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MLOps and Data Quality: Deploying Reliable ML Models in Production

Looking to build a robust machine learning infrastructure to streamline MLOps? Learn from Provectus experts how to ensure the success of your MLOps initiative by implementing Data QA components in your ML infrastructure.

For most organizations, the development of multiple machine learning models, their deployment and maintenance in production are relatively new tasks. Join Provectus as we explain how to build an end-to-end infrastructure for machine learning, with a focus on data quality and metadata management, to standardize and streamline machine learning life cycle management (MLOps).

Agenda
- Data Quality and why it matters
- Challenges and solutions of Data Testing
- Challenges and solutions of Model Testing
- MLOps pipelines and why they matter
- How to expand validation pipelines for Data Quality


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MLOps and Data Quality: Deploying Reliable ML Models in Production

  1. 1. MLOps and Data Quality: Deploying Reliable ML Models in Production Presented by: Stepan Pushkarev, CTO @ Provectus Rinat Gareev, ML Solutions Architect @ Provectus
  2. 2. Webinar Objectives 1. Explore best practices of building and deploying reliable Machine Learning models 2. Review existing open source tools and reference architectures for implementation of Data Quality components as part of your MLOps pipelines 3. Get qualified for Provectus ML Infrastructure Acceleration Program – A fully funded discovery workshop
  3. 3. Agenda ● Introduction and Why ● How: Common Practical Challenges and Solutions ○ Data Testing ○ Model Testing ● MLOps: Wiring Things Together ● Provectus ML Infrastructure Acceleration Program
  4. 4. Introductions Stepan Pushkarev Chief Technology Officer, Provectus Rinat Gareev ML Solutions Architect, Provectus
  5. 5. AI-First Consultancy & Solutions Provider Сlients ranging from fast-growing startups to large enterprises 450 employees and growing Established in 2010 HQ in Palo Alto Offices across the US, Canada, and Europe We are obsessed about leveraging cloud, data, and AI to reimagine the way businesses operate, compete, and deliver customer value
  6. 6. Innovative Tech Vendors Seeking for niche expertise to differentiate and win the market Midsize to Large Enterprises Seeking to accelerate innovation, achieve operational excellence Our Clients
  7. 7. Why Quality Data Matters? After Data Cleaning 0.91 TFIDF, PoS, Stop Words 0.695 Scikit Learn Default 0.69 Python Hyperopt 0.73 ACCURACY Sigmod2016 Sanjay Krishnan (UC Berkeley) And Jiannan Wang (Simon Fraser U.) https://sigmod2016.org/sigmod_tutorial1.shtml
  8. 8. End-to-end deep learning image classification models to detect child gaze, strabismus, crescent, and dark iris/pupil population. GoCheck Kids Case Study Before After Data QA Precision 32% 40% Recall 89% 91% FPR 19% 17% PR AUC 57% 76%
  9. 9. Machine Learning Lifecycle Data Ingestion Data Cleaning Data Merging Data Labeling Feature Engineering Versioned Dataset Model Training Experimentation Model Packaging Model Candidate Regression Testing Model Selection Production Deployment Monitoring Data Preparation ML Engineering Delivery & Operations
  10. 10. All Stages of ML Lifecycle Require QA Data Ingestion Data Cleaning Data Merging Data Labeling Feature Engineering Versioned Dataset Model Training Experimentation Model Packaging Model Candidate Regression Testing Model Selection Production Deployment Monitoring Data Preparation ML Engineering Delivery & Operations Data Tests Code Tests Model Tests Data Tests Code Tests Model Tests Data Tests Code Tests
  11. 11. Error Cascades * from "Everyone wants to do the model work, not the data work": Data Cascades in High-Stakes AI”, N. Sambasivan et al., SIGCHI, ACM (2021)
  12. 12. How: Practical Challenges and Solutions
  13. 13. Common Challenge #1: How to find & access the data I trust? 1. Data is scattered across multiple data sources and technologies: RDMS, DWH, Data Lakes, Blobs 2. Data ownership is not clear 3. Data requirements and SLAs are not clear 4. Metadata is not discoverable 5. As a result, all investments into Data and ML are killed by data access and discoverability issues
  14. 14. Solution: Migrate to Data Mesh Data Mesh is in the convergence of Distributed Domain-Driven Architecture, Self- Serve Platform Design, and Product Thinking with Data ● Brings data closer to Domain Context ● Introduces the concept of Data as a Product and all appropriate data contracts ● Sorts out data ownership issues https://martinfowler.com/articles/data-monolith-to-mesh.html
  15. 15. Invest into Global Data Catalog The solution to answer questions like: ● Does this data exist? Where is it? ● What is the source of truth of the data? ● Who and/or which team is the owner? ● Who are the users of the data? ● Are there existing assets I can reuse? ● Can I trust this data? * There are no established leaders * Commercial vendors are not listed
