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From MLOps to DataOps - Santona Tuli
MP3•Episode hjem
Manage episode 366897897 series 2831626
Indhold leveret af DataTalks.Club. Alt podcastindhold inklusive episoder, grafik og podcastbeskrivelser uploades og leveres direkte af DataTalks.Club eller deres podcastplatformspartner. Hvis du mener, at nogen bruger dit ophavsretligt beskyttede værk uden din tilladelse, kan du følge processen beskrevet her https://da.player.fm/legal.
We talked about:
- Santona's background
- Focusing on data workflows
- Upsolver vs DBT
- ML pipelines vs Data pipelines
- MLOps vs DataOps
- Tools used for data pipelines and ML pipelines
- The “modern data stack” and today's data ecosystem
- Staging the data and the concept of a “lakehouse”
- Transforming the data after staging
- What happens after the modeling phase
- Human-centric vs Machine-centric pipeline
- Applying skills learned in academia to ML engineering
- Crafting user personas based on real stories
- A framework of curiosity
- Santona's book and resource recommendations
Links:
- LinkedIn: https://www.linkedin.com/in/santona-tuli/
- Upsolver website: upsolver.com
- Why we built a SQL-based solution to unify batch and stream workflows: https://www.upsolver.com/blog/why-we-built-a-sql-based-solution-to-unify-batch-and-stream-workflows
Free MLOps course: https://github.com/DataTalksClub/mlops-zoomcamp
Join DataTalks.Club: https://datatalks.club/slack.html
Our events: https://datatalks.club/events.html
168 episoder
MP3•Episode hjem
Manage episode 366897897 series 2831626
Indhold leveret af DataTalks.Club. Alt podcastindhold inklusive episoder, grafik og podcastbeskrivelser uploades og leveres direkte af DataTalks.Club eller deres podcastplatformspartner. Hvis du mener, at nogen bruger dit ophavsretligt beskyttede værk uden din tilladelse, kan du følge processen beskrevet her https://da.player.fm/legal.
We talked about:
- Santona's background
- Focusing on data workflows
- Upsolver vs DBT
- ML pipelines vs Data pipelines
- MLOps vs DataOps
- Tools used for data pipelines and ML pipelines
- The “modern data stack” and today's data ecosystem
- Staging the data and the concept of a “lakehouse”
- Transforming the data after staging
- What happens after the modeling phase
- Human-centric vs Machine-centric pipeline
- Applying skills learned in academia to ML engineering
- Crafting user personas based on real stories
- A framework of curiosity
- Santona's book and resource recommendations
Links:
- LinkedIn: https://www.linkedin.com/in/santona-tuli/
- Upsolver website: upsolver.com
- Why we built a SQL-based solution to unify batch and stream workflows: https://www.upsolver.com/blog/why-we-built-a-sql-based-solution-to-unify-batch-and-stream-workflows
Free MLOps course: https://github.com/DataTalksClub/mlops-zoomcamp
Join DataTalks.Club: https://datatalks.club/slack.html
Our events: https://datatalks.club/events.html
168 episoder
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