LLM Application Development
Build production apps on OpenAI and open-source LLMs: prompting, fine-tuning and structured outputs.
Beginner
5 coursesIntroduction to Llama
Understand the Llama model family, run Llama locally and build an application with it.
DeepSeek: Introduction
This course covered DeepSeek's Mixture-of-Experts architecture, its benchmarked performance against leading closed-source models, and practical local deployment.
Hugging Face: Introduction
hugging · face · llm · application · development · artificial · intelligence · generative · ai · api · community · deployment · execution · inference · local · models · option · spaces ·...
Mistral: Introduction
Mistral is a European AI provider, founded in Paris in April 2023, that has quickly grown into a major player in the LLM space, backed by significant funding (including investment from Mi...
Open-Source LLMs: Introduction
Open-source large language models have evolved from early source-available releases (BLOOM, Llama) into a mature ecosystem of permissively licensed, highly capable models (Mistral, Qwen,...
Intermediate
6 coursesDeveloping Generative AI Applications with Python and OpenAI
Use the OpenAI API end to end: GPT models, prompt engineering, practical apps and building your own chatbot.
Practical Application of LLMs
Hands-on LLM use cases — sentiment analysis, summarization, fine-tuning, RAG and building AI agents.
Aligning Generative AI with Business Cases
Evaluate and leverage generative AI, building agents with Semantic Kernel, plugins and RAG vector stores.
Data Analysis with Generative AI
Use AI tools for exploratory data analysis, analysis and generating polished analytical reports.
Choosing Open-source LLMs
Evaluate open-source LLMs for performance, usability, licensing and practical deployment constraints.
Integrating Open Source LLMs
Integrate LLMs with the OpenAI Agents SDK, RAG, vector stores, moderation and session history.
Advanced
3 coursesDeploying Open-source LLMs
Select deployment strategies, configure the technical environment and optimize open-source LLMs for production.
Fine-tuning and Customizing LLMs
Specialize LLMs through fine-tuning, standardize outputs for reliability and evaluate fine-tuning performance.
GenAI Model Access Layer and Structured Outputs
Production API patterns, structured & validated outputs, function calling, multimodal pipelines and quality.
Interested in LLM Application Development?
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