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 tangible expertise in artificial intelligence, Python programming, and large language models. The curriculum emphasizes real-world application, including model utilization, prompt engineering, and the creation of AI-driven solutions. Participants will engage in progressive exercises that advance from foundational concepts to the development of deployable AI workflows.
Training Format
• In-person classroom instruction
• Instructor-led sessions with guided practice
• Interactive discussions and real-world case studies
• Daily hands-on exercises
Course Objectives
• Grasp core AI and machine learning concepts pertinent to modern applications
• Enhance Python proficiency for AI development and data workflows
• Understand the mechanics of large language models and learn to utilize them effectively
• Design and refine prompts for consistent and reliable outputs
• Develop end-to-end AI solutions utilizing APIs and frameworks
• Integrate AI capabilities into data engineering pipelines
This course is available as onsite live training in Uzbekistan 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
• Refresher on Python fundamentals for AI applications
• Data manipulation using pandas and NumPy
• Introduction to APIs and JSON data handling
• Mini exercise: Loading and transforming datasets
Day 2 - Machine Learning Foundations for Practitioners
• Concepts of supervised and unsupervised learning
• Feature engineering and data preparation techniques
• Basics of model training using scikit-learn
• Model evaluation and performance metrics
• Introduction to model deployment concepts
• Hands-on construction of a simple predictive model
Day 3 - Introduction to LLMs and Prompt Engineering
• Understanding large language models and their operational mechanisms
• Tokenization, context windows, and inherent limitations
• Principles and techniques of prompt design
• Zero-shot and few-shot prompting strategies
• Prompt evaluation and iteration strategies
• Hands-on prompt engineering exercises
Day 4 - Building AI Applications with LLMs
• Utilizing LLM APIs in Python
• Concepts of structured outputs and function calling
• Developing chat-based and task-oriented applications
• Introduction to retrieval augmented generation (RAG)
• Connecting LLMs with external data sources
• Mini project: Creating a simple AI assistant
Day 5 - Productionizing AI Solutions
• Designing scalable AI workflows
• Integrating AI into data pipelines
• Monitoring and optimizing model performance
• Cost optimization and API usage strategies
• Security and responsible AI considerations
• Final project: Constructing an end-to-end AI solution
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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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