Production and Beyond: Deploying and Managing Machine Learning Models
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1) Deploying machine learning models into production involves evaluating, monitoring, deploying, and managing models over their lifecycle. 2) Evaluation involves continuously tracking metrics on both historical and live data to determine when models need to be updated. Monitoring involves choosing between existing models, such as by using A/B testing or multi-armed bandits. 3) Dato provides tools to simplify each stage of the machine learning lifecycle from batch training to real-time predictions to continuous evaluation and management of models in production.
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Production and Beyond: Deploying and Managing Machine Learning Models
- 1. Production and Beyond: Rajat Arya, Senior Product Manager Alice Zheng, Director of Data Science 1 Deploying and Managing Machine Learning Models
- 2. Choosing between deployed models. What is Production? Evaluation Monitoring Deployment Management Easily serve live predictions. Measuring quality of deployed models. Tracking model quality & operations.
- 3. Lifecycle of ML in Production Evaluation Monitoring Deployment Management
- 4. Lifecycle of ML in Production Evaluation Monitoring Deployment Management
- 5. The Setup Suppose we are building a website with product recommendations, trained using Amazon reviews. • 34.6M reviews • 2.4M products • 6.6M users
- 6. Deployment System Model Historical Data Predictions Live Data Batch training Real-time predictions Feedback
- 7. Deployment System Model Historical Data Batch training Real-time predictions input recommendations
- 8. Batch Training: DIY • Use entire cluster efficiently • Scale nodes up or down • No data transfer • Operation metrics, dashboard, alarming 8 Scalable Distributed Easy to schedule, launch, and monitor Co-located with Data Model Historical Data Batch training
- 9. Dato Distributed Dato Distributed Architecture GraphLab Create
- 10. Dato Distributed In one line of code launch a long-running cluster of machines to do parallel / distributed execution from GraphLab Create.
- 11. Deployment System Model Historical Data Predictions Live Data Batch training Real-time predictions
- 12. Real-time Predictions: DIY • REST endpoints, language independence • Fast predictions, with caching • Replicated models • Alarming, Metrics • Scale up or down • Easy to update / deploy models 12 Low Latency Ease of Integration Scalable Fault Tolerant Maintainable Predictions Live Data Real-time predictions
- 13. Dato Predictive Services Architecture Dato Predictive Services website, mobile, browser, etc REST API DIST. CACHE MODEL
- 14. Dato Predictive Services In one line of code launch a fault-tolerant, scalable, robust, and maintainable cluster, putting a service-oriented architecture on machine learning models.
- 15. Dato Predictive Services Benchmark Amazon review dataset (34.6M reviews, 2.4mi users, 6.6mi products). Measured end to end latency Average: < 65ms P99: < 100ms AWS m3.xlarge instances • 4 cores • 15 GB RAM 3 Node deployment Moderate Load Generated
- 16. Recommendation System Model Historical Data Predictions Live Data Batch training Real-time predictions
- 17. Lifecycle of ML in Production Evaluation Monitoring Deployment Management
- 18. What happens after (initial) deployment
- 19. ML production life cycle Evaluation Monitoring Deployment Management
- 20. After deployment Evaluate and track metrics over time. React to feedback from deployed models. MonitoringManagementEvaluation
- 21. ML in production - 101 Model Historical Data Predictions Live Data Feedback Batch training Real-time predictions
- 22. ML in production - 101 Model Historical Data Real-time predictionsBatch training Predictions Model 2 Live Data
- 23. Key questions • When to update a model? • How to choose between existing models? • Answer: continuous evaluation and testing
- 24. What is evaluation? Predictions Metric + Evaluation What data? Which metric?
