Alessandro Romano
Senior Data Scientist
Hamburg, Germany
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I'm an experienced data scientist with a Bachelor’s degree in computer science and a Master’s in data science.I have collaborated with a variety of companies and organizations and currently hold the role of senior data scientist at Kuehne+Nagel. I am passionate about statistics and digital experimentation and have a strong track record of applying these skills to solve complex problems.
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Beyond Pandas: Exploring Polars and Dataframe Standardization in Python
This session, led by Marco Gorelli and Alessandro Romano, offers a comprehensive overview of the evolving landscape of dataframe libraries in Python, focusing on pandas, Polars, and emerging solutions for dataframe standardization. We'll begin with an introduction to pandas, highlighting its core features and well-established place in the data science ecosystem. While pandas has enabled rapid data manipulation and analysis, we will examine their performance limitations and the trade-offs associated with large-scale data processing.
The talk will then introduce Polars, a rising alternative to pandas. We will explore its unique architecture, optimized for high-speed performance, and benchmark its capabilities in comparison to pandas across common workflows. Following this, we’ll discuss the challenges of dataframe standardization, examining how varying implementations across libraries impact code interoperability and usability in diverse data environments.
Finally, we’ll present Narwhals, a Python library designed to bridge dataframe differences by providing a unified, agnostic approach to dataframe operations. This library simplifies multi-library workflows, enabling users to work seamlessly across different dataframe implementations. Join us for a deep dive into the present and future of dataframe manipulation, with practical insights for boosting efficiency and agility in data science projects.
Orchestrating Large Language Models (LLMs) with CrewAI
In an era where AI solutions are increasingly complex, the need for agentic AI, a model of AI that autonomously manages and executes complex workflows, has never been more critical. This session explores the foundations and advantages of agentic AI within the Crew AI framework, highlighting how orchestrations streamline and automate decision-making processes in real-time. The presentation covers Crew AI's core concepts, including tasks, crews, and flow, to show how these components interlink to facilitate flexible, adaptive problem-solving. The talk concludes with a live demo, providing a hands-on look at Crew AI's capabilities and demonstrating how to build a simple agent, giving participants practical insights into orchestrating efficient, intelligent workflows in their own projects.
Alessandro Romano
Senior Data Scientist
Hamburg, Germany
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