Course Outline
Course Outline Training Proposal
Day 1 - Introduction to AI and Python for Data Workflows
• Landscape of artificial intelligence and machine learning
• The impact of AI on modern data engineering practices
• Refresher on Python fundamentals for AI contexts
• Data manipulation using pandas and NumPy
• Overview of APIs and JSON data processing
• Practical exercise: Loading and transforming datasets
Day 2 - Machine Learning Foundations for Practitioners
• Concepts in supervised and unsupervised learning
• Feature engineering and data preparation methods
• Fundamentals of model training with scikit-learn
• Techniques for model evaluation and assessing performance metrics
• Overview of model deployment concepts
• Practical exercise: Building a basic predictive model
Day 3 - Introduction to LLMs and Prompt Engineering
• Mechanics and functionality of large language models
• Tokenization, context windows, and system limitations
• Core principles and strategies for prompt design
• Techniques for zero-shot and few-shot prompting
• Methods for evaluating and iterating on prompts
• Practical exercise: Applying prompt engineering techniques
Day 4 - Building AI Applications with LLMs
• Utilizing LLM APIs within Python environments
• Structured outputs and the concept of function calling
• Development of chat-based and task-oriented applications
• Overview of Retrieval Augmented Generation (RAG)
• Integrating LLMs with external data sources
• Mini-project: Creating a basic AI assistant
Day 5 - Productionizing AI Solutions
• Architecting scalable AI workflows
• Embedding AI into data pipelines
• Strategies for monitoring and enhancing model performance
• Cost efficiency and API usage optimization
• Security protocols and responsible AI practices
• Final 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
