Bespoke Applied Artificial Intelligence and LLM Engineering with Python Training Course
Course Overview
This practical training program 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, focusing on model utilization, prompt engineering, and the creation of AI-driven solutions. Participants will engage in a series of progressive exercises that transition from foundational concepts to the construction of deployable AI workflows.
Training Format
• In-person classroom instruction
• Instructor-led sessions featuring guided practice
• Interactive discussions and real-world case studies
• Daily practical exercises
Course Objectives
• Grasp core AI and machine learning principles applicable to contemporary systems
• Enhance Python proficiency for AI development and data processing
• Comprehend the mechanics of large language models and effective utilization strategies
• Design and refine prompts to ensure consistent and reliable outputs
• Develop end-to-end AI solutions utilizing APIs and frameworks
• Seamlessly integrate AI capabilities into data engineering pipelines
This course is available as onsite live training in Turkey or online live training.
Course Outline
Course Outline Training Proposal
Day 1 - Foundations of AI and Python for Data Workflows
• Comprehensive overview of the artificial intelligence and machine learning landscape
• The pivotal role of AI in modern data engineering
• Python fundamentals refresher focused on AI applications
• Data manipulation using pandas and NumPy
• Introduction to APIs and JSON data handling
• Mini exercise involving dataset loading and transformation
Day 2 - Machine Learning Foundations for Practitioners
• Core concepts of supervised and unsupervised learning
• Techniques for feature engineering and data preparation
• Fundamentals of model training with scikit-learn
• Model evaluation methods and performance metrics
• Introduction to model deployment concepts
• Practical session: Building a simple predictive model
Day 3 - Introduction to LLMs and Prompt Engineering
• Understanding large language models and their underlying mechanisms
• Tokenization, context windows, and inherent limitations
• Principles and techniques for prompt design
• Zero-shot and few-shot prompting strategies
• Strategies for prompt evaluation and iterative refinement
• Practical prompt engineering exercises
Day 4 - Developing AI Applications with LLMs
• Implementing LLM APIs in Python
• Concepts of structured outputs and function calling
• Creating chat-based and task-oriented applications
• Introduction to retrieval-augmented generation
• Connecting 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 optimizing model performance
• Strategies for cost optimization and API usage
• Security measures and responsible AI practices
• Final project: Developing 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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