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Patterns of software choices Reprint Four patterns of the workflow regarding combination of Model Development phase and Model Operating phase
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Lessons Learned from Big Data and AI/ML Collaboration for Giant Hogweed Eradication
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- 1 Intro
- 2 Self introduction
- 3 Outline
- 4 Our journey to apply deep learning for giant hogweed eradication
- 5 About giant hogweed
- 6 Project Overview
- 7 Data volumes
- 8 Various specialty
- 9 Challenges of the project
- 10 Architecture overview
- 11 Data flow-1/4: Data preparation phase
- 12 Assistance tool for data preparation
- 13 Inference processing by Apache Spark
- 14 Data flow-4/4: Data analysis phase
- 15 Lessons Learned from a consideration of architecture design
- 16 Common view in Machine learning system
- 17 Scaled-ML systems
- 18 ML Application with scalability on our architecture
- 19 Model development vs. Model operating
- 20 Dev-friendly to Dev-friendly
- 21 Dev-friendly to Ops-friendly
- 22 Use case: Uber implemented ML Ops on Spark
- 23 Ops-friendly to Ops-friendly Patterns that selected a toolset familiar to Model Operator for both model development and operation
- 24 Use case: Twitter leveraged Scala for feature engineering
- 25 Example software: BigDL by Intel
- 26 Patterns of software choices Reprint Four patterns of the workflow regarding combination of Model Development phase and Model Operating phase
- 27 Architecture and data pipeline ver.2.0
- 28 Tips: Abstraction of functions used in applications
- 29 Tips: Detecting and storing deterioration of confidence
