Course Outline
Curriculum Overview Training Proposal
Day 1 - Introduction to AI and Python for Data Workflows
• Overview of the current artificial intelligence and machine learning landscape
• The role of AI in contemporary data engineering
• A refresher on Python fundamentals specifically for AI applications
• Data manipulation techniques using pandas and NumPy
• Foundations of API integration and JSON data processing
• Practical exercise: Loading and transforming datasets
Day 2 - Machine Learning Essentials for Practitioners
• Core concepts of supervised and unsupervised learning
• Strategies for feature engineering and data preparation
• Basics of model training utilizing scikit-learn
• Model evaluation techniques and key performance metrics
• An introduction to the principles of model deployment
• Hands-on session: Constructing a basic predictive model
Day 3 - Fundamentals of LLMs and Prompt Engineering
• Deep dive into large language models and their internal mechanics
• Understanding tokenization, context windows, and inherent limitations
• Key principles and techniques in prompt design
• Implementing zero-shot and few-shot prompting strategies
• Methods for prompt evaluation and iterative refinement
• Practical application of prompt engineering exercises
Day 4 - Developing AI Applications with LLMs
• Integrating LLM APIs within Python environments
• Managing structured outputs and implementing function calling
• Creating chat-based and task-oriented applications
• Introduction to retrieval-augmented generation (RAG) concepts
• Linking LLMs to external data sources
• Mini project: Developing a simple AI assistant
Day 5 - Deploying AI Solutions for Production
• Architecting scalable AI workflows
• Seamless integration of AI into data pipelines
• Monitoring and enhancing model performance over time
• Strategies for cost optimization and efficient API usage
• Addressing security concerns and responsible AI practices
• Capstone project: Building a comprehensive 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
