2. Abstract
First, I will train and deploy multiple natural language understanding
(NLU) models and compare them in live production using reinforcement
learning to dynamically shift traffic to the winning model.
Second, I will describe the differences between A/B and multi-armed
bandit tests including exploration-exploitation, reward-maximization, and
regret-minimization.
Third, I will dive deep into the details of building and scaling a multi-
armed bandit deployment on AWS using a real-time, stream-based text
classifier with TensorFlow, PyTorch, and BERT on 150+ million reviews
from the Amazon Customer Reviews Dataset.
3. Me Developer Advocate
AI and Machine Learning @ AWS
(Based in San Francisco)
Co-Author of the O'Reilly Book,
"Data Science on AWS."
Founder of the Advanced
Kubeflow Meetup (Global)
https://www.datascienceonaws.com
data-science-on-aws
@cfregly
linkedin.com/in/cfregly
https://meetup.com/Data-Science-on-AWS
4. Data Science on AWS – Book and Workshop Outline
https://www.datascienceonaws.com/
5. Agenda
• Compare A/B Tests vs. Multi-Armed Bandit Tests
• Optimize Bandits with Reinforcement Learning
• Train 2 BERT Languge Models with TensorFlow
• Train a Multi-Armed Bandit Model with Vowpal Wabbit
• Test 2 BERT Models with a Bandit
• DEMO: Scale Multi-Armed Bandits on AWS
6. Traditional A/B Tests
• Static
• Cannot Add New Models After Test Begins
• Static Traffic Split Between Models A and B
• May Negatively Impact Business Metrics
• Must Run Experiment to Completion
• No Concept of Reward for Winning Model
7. Multi-Armed Bandit Tests
• Add New Models
• Dynamically Shift Traffic
• Explore-Exploit Strategy
• Finish Experiment Early - or Run Longer!
• Minimize Regret (Business Impact)
• Maximize Reward
8. Train 2 BERT Models with TensorFlow (Models A & B)
• BERT Mania!
• Fine-Tuning BERT
9. Train a Bandit Model with Reinforcement Learning (RL)
• Popular Reinforcement Learning Strategies
• Epsilon Greedy
• Thompson’s Sampling
• Online Cover
• Bagging
• Implemented in Vowpal Wabbit (VW)!
• Try Our Open Source RL Containers
• https://github.com/aws/sagemaker-rl-container
10. Test 2 BERT Models with a Multi-Armed Bandit Model