{"id":2474,"date":"2022-10-05T19:39:58","date_gmt":"2022-10-05T19:39:58","guid":{"rendered":"https:\/\/admin.acceleratingscience.com\/connectedlab\/?p=2474"},"modified":"2025-01-21T09:52:08","modified_gmt":"2025-01-21T09:52:08","slug":"operationalizing-machine-learning-in-the-laboratory","status":"publish","type":"post","link":"https:\/\/www.thermofisher.com\/blog\/connectedlab\/operationalizing-machine-learning-in-the-laboratory\/","title":{"rendered":"Operationalizing Machine Learning in the Laboratory"},"content":{"rendered":"<p>In previous blogs we have shown how <a href=\"\/connectedlab\/how-can-laboratory-data-analysis-improve-my-business\/\" target=\"_blank\" rel=\"noopener\">Data Analytics Solutions<\/a> can be utilized in many different laboratory scenarios to help identify bottlenecks using Business Intelligence Capability, and then reduce testing and forecast sample throughput using the Machine Learning Capability. In this blog we will show how the Machine Learning (ML) models that have been built can be automatically trained, and how they can be operationalized within Thermo Scientific\u2122 SampleManager\u2122 LIMS itself \u2013 without the need to utilize external applications or platforms.<\/p>\n<p>As the <a href=\"https:\/\/www.thermofisher.com\/uk\/en\/home\/digital-solutions\/lab-informatics\/lab-information-management-systems-lims\/solutions\/samplemanager\/data-analytics.html\">Data Analytics Solution<\/a> is embedded within SampleManager LIMS, it has several advantages from an operationalization perspective, namely &#8211;<\/p>\n<ul>\n<li>All data remains within SampleManager LIMS \u2013 which aids data governance<\/li>\n<li>The data is always up to date<\/li>\n<li>ML models are version controlled<\/li>\n<li>ML models access control and data is governed through roles and groups<\/li>\n<li>Retraining can be automatically scheduled from within SampleManager LIMS<\/li>\n<li>Predictions can run from workflow and so are triggered automatically<\/li>\n<\/ul>\n<p>From the MLOps perspective, the ML model is developed, trained, deployed and monitored within SampleManager LIMS.<\/p>\n<p>Each ML model has several flags which control its visibility at each stage of the MLOps process. For a ML model to be deployed it needs to be flagged as an active model and be flagged for automated training.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-2476 size-full\" src=\"http:\/\/admin.acceleratingscience.com\/connectedlab\/wp-content\/uploads\/sites\/14\/2022\/10\/DH1.jpg\" alt=\"\" width=\"786\" height=\"127\" srcset=\"https:\/\/admin.acceleratingscience.com\/connectedlab\/wp-content\/uploads\/sites\/14\/2022\/10\/DH1.jpg 786w, https:\/\/admin.acceleratingscience.com\/connectedlab\/wp-content\/uploads\/sites\/14\/2022\/10\/DH1-300x48.jpg 300w, https:\/\/admin.acceleratingscience.com\/connectedlab\/wp-content\/uploads\/sites\/14\/2022\/10\/DH1-768x124.jpg 768w\" sizes=\"auto, (max-width: 786px) 100vw, 786px\" \/><\/p>\n<p>The SampleManager LIMS Background Scheduler is used to retrain the ML models flagged as available for automated training. In this example the models are retained every Tuesday and Sunday at 16:35.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-2478 \" src=\"http:\/\/admin.acceleratingscience.com\/connectedlab\/wp-content\/uploads\/sites\/14\/2022\/10\/DH3.jpg\" alt=\"\" width=\"552\" height=\"515\" srcset=\"https:\/\/admin.acceleratingscience.com\/connectedlab\/wp-content\/uploads\/sites\/14\/2022\/10\/DH3.jpg 604w, https:\/\/admin.acceleratingscience.com\/connectedlab\/wp-content\/uploads\/sites\/14\/2022\/10\/DH3-300x280.jpg 300w\" sizes=\"auto, (max-width: 552px) 100vw, 552px\" \/><\/p>\n<h2>Results evaluation<\/h2>\n<p>It is important that the performance of each ML model is tracked as new data is used to retrain each model. The quality of the <a href=\"https:\/\/www.thermofisher.com\/blog\/connectedlab\/lims-enables-scientists-to-close-in-on-edge-ai\/\">ML model<\/a> is dependent upon the quality of the labelled data used to train it, so over time the quality may vary. The performance of the ML model against the labelled trained data can be tracked through the results evaluation page.