How to Catch and Fix Harmful Generative A.I. Output

Today, the A.I. entrepreneur Krishna Gade joins me to detail open-source solutions for overcoming the safety and security issues associated with generative A.I. systems, such as those powered by Large Language Models (LLMs).

The remarkably well-spoken Krishna:
• Is Co-Founder and CEO of Fiddler AI, an observability platform that has raised over $45m in venture capital to build trust in A.I. systems.
• Previously worked as an engineering manager on Facebook’s Newsfeed, as Head of Data Engineering at Pinterest, and as a software engineer at both Twitter and Microsoft.
• Holds a Masters in Computer Science from the University of Minnesota.

In this episode, Krishna details:
• How the LLMs that enable Generative A.I. are prone to inaccurate statements, can be biased against protected groups and are susceptible to exposing private data.
• How these undesirable and even harmful LLM outputs can be identified and remedied with open-source solutions like the Fiddler Auditor that his team has built.

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

How to Learn Data Engineering

As data sets continue to grow exponentially, Data Engineering skills become increasingly essential — standalone or as part of Data Scientists' expertise. In today's episode, Andreas Kretz details how to Learn Data Engineering.

Andreas:
• Is the Founder of Learn Data Engineering, a platform through which he’s taught over a thousand students the theory and practice of data engineering.
• Has provided countless more folks with data engineering tips and tricks through his YouTube channel, which has over 10,000 subscribers.
• Worked for ten years at the German industrial giant Bosch, including as a data engineering team lead and data lab team lead.
• Holds a Computer Science degree from the Technical University of Applied Sciences Würzburg-Schweinfurt (THWS).
• With over 100,000 followers on LinkedIn, has twice been recognized as a Top Voice for Data Science and Analytics on the platform.

Today’s episode will appeal primarily to technical listeners particularly to data scientists that are keen to develop ever-more-critical data engineering skills.

In this episode, Andreas details:
• What data engineering is and how it relates to adjacent fields like data science, software engineering, and machine learning engineering.
• Why data engineering skills become increasingly essential to data scientists and data analysts with each passing year.
• What sets Senior Data Engineers apart from junior ones.
• His general process for tackling data engineering problems.
• The must-know data-engineering tools of today as well as the emerging ones you shouldn’t miss.

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