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AI – 一览分析报告.pptx
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2024-04-22
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AI – 一览分析报告.pptx
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Dr Detlef Nauck
Chief Research Scientist for Data Science
AI – an overview
© British Telecommunications plc 2019
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© British Telecommunications plc 2019
Artificial Intelligence (AI) in BT
20 year history in AI research and adoption
For example in field force optimisation, cyber security, network
planning, blocking nuisance calls.
Over 30 current AI activities across BT research
2
© British Telecommunications plc 2019
The AI and Data Science hierarchy of needs
Monica Rogati: The AI Pyramid of Needs. Hacker-noon blog post, 1 Aug 2017
(https://hacker-noon.com/the-ai-hierarchy-of-needs-18f111fcc007, accessed 19/04/2018).
Aggregate / label
Learn / optimise
Explore / transform
Instrumentation, logging, sensors,
external data, user generated content
Move / store
Collect
Reliable data flow, infrastructure, pipelines,
ETL, structured and unstructured data storage
Cleaning, anomaly,
detection, preparation
Analytics, metrics,
segments, aggregates,
features, training data
AI,
deep learning
A / B testing,
experimentation,
simple Machine
Learning (ML) algorithms
3
© British Telecommunications plc 2019
Follow best practice for AI, ML and Data Science – test everything!
Versioning Versioning Versioning Versioning
Data
Repository
Testing
Data wrangling
Feature
generation
Model building
Logging
Logging
Testing Testing
Testing
Decision
making
Model
application
(prediction)
Model
deployment
Enables
Build
In Production
From versioning
2
Testing
1
Testing
3
Testing
4
4
© British Telecommunications plc 2019
567
Test Scenarios
1. Data quality and completeness
2. Data errors and consistency
3. Feature quality
4. Code errors, model quality
5. Model scalability
6. Model quality, feature consistency
7. Control groups, business impact
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