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-2479 size-full\" src=\"http:\/\/admin.acceleratingscience.com\/connectedlab\/wp-content\/uploads\/sites\/14\/2022\/10\/DH4.jpg\" alt=\"\" width=\"1270\" height=\"770\" srcset=\"https:\/\/admin.acceleratingscience.com\/connectedlab\/wp-content\/uploads\/sites\/14\/2022\/10\/DH4.jpg 1270w, https:\/\/admin.acceleratingscience.com\/connectedlab\/wp-content\/uploads\/sites\/14\/2022\/10\/DH4-300x182.jpg 300w, https:\/\/admin.acceleratingscience.com\/connectedlab\/wp-content\/uploads\/sites\/14\/2022\/10\/DH4-1024x621.jpg 1024w, https:\/\/admin.acceleratingscience.com\/connectedlab\/wp-content\/uploads\/sites\/14\/2022\/10\/DH4-768x466.jpg 768w\" sizes=\"auto, (max-width: 1270px) 100vw, 1270px\" \/><\/p>\n<p>When the results for a new sample have been entered, workflow can be used to automatically perform a prediction using a given ML model.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-2480 size-full\" src=\"http:\/\/admin.acceleratingscience.com\/connectedlab\/wp-content\/uploads\/sites\/14\/2022\/10\/DH5.jpg\" alt=\"\" width=\"937\" height=\"298\" srcset=\"https:\/\/admin.acceleratingscience.com\/connectedlab\/wp-content\/uploads\/sites\/14\/2022\/10\/DH5.jpg 937w, https:\/\/admin.acceleratingscience.com\/connectedlab\/wp-content\/uploads\/sites\/14\/2022\/10\/DH5-300x95.jpg 300w, https:\/\/admin.acceleratingscience.com\/connectedlab\/wp-content\/uploads\/sites\/14\/2022\/10\/DH5-768x244.jpg 768w\" sizes=\"auto, (max-width: 937px) 100vw, 937px\" \/><\/p>\n<p>In this example, when all the results have been entered for the \u2018Wine quality\u2019 Test the \u2018Test Completed\u2019 Event is triggered. The \u2018Wine Classification XGBoost\u2019 profiling model is run for that sample and its quality classification predicted. A \u2018Wine Quality Prediction\u2019 Test is then added to the sample and the predicted wine quality value is stored in the \u2018Quality Cat\u2019 Result. This graphical approach allows a workflow to be configured for each customer\u2019s needs. For example it could send an email, or perform a retest if the quality classification is not acceptable.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In previous blogs we have shown how Data Analytics Solutions can be utilized in many different laboratory scenarios to help identify bottlenecks using Business Intelligence Capability, and then reduce testing and forecast sample throughput using the Machine Learning Capability. In this blog we will show how the Machine Learning (ML) models that have been built<\/p>\n","protected":false},"author":1659,"featured_media":2479,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_kad_blocks_custom_css":"","_kad_blocks_head_custom_js":"","_kad_blocks_body_custom_js":"","_kad_blocks_footer_custom_js":"","_monsterinsights_skip_tracking":false,"_genesis_hide_title":false,"_genesis_hide_breadcrumbs":false,"_genesis_hide_singular_image":false,"_genesis_hide_footer_widgets":false,"_genesis_custom_body_class":"","_genesis_custom_post_class":"","_genesis_layout":"","_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_post_was_ever_published":false},"categories":[168,266,121,89,1],"tags":[1034,589,119,1042,100,499,102,861],"division":[],"class_list":{"0":"post-2474","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-in-the-news","8":"category-industry","9":"category-life-science","10":"category-technology","11":"category-uncategorized","12":"tag-automation","13":"tag-connected-lab","14":"tag-data-analysis-and-visualization","15":"tag-data-analytics","16":"tag-digital-lab","17":"tag-lab-of-the-future","18":"tag-laboratory-information-management-system","19":"tag-samplemanager-lims-software","20":"entry"},"_selected_authors":"","_selected_reviewers":"","acf":[],"yoast_head":"<!-- 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