  16. 16. Common Challenge #2: How to get started with QA for Data and ML? 1. What exactly to test? 2. Who should test (Traditional QA, Data Engs, ML Engs, Analysts)? 3. What tools to use? 4. As a result, low productivity of ML Engineers having to deal with data quality issues.
  17. 17. Data: What to Test Default data quality checks: ● Duplicates ● Missing values ● Syntax errors ● Format errors ● Semantic errors ● Integrity
  18. 18. Advanced unsupervised methods: ● Distribution tests ● KS, Chi-squared tests ● Outlier detection with AutoML ● Auto Constraints suggestion ● Data Profiling for Complex Dependencies Default data quality checks: ● Duplicates ● Missing values ● Syntax errors ● Format errors ● Semantic errors ● Integrity checks Data: What to Test
  19. 19. Unsupervised Constraints Generation Use cases: ● existing data with poor documentation or schema ● rapidly evolving data ● rich structure ● starting from scratch 1. Compute data profiles/summaries 2. Generate checks on: ● types ● completeness ● ranges ● uniqueness ● distributions Extensible: ● e.g., conventions on column naming 3. Evaluate on holdout subset 4. Review and add to test suites
  20. 20. ● Deequ ● GreatExpectations ● Tensorflow Data Validation ● dbt Data Testing: Available Tools * Commercial vendors are not listed
  21. 21. Model Testing
  22. 22. Model Testing: Analyzing Input and Output Datasets
  23. 23. Model Testing: Datasets Are Test Suites with Test Cases ● Golden UAT datasets ● Security datasets ● Production traffic replay ● Regression datasets ● Datasets for bias ● Datasets for edge cases
  24. 24. Model Testing: Bias Bias is considered to be a disproportionate inclination or prejudice for or against an idea or thing.
  25. 25. 10+ Bias Types ● Selection Bias — The selection of data in such a way that the sample is not representative of the population ● The Framing Effect — Annotation questions that are constructed with a particular slant ● Systematic Bias — Consistent and repeatable error. ● Outlier Data, Missing Values, Filtering Data ● Bias / Variance Trade off ● Personal Perception Bias
  26. 26. Model Testing: Available Tools Adversarial Testing & Model Robustness: 1. Cleverhans by Ian Goodfellow & Nicolas Papernot 2. Adversarial Robustness Toolbox (ART) by DARPA Bias and Fairness 1. AWS SageMaker Clarify 2. AIF360 by IBM 3. Aequitas by University of Chicago
  27. 27. MLOps: Wiring Things Together
  28. 28. The Core of MLOps Pipelines Model Code ML Pipeline Code Infrastructure as a Code Versioned Dataset Production Metrics & Alerts Model Artifacts Prediction Service ML Metrics Automated Pipeline Execution Pipeline Metadata Alerts Reports Feature Store Orchestration: Idempotent Execution Feedback Loop for Production Data
  29. 29. The Core of MLOps Pipelines Model Code ML Pipeline Code Infrastructure as a Code Versioned Dataset Production Metrics & Alerts Model Artifacts Prediction Service ML Metrics Automated Pipeline Execution Pipeline Metadata Alerts Reports Feature Store Orchestration: Idempotent Execution Feedback Loop for Production Data Data Quality Checks
  30. 30. Expanding Validation Pipelines Feature Store ML Model Versioned Dataset Batch Quality Checkpoints Dataset Rules Validation Dataset Bias Checker Statistical Assertions Outlier Detector Deployed Model Model Validation Model Test for Bias Model Security Test Regression Test Business Acceptance Traffic Replay
  31. 31. 1. You cannot deploy ML models to production without a clear Data QA Strategy in place. 2. As a leader, focus on organizing data teams around product features, to make them fully responsible for Data as a Product. 3. Design Data QA components as an essential part of your MLOps foundation. Final Recommendations
  32. 32. 125 University Avenue Suite 295, Palo Alto California, 94301 provectus.com Questions, details? We would be happy to answer!

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Looking to build a robust machine learning infrastructure to streamline MLOps? Learn from Provectus experts how to ensure the success of your MLOps initiative by implementing Data QA components in your ML infrastructure. For most organizations, the development of multiple machine learning models, their deployment and maintenance in production are relatively new tasks. Join Provectus as we explain how to build an end-to-end infrastructure for machine learning, with a focus on data quality and metadata management, to standardize and streamline machine learning life cycle management (MLOps). Agenda - Data Quality and why it matters - Challenges and solutions of Data Testing - Challenges and solutions of Model Testing - MLOps pipelines and why they matter - How to expand validation pipelines for Data Quality

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