Course Outline
Course Outline Training Proposal
Day 1 - AI and Python Introduction for Data Workflows
• Survey of the artificial intelligence and machine learning ecosystem
• The significance of AI in contemporary data engineering
• Review of Python essentials for AI contexts
• Manipulating data with pandas and NumPy
• Basics of API interaction and JSON data processing
• Practical task: ingesting and converting datasets
Day 2 - Machine Learning Essentials for Practitioners
• Concepts of supervised and unsupervised learning
• Techniques for feature engineering and data preparation
• Fundamentals of model training via scikit-learn
• Assessing model quality and performance indicators
• Overview of model deployment principles
• Practical exercise: creating a basic predictive model
Day 3 - Introduction to LLMs and Prompt Engineering
• Insights into large language models and their operational logic
• Tokenization, context limits, and system constraints
• Core principles and methods for prompt design
• Application of zero-shot and few-shot prompting
• Strategies for prompt assessment and iterative refinement
• Practical prompt engineering activities
Day 4- Building AI Applications with LLMs
• Utilizing LLM APIs within Python
• Managing structured outputs and function calling
• Developing chat-centric and task-oriented applications
• Introduction to retrieval-augmented generation
• Linking LLMs with external data repositories
• Mini-project: constructing a basic AI assistant
Day 5 - Productionizing AI Solutions
• Architecting scalable AI processes
• Embedding AI into data pipelines
• Tracking and enhancing model effectiveness
• Strategies for cost efficiency and API utilization
• Considerations for security and responsible AI
• Capstone project: implementing a complete 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
