Challenger Models Background Update V2.0

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June 19, 2020
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livefootballscoreLet’s suppose you have a number of machine learning models running in your production environment. Perhaps you are using best free online slots with bonuses MLOps to help you with this effort. Perhaps you are doing it using your own custom-built method. Let’s also suppose that you are monitoring those models using metrics that help you understand their service health, accuracy, and so on. 

winning online poker tournamentsFirst of all, congratulations. You are among the elite 10% of organizations that have actually managed to get this far. The other 90% are still trying to figure out how to get their first model into production and haven’t even started to think about how to monitor them.

tennis open kölnFor the elite 10%, what do you do when your monitoring system tells you, perhaps after a month or two, that a model is deteriorating in terms of accuracy and performance?

soccer goal homeIf this happens to your model, you have a few options at hand:

  1. Replace the deteriorating model with a new one
  2. Retrain the deteriorating model
  3. Rollback to an earlier version of the model

hot sexi indianRegardless of which option you choose, the risk of simply replacing a model that is currently running in production with another model or version is simply bad practice. 

best free online slots with bonuses,So, Why Is This?

women's basketball height hoopEssentially, regardless of how much you train or test a model in your lab environment, the results will still merely represent just an estimation of your model’s behavior once it crosses over to your actual production environment.

best free online slots with bonuses,What Is the Best Way to Address Model Deterioration?

history of handball in ugandaThere are several ways to conduct these important model management activities correctly. Most of them can be characterized as “champion/challenger” best practices.

nike green basketball vestChampion/challenger is a method that allows different approaches to testing predictive models in a production environment. It’s a similar concept to A/B testing from a marketing perspective. The champion/challenger technique enables you to both monitor and measure predictions using different variations of the same decision logic. The overall goal is to identify which variation is the most successful.

volleyball skills images in hdIn a nutshell, we allow for the original model (the champion) and the new or re-trained models (challengers) to shadow the champion model. The challengers will compete against each other for a chance to become the new champion. The results of this production-based competition can then be reviewed, and the MLOps engineer can make a recommendation as to which of the models is the winner (i.e., the existing champion or one of the challengers).

www.live cricket bet 365If this process is performed within a governed system, at this point a designated approver will route her final decision to which model will actually become the new standard (i.e., the new champion). 

maldives vs bahrain live scoreIn addition to understanding the champion/challenger process, it is also important to understand that this is a cyclical process. This means that the activity takes place (or should be taking place) practically all the time, across all production models that are running. This enables a “hot-swap” of models to be undertaken at any given moment, as opposed to waiting for an answer or for testing to complete.

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best free online slots with bonuses,Champion/Challengers Are New to MLOps 6.1

handheld soccer rebounderIn our latest 6.1 release of best free online slots with bonuses , we have added a champion/challenger framework to our MLOps product. This new capability enables best free online slots with bonuses customers, within a governed framework, to run their challenger models in shadow mode, alongside their current best performing model. 

black widow cricket bats cape townFurthermore, best free online slots with bonuses ’s Automated Machine Learning product lets you easily and constantly build high-quality alternative models as potential challengers. You can select a best free online slots with bonuses model or a custom model as a champion, then pick up to three challenger models (which can also be automatically generated by best free online slots with bonuses AutoML, or built externally from best free online slots with bonuses ) to shadow it. 

william hill sign inA key difference with champion/challengers in best free online slots with bonuses ’s MLOps platform is that only one model is ever live at any one time. One hundred percent of prediction requests are serviced by the current champion model. Later on, the same prediction requests are replayed against the challengers for analytical purposes.

tennis racket zurichThis is an important distinction from A/B testing or multi-armed bandit approaches, where you are actually splitting the traffic between different models (i.e., 50% go against one model, 50% against another, both returning predictions to the requesting applications). A/B testing lets you gradually test out a new model and it lets you get better experimental results on the impact to your KPIs of the different models. However, the major drawback of this approach for many organizations is that applications are randomly getting predictions from different models.

a plus tennis cricket batThis means the model variants you are testing need to be acceptable for production (in terms of feature usage, accuracy, and so on). With A/B testing, you cannot compare a maximum accuracy insurance pricing model because you are often prohibited from putting that challenger model into a production environment. And you might not want to run a baseline reference model live because you would leave a lot of accuracy on the table.

tennis shoes europebest free online slots with bonuses ’s shadowing approach lets you safely mirror before taking an approach that impacts your business or your customers. This safety net means you can be more exploratory in the types of models you wish to compare.

MLOps shows how your champion model (this one hand-written in Python) performs over time against three challengers
MLOps shows how your champion model this one hand written in Python performs over time against three challengers

basketball coach vancouverBy feeding the challenger models with the same production data as the champion model, they are activated in parallel, allowing you to compare the champion predictions that actually fed the live business process, to those of the challenger models. You can then further analyze predictions, accuracy, and data errors over time and zoom in on any period in the past.

russia mhl ice hockey predictionsIn addition, this process is tightly governed by MLOps by providing strict approval workflows and audit trails, so that only those who are authorized can propose and analyze challenger models or replace the current champion. Users can also improve the stability of the model replacement process by running proposed replacements in a “shadow mode” before promoting them.

Add new challenger models to test out new and historical predictions and hot swap challengers with the champion
Add new challenger models test out new and historical predictions and hot swap challengers with the champion

midwestern state university tennisMLOps champion/challengers allow you to see what you are leaving on the table in terms of production model options, and to always have a fallback model available. This enables you to react to constantly changing business conditions in order to maintain the highest possible model quality over time. 

best free online slots with bonuses,Want to Know More About MLOps Champion/Challenger Models?

lyon live scoreThis critical new feature is included with your best free online slots with bonuses MLOps license. MLOps champion/challenger models are generally available in Release 6.1 today.

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About the author
Richard Tomlinson
Richard Tomlinson

yappi basketball forumSr Director, Product Marketing, best free online slots with bonuses

orange basketball wallpaperHe works closely with product, marketing, and sales teams to drive adoption and enablement of data management and data engineering capabilities in the best free online slots with bonuses AI platform. Richard has been working in the data warehouse, BI and analytics space for over 20 years with the last eight years focused on Hadoop and cloud platforms. He is based in Chicago but is originally from the UK and has a degree in statistics from the London School of Economics.

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