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Scaling & Managing Production Deployments with H2O ModelOps

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This presentation was made on June 30th, 2020.

Recording of the presentation is available here: https://youtu.be/9LajqAL_CU8

As enterprises “make their own AI”, a new set of challenges emerge. Maintaining reproducibility, traceability, and verifiability of machine learning models, as well as recording experiments, tracking insights, and reproducing results, are key. Collaboration between teams is also necessary as “model factories” are created for enterprise-wide model data science efforts. Additionally, monitoring of models ensures that drift or performance degradation is addressed with either retraining or model updates. Finally, data and model lineage in case of rollbacks or addressing regulatory compliance is necessary.

H2O ModelOps delivers centralized catalog and management, deployment, monitoring, collaboration, and administration of machine learning models. In this webinar, we learn how H2O can assist with operationalizing, scaling and managing production deployments.

Speaker's Bio:

Felix is a part of the Customer Success team in Asia Pacific at H2O.ai. An engineer and an IIM alumni, Felix has held prominent positions in the data science industry.

Publicado en: Tecnología
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Scaling & Managing Production Deployments with H2O ModelOps

  1. 1. 1 Confidential1 H2O Model Ops Scaling and Managing Deployments H 2 O . a i G u i d a n c e Felix A Customer Success, H2O.ai
  2. 2. 4 Confidential4 Confidential Success of AI Business Impact Aid Decision Making Operationalize and consume ML models
  3. 3. 3 Agenda Overview Challenges around machine learning operations 02 01 03 04 Introduction to Model Ops How H2O.ai can help? Your turn – questions and answers
  4. 4. 3 ML Lifecycle Define Data BuildFinalize Execute Problem statement Solution Definition Acquisition Preparation Feature Engineering Algorithm selection Hyper parameter optimization Validation Documentation Unit Testing Deployment In production Monitoring performance Replacement Retirement
  5. 5. 3 ML Lifecycle - Bottleneck Define Data Build Execute Define Data Build Execute Define Data Build Execute Define Data Build Execute Pre Big Data Era Pre Auto ML Era Current State
  6. 6. 66 Confidential Current State Data Scientists and IT not living in harmony “Perhaps not surprisingly, only 15% have deployed AI broadly into production—because that is where people and process issues come into play.” - NVP Survey of 70 industry leading firms you would recognize http://newvantage.com/wp-content/uploads/2020/01/NewVantage-Partners-Big-Data-and-AI-Executive-Survey-2020-1.pdf Different Mindsets Different competencies Limited Resources Lack of Ownership
  7. 7. 66 Confidential Deployments today Data Scientists productivity gets hit Lots of custom codes and brittle structure IT Ops is uncomfortable in scaling such a process
  8. 8. 10 The Solution Model Ops AKA MLOps or ML Ops New set of technology and practices Actors doing the right role Collaboration between actors Allows organizations to scale AI efforts
  9. 9. 11 ModelOps Production Model Deployment Putting models into production Model Lifecycle Management Keeping models healthy and running in production Production Model Governance Maintaining control of production models and environments © H2O.ai 2020
  10. 10. 12 3 rules of production model deployment 1. Use the technology makes it easy to scale 2. Make machine learning immediately visible to deployment teams 3. Allow for a Dev – Test – Prod approach
  11. 11. 11 ModelOps Production Model Deployment Putting models into production Model Lifecycle Management Keeping models healthy and running in production Production Model Governance Maintaining control of production models and environments © H2O.ai 2020
  12. 12. 13 Model Lifecycle Management Monitor Alert Failover and Fallback Update Testing © H2O.ai 2020
  13. 13. 11 ModelOps Production Model Deployment Putting models into production Model Lifecycle Management Keeping models healthy and running in production Production Model Governance Maintaining control of production models and environments © H2O.ai 2020
  14. 14. 14 Production Model Governance Production Model Governance Role Based Access Control Version control (registry) and rollback Audit trail for access and changes Controls in place to manage risk and comply with regulations © H2O.ai 2020
  15. 15. 3 Demo
  16. 16. 3 ML Ops – So what Easy Collaboration Reduced resources Cost Savings Control measures Governance Minimize risk
  17. 17. 3 ML Lifecycle Define Data BuildFinalize Execute Problem statement Solution Definition Acquisition Preparation Feature Engineering Algorithm selection Hyper parameter optimization Validation Documentation Unit Testing Deployment In production Monitoring performance Replacement Retirement
  18. 18. Confidential18 Confidential18 • Automatic feature engineering, machine learning and interpretability • Fully automated machine learning from ingest to deployment • User licenses on a per seat basis annually • GUI-based interface for end-to-end data science • A new and innovated platform to make your own AI apps • Enterprise commercial software • Easy and intuitive platform to have AI answer your question H2O.ai: AI Platforms In-memory, distributed machine learning algorithms with H2O Flow GUI Open Source H2O Driverless AI H2O Q • 100% open source – Apache V2 Licensed • Integration with Apache Spark • Enterprise support subscriptions • Interface using R, Python on H2O Flow H2O Model Ops • AI deployment platform built for DevOps and MLOps • Scalable to support high throughput and low latency model scoring environments • Comprehensive model monitoring • Drift Detection and retrain
  19. 19. 3 ML Lifecycle Define Data BuildFinalize Execute
  20. 20. Confidential17 Over to you – Questions and Answers
  21. 21. Thank You

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