{"course":{"productid":25768,"modality":1,"active":true,"language":"de","title":"MLOps Engineering on AWS","productcode":"MLOE","vendorcode":"AW","vendorname":"Amazon Web Services","fullproductcode":"AW-MLOE","courseware":{"has_ekit":true,"has_printkit":false,"language":""},"url":"https:\/\/portal.flane.ch\/course\/amazon-mloe","essentials":"<p>Erforderlich:<\/p>\n<ul>\n<li><span class=\"cms-link-marked\"><a class=\"fl-href-prod\" href=\"\/swisscom\/course\/amazon-awse\"><svg role=\"img\" aria-hidden=\"true\" focusable=\"false\" data-nosnippet class=\"cms-linkmark\"><use xlink:href=\"\/css\/img\/icnset-linkmarks.svg#linkmark\"><\/use><\/svg>AWS Technical Essentials <span class=\"fl-prod-pcode\">(AWSE)<\/span><\/a><\/span><\/li><li><span class=\"cms-link-marked\"><a class=\"fl-href-prod\" href=\"\/swisscom\/course\/amazon-awsdevops\"><svg role=\"img\" aria-hidden=\"true\" focusable=\"false\" data-nosnippet class=\"cms-linkmark\"><use xlink:href=\"\/css\/img\/icnset-linkmarks.svg#linkmark\"><\/use><\/svg>DevOps Engineering on AWS <span class=\"fl-prod-pcode\">(AWSDEVOPS)<\/span><\/a><\/span><\/li><li><span class=\"cms-link-marked\"><a class=\"fl-href-prod\" href=\"\/swisscom\/course\/amazon-pdsasm\"><svg role=\"img\" aria-hidden=\"true\" focusable=\"false\" data-nosnippet class=\"cms-linkmark\"><use xlink:href=\"\/css\/img\/icnset-linkmarks.svg#linkmark\"><\/use><\/svg>Practical Data Science with Amazon SageMaker <span class=\"fl-prod-pcode\">(PDSASM)<\/span><\/a><\/span><\/li><\/ul><p>Zus&auml;tzlich Empfohlen:<\/p>\n<ul>\n<li>The Elements of Data Science (digitaler Kurs) oder gleichwertige Erfahrung<\/li><li>Machine Learning Terminology and Process (digitaler Kurs)<\/li><\/ul>","audience":"<ul>\n<li>DevOps Engineers<\/li><li>ML Engineers<\/li><li>Entwickler\/Betriebe mit Verantwortung f&uuml;r die Operationalisierung von ML-Modellen<\/li><\/ul>","contents":"<ul>\n<li>Module 0: Welcome<\/li><li>Module 1: Introduction to MLOps<\/li><li>Module 2: MLOps Development<\/li><li>Module 3: MLOps Deployment<\/li><li>Module 4: Model Monitoring and Operations<\/li><li>Module 5: Wrap-up<\/li><\/ul>","outline":"<h5>Module 0: Welcome<\/h5><ul>\n<li>Course introduction<\/li><\/ul><h5>Module 1: Introduction to MLOps<\/h5><ul>\n<li>Machine learning operations<\/li><li>Goals of MLOps<\/li><li>Communication<\/li><li>From DevOps to MLOps<\/li><li>ML workflow<\/li><li>Scope<\/li><li>MLOps view of ML workflow<\/li><li>MLOps cases<\/li><\/ul><h5>Module 2: MLOps Development<\/h5><ul>\n<li>Intro to build, train, and evaluate machine learning models<\/li><li>MLOps security<\/li><li>Automating<\/li><li>Apache Airflow<\/li><li>Kubernetes integration for MLOps<\/li><li>Amazon SageMaker for MLOps<\/li><li>Lab: Bring your own algorithm to an MLOps pipeline<\/li><li>Demonstration: Amazon SageMaker<\/li><li>Intro to build, train, and evaluate machine learning models<\/li><li>Lab: Code and serve your ML model with AWS CodeBuild<\/li><li>Activity: MLOps Action Plan Workbook<\/li><\/ul><h5>Module 3: MLOps Deployment<\/h5><ul>\n<li>Introduction to deployment operations<\/li><li>Model packaging<\/li><li>Inference<\/li><li>Lab: Deploy your model to production<\/li><li>SageMaker production variants<\/li><li>Deployment