Course Outline
Course Outline Training Proposal
Day 1 - Foundations of AI and Python for Data Workflows
• Overview of the current artificial intelligence and machine learning landscape
• The role of AI in contemporary data engineering practices
• A Python fundamentals refresher tailored for AI applications
• Utilizing pandas and NumPy for data manipulation
• Introduction to APIs and JSON data processing
• Mini exercise: Loading and transforming datasets
Day 2 - Machine Learning Foundations for Practitioners
• Concepts of supervised and unsupervised learning
• Techniques for feature engineering and data preparation
• Fundamentals of model training using scikit-learn
• Model evaluation and assessment via performance metrics
• Introduction to concepts surrounding model deployment
• Practical session: Building a simple predictive model
Day 3 - Introduction to LLMs and Prompt Engineering
• Understanding large language models and their operational mechanics
• Tokenization, context windows, and inherent limitations
• Core principles and techniques for prompt design
• Zero-shot and few-shot prompting strategies
• Strategies for evaluating and iterating on prompts
• Hands-on prompt engineering activities
Day 4- Developing AI Applications with LLMs
• Utilizing LLM APIs within Python
• Concepts of structured outputs and function calling
• Developing chat-based and task-oriented applications
• Introduction to retrieval augmented generation
• Linking LLMs with external data sources
• Mini project: Constructing a basic AI assistant
Day 5 - Productionizing AI Solutions
• Designing scalable AI workflows
• Integrating AI components into data pipelines
• Monitoring and enhancing model performance
• Strategies for cost optimization and API usage
• Security and responsible AI considerations
• Final project: Building 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
