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• Robotics • Pricing and Optimization • Big Data, Hadoop and Spark • Data Science, ML in Display Advertising • ML, Relevance in Sponsored Search • Contenting Ranking for FB Posts
About Me
• Advantages from mining/learning patterns in data • Cost of Storage and Compute • Distributed Systems
Machine Learning Why Now?
What People Think
Reality
• Specific Tasks • Quality Data • Feature Engineering • Iterations of Experiments
ML Today
Domain Knowledge
StatisticsEngineering
ML Workflow New Hypothesis• Data Analysis• Problem Formulation• Short/Long Term Objectives
Data Preparation• Acquire Data• Synthesize• Clean/Reformat
Feature Engineering• Domain Knowledge• Creativity• Extraction Pipeline
Online Evaluation• Bucket Test• Launch Criteria• Metrics – CTR, Time Spent• Performance Impact
Offline Evaluation• Evaluate on Test Set• Metrics – PR/AUC/NDCG
Model Training• Training Algorithm• Hyper Parameter Tuning• Over-fitting
Data Algorithm Train Model Fault Tolerant Deployment
ML Workflow
Example of a ML System
Datastore
ETL Ad-hoc Analysis
MLFramework
DistributedKV-Store
Snapshot RealtimeFeatures
AlgorithmService
LoggingService
• Ad-hoc Analysis • Adding and Validating New Features • Gap between Online/Offline Metrics • System/Other Issues
Challenges and Lessons Learned 4 V’s of Big Data
Deep Learning
Word2Vec[1] in Spark
[1]Mikolov et al.
• ConvNet for computer vision tasks • Network architecture
Krizhevsky et. al. (2011)
Reed et. al. 2016
• Expensive computation in training (clusters, GPUs) • Interpretability of model • Power consumption
Challenges
