Modern Machine Learning Infrastructure and Practices

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Slides from Curtis Huang's talk at the Couchbase Meetup in Mountain View on August 18th. Curtis is a Senior Software Engineer at Facebook working on Machine Learning, with experience in both ad tech and search. "AI and machine learning have transformed the technology industry for the last decade, creating a foundation for web search, ranking/recommendation, and object/speech recognition. In this talk, I will discuss a collection of machine learning approaches to effectively analyzing and modeling large-scale data. From a hands-on practitioner's perspective, I will talk about the process of building a ML pipeline from idea to production, the challenges, and lessons learned. As an example, I will describe the infrastructure and components of a modern ML ranking system."

Software

Machine Learning
Curtis Huang
curtishuang@fb.com

•  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?

•  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
ML
Framework
Distributed
KV-Store
Snapshot Realtime
Features
Algorithm
Service
Logging
Service

•  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

•  ConvNet for computer vision tasks
•  Network architecture
Krizhevsky et. al. (2011)

•  Expensive computation in training (clusters, GPUs)
•  Interpretability of model
•  Power consumption
Challenges

Thank You
Curtis Huang
curtishuang@fb.com

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Modern Machine Learning Infrastructure and Practices

1. Machine Learning Curtis Huang curtishuang@fb.com

2. •  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

3. •  Advantages from mining/learning patterns in data •  Cost of Storage and Compute •  Distributed Systems Machine Learning Why Now?

4. What People Think

5. Reality

6. •  Speciﬁc Tasks •  Quality Data •  Feature Engineering •  Iterations of Experiments ML Today Domain Knowledge StatisticsEngineering

7. 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

8. Data Algorithm Train Model Fault Tolerant Deployment ML Workﬂow

9. Example of a ML System Datastore ETL Ad-hoc Analysis ML Framework Distributed KV-Store Snapshot Realtime Features Algorithm Service Logging Service

10. •  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

11. Deep Learning

12. Word2Vec[1] in Spark [1]Mikolov et al.

13. •  ConvNet for computer vision tasks •  Network architecture Krizhevsky et. al. (2011)

14. Reed et. al. 2016

15. •  Expensive computation in training (clusters, GPUs) •  Interpretability of model •  Power consumption Challenges

16. Thank You Curtis Huang curtishuang@fb.com