LangChain: Create LLM Applications Easily in Python

Today's episode is a fun intro to the powerful, versatile LLM-development framework LangChain. In it, Kris Ograbek talks us through how to use LangChain to chat with previous episodes of SuperDataScience! 😎

Kris:
• Is a content creator who specializes in creating LLM-based projects — with Python libraries like LangChain and the Hugging Face Transformers library — and then using the projects to teach these LLM techniques.
• Previously, he worked as a software engineer in Germany.
• He holds a Master’s in Electrical and Electronics Engineering from the Wroclaw University of Science and Technology.

In this episode, Kris details:
• The exceptionally popular LangChain framework for developing LLM applications.
• Specifically, he introduces how LangChain is so powerful by walking us step-by-step through a chatbot he built that interactively answers questions about episodes of the SuperDataScience podcast.

Having been listening to the podcast for years, at the end of the episode Kris flips the script on me and asks me some of the burning questions he has for me — questions that perhaps many other listeners also have wondered the answers to.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

XGBoost: The Ultimate Classifier, with Matt Harrison

XGBoost is typically the most powerful ML option whenever you're working with structured data. In today's episode, world-leading XGBoost XPert (😂) Matt Harrison details how it works and how to make the most of it.

Matt:
• Is the author of seven best-selling books on Python and Machine Learning.
• His most recent book, "Effective XGBoost", was published in March.
• Teaches "Exploratory Data Analysis with Python" at Stanford University.
• Through his consultancy MetaSnake, he’s taught Python at leading global organizations like NASA, Netflix, and Qualcomm.
• Previously worked as a CTO and Software Engineer.
• Holds a degree in Computer Science from Stanford.

Today’s episode will appeal primarily to practicing data scientists who are keen to learn about XGBoost or keen to become an even deeper expert on XGBoost by learning about it from a world-leading educator on the library.

In this episode, Matt details:
• Why XGBoost is the go-to library for attaining the highest accuracy when building a classification model.
• Modeling situations where XGBoost should not be your first choice.
• The XGBoost hyperparameters to adjust to squeeze every bit of juice out of your tabular training data and his recommended library for automating hyperparameter selection.
• His top Python libraries for other XGBoost-related tasks such as data preprocessing, visualizing model performance, and model explainability.
• Languages beyond Python that have convenient wrappers for applying XGBoost.
• Best practices for communicating XGBoost results to non-technical stakeholders.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Taipy, the open-source Python application builder

An A.I. expert for nearly 40 years, Vincent Gosselin adores the field's lingua franca, Python. In today's episode, hear how he created its open-source Taipy library so you can easily build Python-based web apps and scalable, reusable data pipelines.

Vincent:
• Is CEO and co-founder of taipy.io, an open-source Python library that works up and down the stack to both easily build web applications and back-end data pipelines.
• Having obtained his Masters in CS and A.I. from the Université Paris-Saclay in 1987, he’s amassed a wealth of experience across a broad range of industries, including semiconductors, finance, airspace, and logistics.
• Has held roles including Director of Software Development at ILOG, Director of Advanced Analytics at IBM, and VP of Advanced Analytics at DecisionBrain.

Today’s episode will appeal primarily to hands-on practitioners who are keen to hear about how they can be accelerating their productivity in Python, whether it’s on the front end (to build a data-driven web-application) or on the back end (to have scalable, reusable and maintainable data pipelines). That said, Vincent’s breadth of wisdom — honed over his decades-long A.I. career — may prove to be fascinating and informative to technical and non-technical listeners alike.

In this episode, Vincent details:
• The critical gaps in Python development that led him to create Taipy.
• How much potential there is for data-pipeline engineering to be improved.
• How shifting toward lower-code environments can accelerate Python development without sacrificing any flexibility.
• The 50-year-old programming language that was designed for A.I. and that he was nostalgic for until Python emerged on the scene.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.