AI requires interdisciplinary teams, Quality Data & Explainability

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...