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