The Data Exchange with Ben Lorica

Matt Welsh is a technical leader at Aryn AI, an AI-powered ETL system for RAG frameworks, LLM-based applications, and vector databases. In this episode, we explore how AI is revolutionizing programming and software development.
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Detailed show notes - with links to many references - can be found on The Data Exchange web site.

In this episode of The Data Exchange, I speak with Paco Nathan, author, teacher, and founder of Derwen.ai, a boutique consulting firm specializing in Data, ML, and AI. Paco consults with companies and speaks before audiences all over the world, and I plan to have him as a frequent guest on this podcast to draw on his observations of diverse organizations.
This particular conversation spanned many topics, including:

• Data Governance: Paco’s talk on the topic
• AutoML: Paco’s talk on the topic
• PyTorch and TensorFlow: posts we discussed - [1], [2]
• Reproducibility and feature selection
• The Streamlit open source project for ML app development, and Grus Law (“if you can think up something crazy and/or dangerous to do with notebooks, people are doing it.”)

I want this to be more than just a podcast. I want to create a community to help people make better decisions. A key part of this is getting you involved. I have ideas on how this community will grow, but as a first step, I want to ask a question related to one of the topics that Paco and I discussed: PyTorch and TensorFlow. I'd love to have you weigh in by filling out the survey form. I'll report on results and key insights in a future episode of this podcast.
[full show notes can be found on the Data Exchange site.]

Max Mergenthaler (CEO) and Azul Garza Ramirez (CTO) are co-founders of Nixtla, a startup that seeks to make cutting-edge predictive insights widely accessible. In this episode we discuss TimeGPT, Nixtla’s new frontier model for time series forecasting.
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Detailed show notes can be found on The Data Exchange web site.

Deepti Srivastava is the Founder and CEO of Snow Leopard. We dive into Snow Leopard’s innovative approach to data integration, exploring its live data access model that bypasses traditional ETL pipelines to offer real-time data retrieval directly from source systems.
Subscribe to the Gradient Flow Newsletter: https://gradientflow.substack.com/
Subscribe: Apple • Spotify • Overcast • Pocket Casts • AntennaPod • Podcast Addict • Amazon • RSS.
Detailed show notes - with links to many references - can be found on The Data Exchange web site.

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In this episode of the Data Exchange I speak with Murat Özbayoğlu, Chair of Artificial Intelligence Engineering at TOBB University of Economics and Technology in Ankara, Turkey. I wanted Murat on to discuss two survey papers he and his colleagues wrote on the use of deep learning in finance.
I’ve long been fascinated with finance and trading. My first job after I left academia was as the lead quant in a hedge fund, and ever since, I’ve tried to stay abreast of what tools and techniques quants and data scientists in finance are using. Forecasting in this setting usually means price prediction or price movement (trend) prediction . Output of forecasting models are used to inform investment decisions. What makes finance particularly challenging is that many people are using the same underlying data (time series of prices/values), and thus as Murat notes, many firms use alternative data sources (such as text) as potential sources of additional signal.
Download the 2020 NLP Survey Report and learn how companies are using and implementing natural language technologies.
Detailed show notes can be found on The Data Exchange web site.
Subscribe to The Gradient Flow Newsletter.