Course Outline
Course Outline Training Proposal
Day 1 - Introduction to AI and Python for Data Workflows
• Overview of the artificial intelligence and machine learning landscape
• The role of AI in contemporary data engineering
Python fundamentals review for AI applications
• Manipulating data with pandas and NumPy
• Introduction to APIs and JSON data management
• Mini exercise: loading and transforming datasets
Day 2 - Machine Learning Foundations for Practitioners
• Concepts in supervised and unsupervised learning
• Feature engineering and data preparation techniques
• Basics of model training using scikit-learn
• Model evaluation and key performance metrics
• Introduction to the concept of model deployment
• Hands-on exercise: building a simple predictive model
Day 3 - Introduction to LLMs and Prompt Engineering
• Understanding large language models and their underlying mechanisms
• Tokenization, context windows, and inherent limitations
• Principles and techniques of prompt design
• Zero-shot and few-shot prompting strategies
• Strategies for prompt evaluation and iterative improvement
• Hands-on prompt engineering exercises
Day 4 - Building AI Applications with LLMs
• Leveraging LLM APIs within Python
• Structured outputs and the concept of function calling
• Developing chat-based and task-oriented applications
• Introduction to retrieval augmented generation
• Connecting LLMs to external data sources
• Mini project: building a basic AI assistant
Day 5 - Productionizing AI Solutions
• Designing scalable AI workflows
• Integrating AI into existing data pipelines
• Monitoring and enhancing model performance
• Cost optimization and strategies for API usage
• Security and considerations for responsible AI
• Final project: developing 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
