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How to build analytical models with a factory approach

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Good news! Your analytical model is ready for production. Bad news. You needed it three months ago. Sound familiar? Often, models take so long to implement because companies produce and deploy them in two separate environments – business and IT – that rely on very different processes. This paper describes how taking a factory approach to building models will enable you to meet the needs of both business analysts and IT. Not sure what a “factory approach” is? Don’t worry. The paper describes that too.

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How to build analytical models with a factory approach

  1. 1. Copyright © SAS Institute Inc. All rights reserved. Breaking Down the Barriers Between Model Development and Deployment How a Factory Approach Speeds Time to Market
  2. 2. Copyright © SAS Institute Inc. All rights reserved. The Problem It takes too long to efficiently deploy models across your business and get a return on your investment. Let’s Change That™
  3. 3. Copyright © SAS Institute Inc. All rights reserved. A model’s value lies in how quickly it’s deployed In every case, models only drive action when they’re efficiently integrated into production systems. Predictive models play a role in different industries: Retail banking Health care Government Retail
  4. 4. Copyright © SAS Institute Inc. All rights reserved. Why does it take so long to get a model through production?
  5. 5. A tale of two environments Sandbox versus production Model development and deployment often happens in two different environments. The problem: IT has to convert and adapt models as they move from sandbox to promotion.
  6. 6. The answer: automation The answer: Move to a single architecture that removes the need to convert models – and automates some routine tasks.
  7. 7. Copyright © SAS Institute Inc. All rights reserved. 7 benefits of a factory approach
  8. 8. Copyright © SAS Institute Inc. All rights reserved. 1. Dedicated analytics data store At its heart, an analytics factory is a dedicated analytics production data store that also handles the complete model life cycle. Everything is handled within a single production environment that feeds into the data warehouse.
  9. 9. Copyright © SAS Institute Inc. All rights reserved. 2. Value for Hadoop: analytical base tables Describing a company’s interactions can grow the number of columns quickly, sometimes into the tens of thousands. But Hadoop can easily handle those wide ATBs, which makes it a great fit for the analytics factory.
  10. 10. Copyright © SAS Institute Inc. All rights reserved. 3. Reuse of feature development code in production An analytics factory reuses the feature code in its native form, without the need for a manual conversion process. This is a migration rather than a conversion, so that when you deploy your model, the logic you need to build the ABT is already there in production.
  11. 11. Copyright © SAS Institute Inc. All rights reserved. 4. Simplified integration of new logic An analytics factory is a continually evolving environment. As you create new models, your analytics factory automatically incorporates the data sources and logic of those models into the production process.
  12. 12. Copyright © SAS Institute Inc. All rights reserved. 5. Scoring and decision interfaces As you deploy predictive models to the analytics factory, you’ll want to make sure those models deliver their output of scores, decisions or data streams to other systems. An analytics factory sets up the following interfaces: Publish-subscribe On-demand/near-real time Real-time Streaming
  13. 13. Copyright © SAS Institute Inc. All rights reserved. 6. Containers to further speed deployment Containers allow applications and models to look uniform as they cross between non-production and production environments, or even between on-site and cloud. This makes it possible for many teams of modelers to share the same infrastructure.
  14. 14. Copyright © SAS Institute Inc. All rights reserved. 7. Production hardening An analytics factory validates a model’s scoring logic using a systematic template and process to record each test the scoring engine goes through. This ensures that a model’s logic is sound before the model goes into production.
  15. 15. Copyright © SAS Institute Inc. All rights reserved. Why you should implement an analytics factory
  16. 16. Copyright © SAS Institute Inc. All rights reserved. Significant cost savings An analytics factory drives real cost savings by dramatically shortening the model deployment process and reducing intensive manual IT costs.
  17. 17. Copyright © SAS Institute Inc. All rights reserved. Top-line and bottom-line value from analytics At analytics factory powers timely and relevant customer interactions and decisions, increasing sales and return on marketing. It also reduces fraud and credit losses, speeds up product time to market and shortens supply chains.
  18. 18. Copyright © SAS Institute Inc. All rights reserved. Increased innovation Containers’ current analytical process makes it hard to innovate. In contrast, an analytics factory embraces innovation by tying data scientists and analysts more closely to deployment.
  19. 19. Copyright © SAS Institute Inc. All rights reserved. Cultural change An analytics factory not only fosters collaboration between business and IT, it breaks down the barriers between model development and production. As a result, it nurtures a culture of agile development and continuous deployment.
  20. 20. sas.com Copyright © SAS Institute Inc. All rights reserved. Get started with your own analytics factory Download a free copy

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