analytics model monitoring

You’ve even taken the next step – often one of the least spoken about – of putting your model into production (or model deployment). So, it is safe to say that in today’s world, the “auto” in auto-healing is almost non-existent for all practical purposes. How to Evaluate and Update Your Predictive Analytics Model, How to Create a Supervised Learning Model with Logistic Regression, How to Explain the Results of an R Classification Predictive…, How to Define Business Objectives for a Predictive Analysis Model, How to Choose an Algorithm for a Predictive Analysis Model, By Anasse Bari, Mohamed Chaouchi, Tommy Jung. 0000001163 00000 n Amazon SageMaker Model Monitor currently supports only endpoints that host a single 197 0 obj <> endobj schema constraints and statistics for each feature using Deequ, an open source library He has made significant contributions to the field of data sciences for close to two decades now, which include 50+ patents (filed/granted) 50+ international publications and multi-million dollar top-line / bottom line impact across various business verticals. 0000008476 00000 n Since the emphasis of this paper is on monitoring the model performance and not on the model itself, we won’t show the results of the model. (adsbygoogle = window.adsbygoogle || []).push({}); This article is quite old and you might not get a prompt response from the author. The data science team thus needs to be cautious when accepting the rectifications suggested by the reactive model maintenance process as those recommendations can possibly be detrimental to a wide range of data samples. There is a popular and dangerously incorrect myth about machine learning models that they auto-heal. endstream endobj 198 0 obj<. 0000001296 00000 n 0000003136 00000 n Automated monitoring saves time and helps you avoid errors in tracking the model’s performance. Post-deployment monitoring is a crucial step in any machine learning project, Learn from an experienced machine learning leader about the various aspects of post-model production monitoring, A Quick Recap of this Data Science Thought Leaders Series, Identify all the data samples which have at least one word not seen in the training data, Identify all the data samples which are at least N-words longer or M words shorter than the average number of words in the training data, A module for audio analysis of the raw speech input to identify the sentence type (i.e., statement, question, exclamation or command), A module for text analysis of the transcribed speech input to identify the semantic message, and, A module that combines the output of the other two modules to identify the intent, Data science cannot generate impact in isolation and that the entire organization has to be trained into a ‘data-culture’, which of course is easier said than done, and, Years of concerted efforts by data experts have gone into building the consumer-AI applications that are gaining popularity in the media of late. how to analyze it, and which reports to produce. Given these wide varieties of sources that may lead to a drop in performance of the ML-systems over time and the intense pressure to fix the issues within a given SLA, it can be tempting to have a ‘thin-layer-of-rules’ which bypasses the ML machinery completely to address the immediate customer escalation. Learn from an experienced machine learning leader about the various aspects of post-model production monitoring

0000012892 00000 n An area I would love to discuss more, but not in this series. enables developers to set alerts for when there are deviations in the model quality,

The business teams and the customer-facing teams may, in such cases, make a decision that certain customers will continue to get the old speech recognition system while the other customers will be migrated to the newer one.
I would absolutely love to hear your thoughts on this. Please refer to your browser's Help pages for instructions. For instance, any machine learning solution can be thought of as a combination of multiple elemental ML components. But there are a variety of changes which are difficult to catch, have a substantially detrimental impact on the output of the machine learning system and unfortunately are not uncommon. To add to the mix, a lot of the times the end client may prefer receiving consistent output over a now-correct-but-earlier-incorrect output. Thanks for letting us know we're doing a good 0000014598 00000 n In particular, the expectation is that a machine learning model will continuously and automatically identify where it makes mistakes, find optimal ways to rectify those mistakes, and incorporate those changes in the system, all with almost no human intervention. 197 42 0000012544 00000 n

real-life data that is not carefully curated like most training datasets. Amazon SageMaker Model Monitor does support monitoring inference pipelines, but capturing All data samples with a probability lower than a certain threshold can be marked as ‘non-representative’  (i.e., anomalous) and sent to the domain experts for further investigation.

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