Data Transformers Podcast
AI requires interdisciplinary teams, Quality Data & Explainability








1x

00:00
/

00:24:34

Subscribe
Share




Apple Podcasts

Google Podcasts

Spotify

Stitcher

TuneIn
RSS Feed


Share






Link


Embed

AI requires interdisciplinary teams, Quality Data & Explainability
'
/>



Apple Podcasts



Google Podcasts



Spotify



Stitcher

https://youtu.be/yCxofWqPyyU

Episode Title : AI requires interdisciplinary teams, Quality Data & Explainability

Episode Summary: Artificial Intelligence and Machine Learning projects require interdisciplinary skills in devops, SW engineering in addition to hard core data science coding skills. Additionally, lot of rigor needs to be put into cleaning up the data that is fed into the models. On an interesting note, AI models can also be used for improving data quality as well. Lastly, Explainability of models and data is becoming important and as such explainability needs to be baked in.
Topics discussed in this episode:
Leveraging past experience in current job (01:47): Fiona’s past experience in SW engineering, Ph.D., and SW development...

Ver todos los episodios