Course Outline
Curriculum Outline Training Proposal
Day 1 - Foundations of AI and Python for Data Operations
• Landscape overview of artificial intelligence and machine learning
• The significance of AI in contemporary data engineering
• Python core concepts refresher focused on AI use cases
• Data manipulation using pandas and NumPy
• Exploring APIs and JSON data management
• Practical task: Loading and processing datasets
Day 2 - Core Machine Learning Concepts for Professionals
• Principles of supervised and unsupervised learning
• Techniques for feature engineering and data preprocessing
• Basics of model training with scikit-learn
• Assessing model performance and key metrics
• Introduction to model deployment methodologies
• Practical task: Constructing a basic predictive model
Day 3 - LLM Fundamentals and Prompt Engineering
• Mechanics of large language models
• Tokenization, context limits, and operational constraints
• Core principles and strategies for prompt design
• Zero-shot and few-shot prompting techniques
• Strategies for prompt assessment and iterative refinement
• Practical task: Prompt engineering exercises
Day 4- Building AI Applications with LLMs
• Utilizing LLM APIs within Python environments
• Concepts of structured outputs and function calling
• Developing chat-based and task-oriented applications
• Introduction to retrieval-augmented generation
• Integrating LLMs with external data sources
• Project task: Creating a basic AI assistant
Day 5 - Deploying AI Solutions to Production
• Architecting scalable AI workflows
• Embedding AI into data processing pipelines
• Monitoring and enhancing model performance
• Cost efficiency and API management strategies
• Security protocols and responsible AI practices
• Capstone project: Developing a comprehensive 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
