Hands-on sprint to contribute to Streamlit, focused on creating custom components.

Hands-on bootcamp to build a working QA Agent from scratch. 8 live instructor-led sessions (weekends only) designed to help testers, QAs, and SDETs move beyond theory and actually build their own functional QA Agent from scratch. By the end you’ll know how to design and implement AI-driven QA workflows that assist with bug triage and prioritization, generate and organize test reports automatically, run and manage complex testing workflows, and take over repetitive tasks. Tools covered include LangChain, Streamlit, Postman, Playwright, Memory, Prompting, and Tool Use. Format: 8 live sessions, 3 hours each, instructor-led with real coding and projects.

An online session on building production-ready RAG apps using Python, SurrealDB, and Streamlit. Learn the fundamentals in pure Python before using a framework, how to manage multi-model data with SurrealDB, and how to build a front end for your RAG app with Streamlit.

Overview of building production-ready RAG applications using only Python, SurrealDB, and Streamlit; covers fundamentals in pure Python, managing multi-model data with SurrealDB, and building a front end for a RAG app with Streamlit.

Dive into the future of financial analysis and the power of uncertainty quantification using conformal prediction techniques, applied through the robust capabilities of Nixtla library. Gain insights into how this tool provides reliable predictive intervals, enhancing decision-making in volatile markets. Advantages and Fundamentals concepts of Conformal Prediction: Explore the impact of conformal prediction on financial time series forecasting. This talk will introduce the fundamentals of conformal prediction, highlighting its ability to provide reliable uncertainty quantification by generating predictive intervals that encompass real market behaviours. In this way conformal prediction allows investors to make informed decisions despite inherent market volatility. Nixtla library and practical example of a financial time series: To bring theory into practice, Claudio will present the Nixtla library, which simplifies the implementation of conformal prediction, and walk us through a use case using Yahoo! Finance's API with a comparison of TimeGPT against several statistical and machine learning models. Streamlit App: Develop a Streamlit App for a general Stock Market Forecasting, supported by a Stock Agent Analysis built with pyautogen

ChatGPT is awesome, but developing with its API comes at a cost. Fortunately, there are open-source alternatives like Google Gemini, Streamlit, and Python APIs that can fetch prompt results using an API key. In this presentation, I'll explore how to create a lightweight, self-service end-to-end LLMs application using prompt engineering and fine-tuning based on user requests. Additionally, I'll demonstrate how to build a food suggestion application based on ingredients or food names.

Session sur la création d'interfaces web conviviales pour les applications LLM.

Approches et meilleures pratiques pour le déploiement d'applications web alimentées par les LLM.

Discussion sur les widgets, les sessions et la gestion des états dans Streamlit.