Bespoke Applied Artificial Intelligence and LLM Engineering with Python Training Course
Course Overview
This practical training is tailored for data engineering professionals aiming to develop hands-on expertise in artificial intelligence, Python, and large language models. The curriculum emphasizes real-world applications, including model utilization, prompt engineering, and the creation of AI-driven solutions. Participants will engage in a series of progressive exercises that advance from foundational concepts to the development of deployable AI workflows.
Training Format
• In-person classroom instruction
• Instructor-led sessions featuring guided practice
• Interactive discussions complemented by real-world case studies
• Daily hands-on exercises
Course Objectives
• Gain a solid understanding of core AI and machine learning concepts applicable to modern systems
• Enhance Python proficiency for AI development and data workflows
• Comprehend the mechanics of large language models and learn to leverage them effectively
• Design and optimize prompts to ensure consistent and reliable outputs
• Construct end-to-end AI solutions utilizing APIs and frameworks
• Integrate AI capabilities into existing data engineering pipelines
This course is available as onsite live training in Mexico or online live training.
Course Outline
Course Outline Training Proposal
Day 1 - Introduction to AI and Python for Data Workflows
• Overview of the artificial intelligence and machine learning landscape
• The role of AI in contemporary data engineering
• Refreshing Python fundamentals for AI applications
• Working with data using pandas and NumPy
• Introduction to APIs and JSON data handling
• Mini exercise focused on loading and transforming datasets
Day 2 - Machine Learning Foundations for Practitioners
• Concepts of supervised and unsupervised learning
• Techniques for feature engineering and data preparation
• Basics of model training using scikit-learn
• Model evaluation and performance metrics
• Introduction to model deployment concepts
• Hands-on session building a simple predictive model
Day 3 - Introduction to LLMs and Prompt Engineering
• Understanding large language models and their operational principles
• Tokenization, context windows, and inherent limitations
• Principles and techniques for prompt design
• Zero-shot and few-shot prompting strategies
• Strategies for prompt evaluation and iteration
• Hands-on prompt engineering exercises
Day 4 - Building AI Applications with LLMs
• Using LLM APIs in Python
• Concepts of structured outputs and function calling
• Developing chat-based and task-oriented applications
• Introduction to retrieval augmented generation
• Connecting LLMs with external data sources
• Mini project involving the creation of a simple AI assistant
Day 5 - Productionizing AI Solutions
• Designing scalable AI workflows
• Integrating AI into data pipelines
• Monitoring and enhancing model performance
• Strategies for cost optimization and API usage
• Security and responsible AI considerations
• Final project focused on building an end-to-end AI solution
Open Training Courses require 5+ participants.
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Testimonials (2)
Examples/exercices perfectly adapted to our domain
Luc - CS Group
Course - Scaling Data Analysis with Python and Dask
The trainer was very available to answer all te kind of question I did
Caterina - Stamtech
Course - Developing APIs with Python and FastAPI
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