Imagine that you have collected data once, used it to develop a ML model, then integrated this model into your business infrastructure and now it delivers flawless results. But over time, this model will inevitably lose some accuracy, because the data it was trained on goes obsolete and does not reflect the current business landscape. In this case, your competitors who leverage ML better can now take advantage of the market situation that your currently decayed model has failed to predict.
To avoid this, you initiate this loop again — gather data, update the model, deploy and integrate it. After this, your business scales up and you now need several ML models, each of them ten times larger than this one. How can you keep up with the pace of a large-scale ML model time-to-market? Or rather, how can your company become that competitor that sees the market ahead and reaps the benefits?
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