![]() ![]() One day, three months later, someone discovers the bug. Parts of the loan book are sold to external parties and assurances are made to the industry regulator about the level of risk being taken. But there is no dramatic effect, so no alarms are sounded, and a few months go by. This makes it appear that a subset of bank customers are spending more on their lifestyle, which means the bank issues them a slightly worse credit score, and therefore a loan with a slightly higher interest rate than it usually would (the exact specifics don’t matter, the point is, there is a subtle error). Now for example, instead of using a key feature which distinguishes student loan payments, an older version of the feature which doesn’t make the distinction is used. ![]() Perhaps it was a config typo, or maybe the pipeline code had an edge case, or it could have just been a simple breakdown in communication. Somewhere between the data science department and the release, one of the features was created incorrectly. The model is released to customers, where it begins assessing loan applicant credit worthiness. The features are all captured in config and all the tests pass. Jenny is happy that her model performs well, and passes it to one of her ML engineer colleagues who proceeds to write some new code to perform the feature engineering steps in the production ML application. This new model requires a dozen additional features that none of the bank’s other credit risk models use, but it is decided that the improvements in performance warrant the inclusion of these extra features. Jenny is building a new credit risk assessment model. Why should you care about Machine Learning Deployments?Ħ. “Shadow Mode” is one such deployment strategy, and in this post I will examine this approach and its trade-offs. This is particularly true for machine learning systems, where detecting subtle data munging, feature engineering or model bugs in production can be very challenging, particularly when the production data inputs are hard to replicate exactly. But since the publisher hasn’t actually confirmed its inclusion yet, you’ll need to temper your expectations for now while we wait for an official statement.The strategies you adopt when deploying software have the potential to save you from expensive and insidious mistakes. If Activision does end up deciding to add this particular skin to their game, then it will undoubtedly be an immediate hit as evidenced by the popularity of this past Reddit post from a user named leeTheblackReaper. Although still not entirely convincing, this idea seemed to at least persuade the naysayers as they went on to say that they hope it comes as part of “an event reward instead of a crappy bundle.” ![]() Apparently, this other specific scene in the clip sees the Shadow Company character “tac sprinting,” which AI apparently can’t do. However, the post creator went on to exhibit another segment of the trailer that made this outfit’s introduction into Modern Warfare 2 even more likely. ![]() Of course, a few commenters couldn’t help but point out that this may just be a coincidence and its inclusion in the preview doesn’t mean that it will actually be included in the game. Since the game’s release back in October 2022, several players have often asked for the addition of these cosmetics after constantly seeing them throughout the entirety of the game’s campaign.Īccording to a post from an eagle-eyed Redditor named Whole_Carob3178, they noticed that one of the scenes in Activision’s latest trailer included a look at the skin in question. A Modern Warfare 2 Season 4 trailer posted on the official Call of Duty Twitter page has apparently teased the imminent arrival of the long-awaited Shadow Company skin. ![]()
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