PyData Milan: Stock Forecasting & AI POCs That Deliver

📅 Date: 17-06-2025 (Tuesday)
📍 Location: Talentware @ WAO PL7, Via Luigi Porro Lambertenghi 7 (Isola/Zara/Garibaldi FS)

Agenda

• 18:30 - Doors Open
• 18:50 - Welcome to PyData
• 19:00 - Talk 1: Stock Market Forecasting with Nixtla Conformal Prediction and Stock Agent Analysis
• Speaker: Claudio Giorgio Giancaterino, Actuary & AI Scientist @ Intesa San Paolo Assicurazioni
• 19:40 - Talk 2: Proof of Concept or Proof of Chaos? Develop AI POC that impress and create value for your product
  Speaker: Raffaele Bongo, Senior ML Engineer & Data Solutions Architect @ Talentware
• 20:20 - Networking & Aperitivo offered by Talentware

Join us for dinner after the event too to continue chatting!

Talks and Speakers

Stock Market Forecasting with Nixtla Conformal Prediction and Stock Agent Analysis

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

Claudio Giorgio Giancaterino is an actuary at ISPA by day and freelance data scientist in his spare time.

Proof of Concept or Proof of Chaos? Develop AI POC that impress and create value for your product

With hundreds of new AI technologies and methodologies emerging daily, having a robust and holistic Proof of Concept (POC) framework to identify what truly adds value to a product has never been more critical.

POC projects are often underestimated, yet they play a pivotal role in the success of AI initiatives and features. This talk highlights the importance of bringing rigor back to AI POCs, offering a structured, end-to-end approach to ensure their effectiveness. It also examines how AI feature POCs intersect with traditional software and design processes, clarifying AI’s role within the broader product development lifecycle.

We’ll explore common pitfalls and strategies to address them. You’ll learn how to:

1. Define scope: Set clear objectives, boundaries, and success metrics while involving the right stakeholders from the start.
2. Iterate effectively: Track experiments properly, build in a flexible and repeatable way, and be ready to pivot or start over when necessary.
3. Facilitate feedback loops: Present results effectively, collect actionable feedback, and guide discussions to improve the solution.

By applying these principles, teams can develop AI POCs that not only prove value but also lay the groundwork for scalable, production-ready solutions.

Raffaele Bongo is a Senior Machine Learning Engineer and Data Solutions Architect at Talentware. Raffaele has an extensive experience in startups and scale-ups, where he has specialized in building and deploying machine learning models and data pipelines on cloud platforms. Originally, Raffaele signed up for mechanical engineering but switched to computer science last minute because AI sounded cooler. Great call!