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Machine Learning Techniques and Workflow

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Will Gardella
Will Gardella

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."

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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?
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 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
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
Thank You Curtis Huang curtishuang@fb.com

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