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Agile Data Science - Achieving Salesforce-Scale Machine Learning in Production
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- 1 Introduction
- 2 Why are AI Machine Learning and Data Science still out of reach
- 3 What does it mean to move beyond giving your data scientists access
- 4 Salesforces approach to AI
- 5 Agenda
- 6 Building ML apps
- 7 No company is building one app
- 8 We need a third data scientist
- 9 Different degrees of skill set
- 10 Different data sizes
- 11 Classification
- 12 Language
- 13 Customization
- 14 Trust
- 15 Fixing leaks
- 16 Traditional AI process
- 17 Automation
- 18 Data Science Journey
- 19 Building Models
- 20 Getting Access to Data
- 21 Shipping Your App
- 22 Everyone Needs a Data Scientist
- 23 Data Scientists and Software Developers
- 24 Data Scientist
- 25 Building a Platform
- 26 Working Together
- 27 Finding opportunities for reuse
- 28 Transmogrify
- 29 Automated Pipeline
- 30 Data Sampling
- 31 Text Data
- 32 Stop Words
- 33 Learning Opportunities
- 34 Model Selection
- 35 The Job is Never Done
- 36 Metrics to Drive Agility
- 37 What Happens After Deployment
- 38 Minimum Viable Product
- 39 Agile Process
- 40 Agile Data Science
- 41 Monitoring
- 42 Model Monitoring
- 43 Investigate
- 44 Backlog
- 45 Focus
- 46 Key takeaways
- 47 Join the open source community
- 48 Thank you
- 49 Getting started in data science
- 50 ACM resources
- 51 Open source components
- 52 Platform secured experimentation
- 53 Latency considerations
