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[] DevOps for Data as a Service
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Classroom Contents
Applying DevOps Practices in Data and ML Engineering
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- 1 [] Introduction to Antoni Ivanov
- 2 [] Applying DevOps Practices in Data and ML Engineering
- 3 [] Agenda
- 4 [] DevOps Challenges
- 5 [] Versatile Data Kit
- 6 [] DevOps for Data as a Service
- 7 [] Components
- 8 [] VDK, SDK, and VDK Runtime Control Service
- 9 [] Automate and Abstract the Development Process
- 10 [] Quick Example: DevOps Plugin
- 11 [] Automate and Abstract the Data Journey
- 12 [] Quick Example: vdk-impala, vdk-trino
- 13 [] Data Journey: Ingestion
- 14 [] Ingestion Job
- 15 [] Demo
- 16 [] Step 1: Explore VDK's Functionalities
- 17 [] Step 2: Create a Data Job
- 18 [] Features in real-time
- 19 [] Working with streaming tools
- 20 [] VK as a training model
- 21 [] Step 3: Ingestion Job
- 22 [] Step 4: Processing Job
- 23 [] Step 5: Deploy
- 24 [] Step 6: Extend Anonymize
- 25 [] Step 6: Extend SQL Validation