strategies<\/li><li>Deploying to the edge<\/li><li>Lab: Conduct A\/B testing<\/li><li>Activity: MLOps Action Plan Workbook<\/li><\/ul><h5>Module 4: Model Monitoring and Operations<\/h5><ul>\n<li>Lab: Troubleshoot your pipeline<\/li><li>The importance of monitoring<\/li><li>Monitoring by design<\/li><li>Lab: Monitor your ML model<\/li><li>Human-in-the-loop<\/li><li>Amazon SageMaker Model Monitor<\/li><li>Demonstration: Amazon SageMaker Pipelines, Model Monitor, model registry, and Feature Store<\/li><li>Solving the Problem(s)<\/li><li>Activity: MLOps Action Plan Workbook<\/li><\/ul><h5>Module 5: Wrap-up<\/h5><ul>\n<li>Course review<\/li><li>Activity: MLOps Action Plan Workbook<\/li><li>Wrap-up<\/li><\/ul>","summary":"<p>Dieser Kurs baut auf der in der Softwareentwicklung vorherrschenden DevOps-Praxis auf und erweitert sie, um Modelle f&uuml;r maschinelles Lernen (ML) zu erstellen, zu trainieren und bereitzustellen. Die Bedeutung von Daten, Modellen und Code f&uuml;r erfolgreiche ML-Bereitstellungen wird vermittelt. Im Kurs wird der Einsatz von Tools, Automatisierung, Prozessen und Teamwork demonstriert, um die Herausforderungen zu bew&auml;ltigen, die mit &Uuml;bergaben zwischen Dateningenieuren, Datenwissenschaftlern, Softwareentwicklern und dem Betrieb verbunden sind. Die Verwendung von Werkzeugen und Prozessen zur &Uuml;berwachung und Ergreifung von Massnahmen wird diskutiert, wenn die Modellvorhersage in der Produktion von vereinbarten Leistungskennzahlen abweicht.<\/p>\n<p>Der Dozent ermutigt die Teilnehmer dieses Kurses, einen MLOps-Aktionsplan f&uuml;r ihre Organisation durch t&auml;gliche Reflexion der Unterrichts- und Laborinhalte sowie durch Gespr&auml;che mit Kollegen und Dozenten zu erstellen.<\/p>","essentials_plain":"Erforderlich:\n\n\n- AWS Technical Essentials (AWSE)\n- DevOps Engineering on AWS (AWSDEVOPS)\n- Practical Data Science with Amazon SageMaker (PDSASM)\nZus\u00e4tzlich Empfohlen:\n\n\n- The Elements of Data Science (digitaler Kurs) oder gleichwertige Erfahrung\n- Machine Learning Terminology and Process (digitaler Kurs)","audience_plain":"- DevOps Engineers\n- ML Engineers\n- Entwickler\/Betriebe mit Verantwortung f\u00fcr die Operationalisierung von ML-Modellen","contents_plain":"- Module 0: Welcome\n- Module 1: Introduction to MLOps\n- Module 2: MLOps Development\n- Module 3: MLOps Deployment\n- Module 4: Model Monitoring and Operations\n- Module 5: Wrap-up","outline_plain":"Module 0: Welcome\n\n\n- Course introduction\nModule 1: Introduction to MLOps\n\n\n- Machine learning operations\n- Goals of MLOps\n- Communication\n- From DevOps to MLOps\n- ML workflow\n- Scope\n- MLOps view of ML workflow\n- MLOps cases\nModule 2: MLOps Development\n\n\n- Intro to build, train, and evaluate machine learning models\n- MLOps security\n- Automating\n- Apache Airflow\n- Kubernetes integration for MLOps\n- Amazon SageMaker for MLOps\n- Lab: Bring your own algorithm to an MLOps pipeline\n- Demonstration: Amazon SageMaker\n- Intro to build, train, and evaluate machine learning models\n- Lab: Code and serve your ML model with AWS CodeBuild\n- Activity: MLOps Action Plan Workbook\nModule 3: MLOps Deployment\n\n\n- Introduction to deployment operations\n- Model