Chapters
Transcript
Episode notes
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
Introduction
00:00 • 2min
The Diversity of NP Models
01:59 • 2min
I Think It's a Draw to Think About Language
04:10 • 2min
Getting Machine Learning Models Into Production
06:36 • 2min
How to Scale Models for Inference
08:48 • 2min
How to Train a Large Language Model for Hebrew?
11:13 • 2min
Data Augmentation in an NLP Framework?
13:32 • 2min
The Role of Data Labelling in Modeling
15:44 • 3min
Data Quality Matters a Lot More Than Quantity
18:36 • 2min
The Power of Data Quality in a Bi M L Career
20:14 • 2min
How to Do Data Labelling?
22:27 • 2min
How to Automate the Data Labelling Process?
24:46 • 3min
Is Your Model Good Enough?
27:33 • 2min
Unitesting for Data and Models
29:28 • 5min
How Long Is an Iteration Cycle for Data Labelling?
34:27 • 3min
Designing High Lighting for Relationships
37:14 • 2min
How to Deploy Machine Learning Models to Production
39:02 • 2min
How Do You Deploy Multiple Models in a Container?
41:12 • 2min
How to Standardize M L Deployments at Large Scale
43:29 • 2min
Then, What Are the Strongest, Most Exciting Trends in Ml?
45:07 • 3min
The M L Tooling Space Is Growing Generally
47:55 • 2min
What's Next?
50:19 • 2min
M L and Data Science Podcasts
52:04 • 2min
Machine Learning - What Are the Trends?
53:59 • 3min
How to Be a Data Scientist?
56:43 • 2min
Do You Have a Computer Science Background?
58:20 • 4min