- 25. Evaluating a recommender Model Historical Data Predictions Live Data Ranking loss User engagement
- 26. Evaluating a recommender Model Historical Data Predictions Live Data Ranking loss User engagementOffline evaluation: When to update model Online evaluation: Choosing between models
- 27. Updating ML models Why update? • Trends and user tastes change over time • Model performance drops When to update? • Track statistics of data over time • Monitor both offline & online metrics on live data • Update when offline metric diverges from online metrics
- 28. Choosing between ML models Model 2 Model 1 2000 visits 10% CTR Group A Everybody gets Model 2 2000 visits 30% CTR Group B Strategy 1: A/B testing—select the best model and use it all the time
- 29. Choosing between ML models A statistician walks into a casino… Pay-off $1:$1000 Pay-off $1:$200 Pay-off $1:$500 Play this 85% of the time Play this 10% of the time Play this 5% of the time Multi-armed bandits
- 30. Choosing between ML models A statistician walks into an ML production environment Pay-off $1:$1000 Pay-off $1:$200 Pay-off $1:$500 Use this 85% of the time (Exploitation) Use this 10% of the time (Exploration) Use this 5% of the time (Exploration) Model 1 Model 2 Model 3
- 31. MAB vs. A/B testing Why MAB? • Continuous optimization, “set and forget” • Maximize overall reward Why A/B test? • Simple to understand • Single winner • Tricky to do right
- 32. Other production considerations • Versioning • Logging • Provenance • Dashboards • Reports “Machine learning: The high interest rate credit card of technical debt,” D. Sculley et al, Google, 2014 “Two big challenges in machine learning,” Leon Bottou, ICML 2015 invited talk
- 33. Conclusions Evaluation Monitoring Deployment Management Dato Distributed & Dato Predictive Services A/B testing, multi-armed bandits & much more Dato – one stop shop for all stages of the ML lifecycle Simple, platform agnostic interface @datoinc, #DataSmt
Editor's Notes
- For both hyper-parameter tuning and model training the system we are looking for should be: Distributed Do embarrassingly parallel things in parallel Do ML things that distribute well in parallel Scalable Scale nodes up or down Co-located with the data Execution happens where the data lives Easy to schedule, launch, and monitor Same code as model Operational metrics, dashboard, alarming Easy integration with Dato Predictive Services
- Why architecture meets requirements? Distributed, Scalable, Co-located with data, easy to schedule/launch/monitor
- Dato Distributed - one command to launch a long-running cluster of machines to do parallel / distributed execution of Jobs from GraphLab Create. These clusters can be launched in the cloud in AWS EC2 or on-premise in Hadoop YARN or Spark clusters.
- Meets requirements: Ease of integration Low Latency Fault Tolerant Scalable Maintainable
- Dato Predictive Services - with one line we deploy a fault-tolerant, scalable, robust, and maintainable cluster to put a service-oriented architecture on machine learning models. We can choose to deploy in AWS EC2, or on-premise in our Hadoop YARN or Spark cluster environments.
- We launch a 3-node Predictive Service deployment in AWS EC2 instances (m3.xlarge with 4 cores and 15gb RAM) and deploy our Recommender model. I should mention, our recommender model has 2.4mi users, and 6.6mi products (say). We measure operational metrics for getting a real-time live set of recommendations for a given user, and see average latency < 65ms. And of course average round-trip latency is insufficient for a production system, so we measure 99th percentile, P99 latency, and see it is < 100ms.
- For both hyper-parameter tuning and model training the system we are looking for should be: Distributed Do embarrassingly parallel things in parallel Do ML things that distribute well in parallel Scalable Scale nodes up or down Co-located with the data Execution happens where the data lives Easy to schedule, launch, and monitor Same code as model Operational metrics, dashboard, alarming Easy integration with Dato Predictive Services
- For periodic training and distributed feature engineering we decide to use our on-premise Spark cluster, since our data is already stored on HDFS and we already use Spark DataFrames in our data engineering pipeline. So we schedule a nightly job to take the historical data and train our FactorizationRecommender model as a Spark Job, and as part of that job the trained model is updated on the Predictive Service deployment we launched earlier. And with Dato Distributed installed on the Spark cluster, our data scientists can now run distributed hyperparameter tuning regularly, and find an optimal set of parameters for the FactorizationRecommender model.
- So far so good, we get together in a meeting with the web team to get the frontend to start using the new recommendation system we've developed. In that meeting, one of the data scienstists on the team mentions, 'I didn't tune the hyperparameters on that recommender model, there is probably an opportunity to get better results from the model.' One of the software engineers asks, 'How regularly will the model be trained?' Uh-oh, we hadn't thought of those things. The web team puts the frontend REST API work on their next sprint and the data science team goes back to think about how to incorporate hyperparameter tuning and frequent model training into the recommender application.