Course Outline
Course Outline Training Proposal
Day 1 - Foundations of AI and Python for Data Workflows
• Survey of the artificial intelligence and machine learning ecosystem
• The role of AI within contemporary data engineering
• Refreshing Python fundamentals for AI contexts
• Data manipulation using pandas and NumPy
• Intro to APIs and JSON data management
• Mini exercise: Loading and transforming datasets
Day 2 - Machine Learning Essentials for Practitioners
• Concepts of supervised and unsupervised learning
• Feature engineering and data preprocessing methods
• Basics of model training with scikit-learn
• Model assessment and performance indicators
• Introduction to model deployment concepts
• Practical session: Constructing a basic predictive model
Day 3 - Overview of LLMs and Prompt Engineering
• Understanding large language models and their operational mechanisms
• Tokenization, context windows, and associated constraints
• Principles and techniques for prompt design
• Zero-shot and few-shot prompting strategies
• Prompt assessment and iterative improvement
• Hands-on prompt engineering tasks
Day 4 - Developing AI Applications with LLMs
• Utilizing LLM APIs in Python
• Structured outputs and function calling concepts
• Creating chat-based and task-oriented applications
• Introduction to retrieval augmented generation
• Linking LLMs with external data sources
• Mini project: Building a basic AI assistant
Day 5 - Productionizing AI Solutions
• Architecting scalable AI workflows
• Integrating AI into data pipelines
• Monitoring and enhancing model performance
• Cost optimization and API consumption strategies
• Security and responsible AI considerations
• Capstone 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
