Course Outline
Curriculum Training Proposal
Day 1 - Introduction to AI and Python for Data Workflows
• Survey of the current artificial intelligence and machine learning landscape
• The impact of AI in contemporary data engineering
• Python fundamentals review focused on AI use cases
• Data manipulation using pandas and NumPy
• Overview of APIs and handling JSON data
• Mini exercise: Loading and transforming datasets
Day 2 - Machine Learning Basics for Practitioners
• Concepts of supervised and unsupervised learning
• Techniques for feature engineering and data preparation
• Fundamentals of model training with scikit-learn
• Assessing model performance and evaluation metrics
• Introduction to deployment concepts for models
• Practical session: Creating a basic predictive model
Day 3 - Introduction to LLMs and Prompt Engineering
• Exploring the functionality and structure of large language models
• Understanding tokenization, context windows, and associated constraints
• Key principles and methods for prompt design
• Zero-shot and few-shot prompting techniques
• Strategies for evaluating and refining prompts
• Practical prompt engineering tasks
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
• Overview of retrieval-augmented generation
• Linking LLMs with external data repositories
• Mini project: Constructing a basic AI assistant
Day 5 - Implementing AI Solutions in Production
• Architecting scalable AI processes
• Embedding AI into data pipelines
• Monitoring and enhancing model performance
• Strategies for cost optimization and API management
• Considerations for security and responsible AI
• Capstone project: Building 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
