context windows, and inherent limitations
• Key principles and methods in prompt design
• Zero-shot and few-shot prompting strategies
• Methods for prompt assessment and iterative refinement
† Practical prompt engineering workshops
Day 4- Creating AI Applications with LLMs
• Integrating LLM APIs within Python environments
† Concepts of structured outputs and function calling
• Developing chat-based and task-oriented applications
• Introduction to Retrieval-Augmented Generation (RAG)
• Linking LLMs with external data sources
• Capstone exercise: Building a basic AI assistant
Day 5 - Deploying AI Solutions to Production
• Architecting scalable AI workflows
• Embedding AI into existing data pipelines
• Monitoring and enhancing model performance
• Strategies for cost optimization and efficient API usage
• Security protocols and responsible AI practices
• Final project: Developing a complete 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