packaging\n- Inference\n- Lab: Deploy your model to production\n- SageMaker production variants\n- Deployment strategies\n- Deploying to the edge\n- Lab: Conduct A\/B testing\n- Activity: MLOps Action Plan Workbook\nModule 4: Model Monitoring and Operations\n\n\n- Lab: Troubleshoot your pipeline\n- The importance of monitoring\n- Monitoring by design\n- Lab: Monitor your ML model\n- Human-in-the-loop\n- Amazon SageMaker Model Monitor\n- Demonstration: Amazon SageMaker Pipelines, Model Monitor, model registry, and Feature Store\n- Solving the Problem(s)\n- Activity: MLOps Action Plan Workbook\nModule 5: Wrap-up\n\n\n- Course review\n- Activity: MLOps Action Plan Workbook\n- Wrap-up","summary_plain":"Dieser Kurs baut auf der in der Softwareentwicklung vorherrschenden DevOps-Praxis auf und erweitert sie, um Modelle f\u00fcr maschinelles Lernen (ML) zu erstellen, zu trainieren und bereitzustellen. Die Bedeutung von Daten, Modellen und Code f\u00fcr erfolgreiche ML-Bereitstellungen wird vermittelt. Im Kurs wird der Einsatz von Tools, Automatisierung, Prozessen und Teamwork demonstriert, um die Herausforderungen zu bew\u00e4ltigen, die mit \u00dcbergaben zwischen Dateningenieuren, Datenwissenschaftlern, Softwareentwicklern und dem Betrieb verbunden sind. Die Verwendung von Werkzeugen und Prozessen zur \u00dcberwachung und Ergreifung von Massnahmen wird diskutiert, wenn die Modellvorhersage in der Produktion von vereinbarten Leistungskennzahlen abweicht.\n\nDer Dozent ermutigt die Teilnehmer dieses Kurses, einen MLOps-Aktionsplan f\u00fcr ihre Organisation durch t\u00e4gliche Reflexion der Unterrichts- und Laborinhalte sowie durch Gespr\u00e4che mit Kollegen und Dozenten zu erstellen.","skill_level":"Intermediate","version":"2.0","duration":{"unit":"d","value":3,"formatted":"3 Tage"},"pricelist":{"List Price":{"SI":{"country":"SI","currency":"EUR","taxrate":20,"price":1995},"IT":{"country":"IT","currency":"EUR","taxrate":20,"price":1650},"DE":{"country":"DE","currency":"EUR","taxrate":19,"price":1995},"AT":{"country":"AT","currency":"EUR","taxrate":20,"price":1995},"AE":{"country":"AE","currency":"USD","taxrate":5,"price":2250},"IL":{"country":"IL","currency":"ILS","taxrate":17,"price":6920},"GR":{"country":"GR","currency":"EUR","taxrate":null,"price":1995},"MK":{"country":"MK","currency":"EUR","taxrate":null,"price":1995},"HU":{"country":"HU","currency":"EUR","taxrate":20,"price":1995},"BE":{"country":"BE","currency":"EUR","taxrate":21,"price":2095},"US":{"country":"US","currency":"USD","taxrate":null,"price":2025},"PL":{"country":"PL","currency":"PLN","taxrate":23,"price":5200},"GB":{"country":"GB","currency":"GBP","taxrate":20,"price":2655},"CH":{"country":"CH","currency":"CHF","taxrate":8.1,"price":2470},"CA":{"country":"CA","currency":"CAD","taxrate":null,"price":2795},"FR":{"country":"FR","currency":"EUR","taxrate":19.6,"price":2450},"NL":{"country":"NL","currency":"EUR","taxrate":21,"price":2395}}},"lastchanged":"2026-03-16T13:58:51+01:00","parenturl":"https:\/\/portal.flane.ch\/swisscom\/json-courses","nexturl_course_schedule":"https:\/\/portal.flane.ch\/swisscom\/json-course-schedule\/25768","source_lang":"de","source":"https:\/\/portal.flane.ch\/swisscom\/json-course\/amazon-mloe"}}