The Analytics Engineering Podcast

What Can Generative AI Do for Data People? (W/ Sarah Nagy + Chris Aberger)

13 snips
Feb 24, 2023
Guest
Sarah Nagy
Guest
Chris Aberger
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Episode notes
1
Introduction
00:00 • 2min
2
How to Make Machine Learning a Reality in the Data Analysis Space
02:14 • 2min
3
The Advancements in Deep Learning - Where Do You Think We're At?
04:12 • 2min
4
ML Foundation Models - What Are They?
06:17 • 2min
5
The Transformer Architecture in Foundation Models
08:14 • 2min
6
GPT Three Isn't That New, I Think?
10:23 • 2min
7
Is There a Lesson to Be Drawn From This?
12:01 • 3min
8
Are You Using Foundation Models for Data Applications Within Enterprises?
14:51 • 2min
9
Pre-Trained Foundational Models - Is There a Future?
16:36 • 2min
10
What's the Magic of Open AI?
18:55 • 2min
11
The Challenges of Large Language Models
21:08 • 3min
12
Is There a Complete Answer to This Question?
23:49 • 2min
13
How Do We Solve This Accuracy Problem?
25:31 • 2min
14
How Do We Translate Human Speech to Data?
27:18 • 2min
15
The Future of Machine Learning Is Personalized Foundation Models
29:23 • 2min
16
Is There a Higher Barrier to Entry?
31:15 • 2min
17
The Gaps in Text to SQL?
32:46 • 2min
18
A Number Station - Accelerating DBT Code and AI Capabilities
34:28 • 2min
19
Do You Expect the Data Team to Be More Productive?
36:09 • 3min
20
Is There a Law in the Data Space?
38:50 • 2min
21
UI UX for DBT and Power BI?
40:52 • 2min
22
I Think It's a Big if It Works
42:31 • 2min
23
The Challenge of Integrating Generative AI in Startups
44:26 • 2min
24
Analytics Engineering Podcast - Are You Sick of Hearing About DBT?
46:01 • 2min