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Continuous Delivery of Deep Transformer-Based NLP Models Using MLflow and AWS SageMaker for Enterprise AI Scenarios


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Transformer-based pre-trained language models such as BERT, XLNet, Roberta and Albert significantly advance the state-of-the-art of NLP and open doors for solving practical business problems with high-performance transfer learning. However, operationalizing these models with production-quality continuous integration/ delivery (CI/CD) end-to-end pipelines that cover the full machine learning life cycle stages of train, test, deploy and serve while managing associated data and code repositories is still a challenging task. In this presentation, we will demonstrate how we use MLflow and AWS Sagemaker to productionize deep transformer-based NLP models for guided sales engagement scenarios at the leading sales engagement platform,

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Continuous Delivery of Deep Transformer-Based NLP Models Using MLflow and AWS SageMaker for Enterprise AI Scenarios

  1. 1. Continuous Delivery of Deep Transformer-based NLP Models Using MLflow and AWS Sagemaker for Enterprise AI Scenarios Yong Liu Principal Data Scientist Outreach Corporation Andrew Brooks Senior Data Scientist Outreach Corporation
  2. 2. Presentation Outline ➢ Introduction and Background ➢ Challenges in Enterprise AI Implementation ➢ Full LifeCycle ML Experience at Outreach ➢ Conclusion and Future Work
  3. 3. Introduction and Background
  4. 4. 4,000+ Customers
  5. 5. Sales Engagement Platform (SEP) ▪ SEP encodes and automates sales activities into workflows ▪ Enables reps to perform one-on-one personalized outreach to up to 10x Day 1 Phone Call Day 1 Email Day 3 LinkedIn Day 5 Phone Day 5 Email
  6. 6. ML/NLP/AI Roles in Enterprise Sales Scenarios ▪ Continuous learning from data ( emails, phone calls, engagement logs etc.) ▪ Reasoning from knowledge to create a flywheel for the continual success of Reps
  7. 7. Challenges in Enterprise AI Implementation
  8. 8. Implementation Challenges: the Digital Divide Outline Dev-Prod Divide Dev-Prod Differences Challenge 2Challenge 1 Arbitrary Uniqueness Challenge 3 Provenance Challenge 4
  9. 9. Challenge 1: Dev-Prod Divide ▪ can’t test on “live” data ▪ can’t verify model invoked correctly ▪ can’t reproduce bugs or issues reported by users ▪ can’t reuse prod code for model development Isolated prod environment Source: Winderresearch
  10. 10. Challenge 2: Dev-Prod Differences Dev-Prod ▪ training data != prod data ▪ production scoring requires logic not used during model training When training & prod pipelines are and need to be different Source:
  11. 11. when the “whole” is not greater than the “sum of its parts”. Challenge 3: Arbitrary Uniqueness ▪ deploying each model feels like a “special case” ▪ gates and deploy mechanisms are ad hoc ▪ pipeline maintenance is costly Source: Rowperfect UK
  12. 12. Challenge 4: Provenance ▪ don’t know what exactly is running in prod ▪ inability to repro and debug model issues reported by customers ▪ model/pipeline changes = 😬 ▪ undocumented model/code changes compromise metric drifting. ▪ model experiments wasted Provenance from models to source code and data. Source: Slane Cartoons
  13. 13. Full-life Cycle ML Implementation Experience at Outreach
  14. 14. A Use Case: Guided Engagement powered by an intent classification model ▪ ML model predicts the intent of prospect’s email reply and then recommends the right template to respond.
  15. 15. Six Stages of ML Full Life Cycle
  16. 16. Six Stages of ML Full Life Cycle
  17. 17. Model Development and Offline Experimentation MLFlow tracking server to log all offline experiments Tracking experiments
  18. 18. Creating a transformer flavor model A new MLflow model flavor (transformer) & TransformerClassifier (sklearn pipeline) Transformer MLflow model Flavor code TransformerClassifier
  19. 19. Saving and Loading Transformer Artifacts An example of a fully saved and reloadable MLflow “Transformer”-flavor Model Load: mlflow.pyfunc.load_model(model_uri) Save: mlflow_transformer.log_model( transformer_classifier=trained_model, artifact_path="transformer_classifier", conda_env=CONDA_ENV, )
  20. 20. Productionizing Code and Git Repos ▪ MLProject Conda.yml IDE Dev Environment, MLflow MLProject, Github repo structure, flake8
  21. 21. Flexible Execution Mode MLProject allows using code and execution environments either locally or remotely (1)mlflow run ./ -e train (1)mlflow run git+ssh:// -e train --version 1.1 (1)mlflow run ./ -e --backend databricks --backend-config gpu_cluster_type.json (1)mlflow run git+ssh:// -e -- version 1.1 --backend databricks --backend-config gpu_cluster_type.json
  22. 22. Models: trained, wrapped, private-wheeled To support deployment specific logic and environment, we create three progressively evolved models for final deployment in a host (Sagemaker) Fine-tuned Trained Transformer Classifier Pre-score filter Post-score filter Wrapped sklearn pipeline model Private wheeled model No need to access github
  23. 23. Continuous Integration through CircleCI
  24. 24. Continuous Delivery/Rollback through Concourse Gated by two human gates: 1) start the full model deployment 2)promote the model from staging to production; and one regression test gate: accuracy must not be lower than previous version
  25. 25. Model Registry to Track Deployed Model Provenance
  26. 26. How Well Did We Do? Dev/Prod Divide Dev/Prod Differences Arbitrary Uniqueness Provenance + + _
  27. 27. Conclusions and Future Work
  28. 28. Conclusions and Future Work ▪ We highlight four typical enterprise AI implementation challenges and how we solve them with MLflow, Sagemaker and CICD tools ▪ Our intent classification model has been deployed in production and in operation using this framework ▪ Next steps: Incorporating model in-production feedback loop into annotation and model dev cycle We are further improving the annotation pipeline to have seamless human-in-the-loop active learning and model validation
  29. 29. Acknowledgements Data Science Group | Contact: