IT-kurs

Understand the purpose and key concepts of the Monitor, Support, and Fulfil practices, elucidating their importance in maintaining, supporting, and delivering IT services effectively.InteractiveOur eLearning:Self-pacedDevice-friendly12 hour contentMobile-optimised Exam:60 questionsMultiple Choice90 minutesClosed bookMinimum required score to pass: 65%  [-]

Understand the key concepts of Relationship Management, Supplier Management, Service Level Management, Continual Improvement, and Information Security Management, elucidating their significance in fostering collaboration, ensuring service quality, driving continual improvement, and maintaining information security. This eLearning is: Interactive Self-paced   Device-friendly   12 hours content   Mobile-optimised   Practical exercises   Exam: 60 questions Multiple choise 90 minutes Closed book Minimum required score to pass: 65% [-]

Kurset fokuserer på konvertering av etterspørsel til verdi via IT-relaterte tjenester. Modulen dekker sentrale emner som SLA-design, styring av flere leverandører, kommunikasjon, relasjonsstyring, CX- og UX-design, kartlegging av kunder og mer. E-læringskurset inneholder 18 timer med undervisning, og er delt inn i 8 moduler. Les mer om ITIL® 4 på  AXELOS sine websider. Du vil motta en e-post med tilgang til e-læringen, sertifiseringsvoucher og digital bok fra Peoplecert. Du avtaler tid for sertifiseringen som beskrevet i e-posten fra Peoplecert. [-]

Understand the purpose and key concepts of the Plan, Implement, and Control practices, highlighting their importance in establishing, executing, and governing IT service strategies effectively. This eLearning is: Interactive Self-paced Device-friendly 2-3 hours content Mobile-optimised Practical exercises   Exam: 60 questions Multiple Choice 90 Minutes Closed book Pass Mark: 65% [-]

Understand the purpose and key concepts of the Service Level Management Practice, elucidating its significance in defining, negotiating, and managing service levels to meet customer expectations. This eLearning is: Interactive Self-paced   Device-friendly   2-3 hours content   Mobile-optimised   Practical exercises   Exam: 20 questions Multiple choise 30 minutes Closed book Minimum required score to pass: 65% [-]

ITIL® 4 Foundation-kurset er en introduksjon til ITIL® 4. Kurset lar kandidater se på IT-tjenestestyring gjennom en ende-til-ende driftsmodell, som inkluderer oppretting, levering og kontinuerlig forbedring av IT-relaterte produkter og tjenester. E-læringskurset inneholder 12 timer med undervisning, og er delt inn i 8 moduler. Les mer om ITIL® 4 på AXELOS sine websider. Inkluderer: Tilgang til ITIL® 4 Foundation e-læring (engelsk) i 12 måneder. ITIL® Foundation online voucher til sertifiseringstest + digital ITIL Foundation bok Du vil motta en e-post med tilgang til e-læringen, sertifiseringsvoucher og digital bok fra Peoplecert. Du avtaler tid for sertifiseringen som beskrevet i e-posten fra Peoplecert. Sertifiseringen består av: 40 spørsmål Multiple Choice 60 minutter + 15 minutter til rådighet dersom du ikke har engelsk som morsmål For å bestå må du ha minimum 26 riktige (65%) Ingen hjelpemidler tillatt ITIL®/PRINCE2®/MSP®/MoP® are registered trademarks of AXELOS Limited, used under permission of AXELOS Limited. All rights reserved. [-]

Understand the purpose and key concepts of Release Management, elucidating its significance in planning, scheduling, and controlling the build, test, and deployment of releases to ensure they deliver the expected outcomes. The eLearning course: Interactive Self-paced Device-friendly 2-3 hour content Mobile-optimised Practical exercises   Exam:   20 questions Multiple choise Closed book 30 minutes Minimum required score to pass: 65% [-]

Understand the purpose and key concepts of Service Configuration Management, including its role in maintaining accurate and reliable information about configuration items (CIs) within the IT infrastructure. This eLearning is: Interactive Self-paced Device-friendly 2-3 hours content Mobile-optimised Practical exercises   Exam: 20 questions Multiple Choice 30 Minutes Closed book Pass Mark: 65% [-]

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 This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure. TARGET AUDIENCE This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud. COURSE CONTENT Module 1: Introduction to Azure Machine Learning In this module, you will learn how to provision an Azure Machine Learning workspace and use it to manage machine learning assets such as data, compute, model training code, logged metrics, and trained models. You will learn how to use the web-based Azure Machine Learning studio interface as well as the Azure Machine Learning SDK and developer tools like Visual Studio Code and Jupyter Notebooks to work with the assets in your workspace. Getting Started with Azure Machine Learning Azure Machine Learning Tools Lab : Creating an Azure Machine Learning WorkspaceLab : Working with Azure Machine Learning Tools After completing this module, you will be able to Provision an Azure Machine Learning workspace Use tools and code to work with Azure Machine Learning Module 2: No-Code Machine Learning with Designer This module introduces the Designer tool, a drag and drop interface for creating machine learning models without writing any code. You will learn how to create a training pipeline that encapsulates data preparation and model training, and then convert that training pipeline to an inference pipeline that can be used to predict values from new data, before finally deploying the inference pipeline as a service for client applications to consume. Training Models with Designer Publishing Models with Designer Lab : Creating a Training Pipeline with the Azure ML DesignerLab : Deploying a Service with the Azure ML Designer After completing this module, you will be able to Use designer to train a machine learning model Deploy a Designer pipeline as a service Module 3: Running Experiments and Training Models In this module, you will get started with experiments that encapsulate data processing and model training code, and use them to train machine learning models. Introduction to Experiments Training and Registering Models Lab : Running ExperimentsLab : Training and Registering Models After completing this module, you will be able to Run code-based experiments in an Azure Machine Learning workspace Train and register machine learning models Module 4: Working with Data Data is a fundamental element in any machine learning workload, so in this module, you will learn how to create and manage datastores and datasets in an Azure Machine Learning workspace, and how to use them in model training experiments. Working with Datastores Working with Datasets Lab : Working with DatastoresLab : Working with Datasets After completing this module, you will be able to Create and consume datastores Create and consume datasets Module 5: Compute Contexts One of the key benefits of the cloud is the ability to leverage compute resources on demand, and use them to scale machine learning processes to an extent that would be infeasible on your own hardware. In this module, you'll learn how to manage experiment environments that ensure consistent runtime consistency for experiments, and how to create and use compute targets for experiment runs. Working with Environments Working with Compute Targets Lab : Working with EnvironmentsLab : Working with Compute Targets After completing this module, you will be able to Create and use environments Create and use compute targets Module 6: Orchestrating Operations with Pipelines Now that you understand the basics of running workloads as experiments that leverage data assets and compute resources, it's time to learn how to orchestrate these workloads as pipelines of connected steps. Pipelines are key to implementing an effective Machine Learning Operationalization (ML Ops) solution in Azure, so you'll explore how to define and run them in this module. Introduction to Pipelines Publishing and Running Pipelines Lab : Creating a PipelineLab : Publishing a Pipeline After completing this module, you will be able to Create pipelines to automate machine learning workflows Publish and run pipeline services Module 7: Deploying and Consuming Models Models are designed to help decision making through predictions, so they're only useful when deployed and available for an application to consume. In this module learn how to deploy models for real-time inferencing, and for batch inferencing. Real-time Inferencing Batch Inferencing Lab : Creating a Real-time Inferencing ServiceLab : Creating a Batch Inferencing Service After completing this module, you will be able to Publish a model as a real-time inference service Publish a model as a batch inference service Module 8: Training Optimal Models By this stage of the course, you've learned the end-to-end process for training, deploying, and consuming machine learning models; but how do you ensure your model produces the best predictive outputs for your data? In this module, you'll explore how you can use hyperparameter tuning and automated machine learning to take advantage of cloud-scale compute and find the best model for your data. Hyperparameter Tuning Automated Machine Learning Lab : Tuning HyperparametersLab : Using Automated Machine Learning After completing this module, you will be able to Optimize hyperparameters for model training Use automated machine learning to find the optimal model for your data Module 9: Interpreting Models Many of the decisions made by organizations and automated systems today are based on predictions made by machine learning models. It's increasingly important to be able to understand the factors that influence the predictions made by a model, and to be able to determine any unintended biases in the model's behavior. This module describes how you can interpret models to explain how feature importance determines their predictions. Introduction to Model Interpretation using Model Explainers Lab : Reviewing Automated Machine Learning ExplanationsLab : Interpreting Models After completing this module, you will be able to Generate model explanations with automated machine learning Use explainers to interpret machine learning models Module 10: Monitoring Models After a model has been deployed, it's important to understand how the model is being used in production, and to detect any degradation in its effectiveness due to data drift. This module describes techniques for monitoring models and their data. Monitoring Models with Application Insights Monitoring Data Drift Lab : Monitoring a Model with Application InsightsLab : Monitoring Data Drift After completing this module, you will be able to Use Application Insights to monitor a published model Monitor data drift   [-]

Microsoft 365: Bli en digital pådriver [-]

MD-101: Managing Modern Desktops [-]

Fleksible kurs for fremtidenNy kunnskap skal gi umiddelbar effekt, og samtidig være holdbar og bærekraftig på lang sikt.NTI AS har 30 års erfaring innen kurs og kompetanseheving, og utdanner årlig rundt 10.000 personer i Nord Europa innen CAD, BIM, industri, design og konstruksjon.   Solibri Anywhere og Site   På kurset vil du lære å: Sammenstille flere IFC-modeller og navigere i disse Velge ut grupper av objekter for nærmere studier Legge inn snitt, måle, markere og opprette slides fra visninger av modellen Opprette rapporter og kommentere «issues» i Excel og BCF-format Se på resultatet av utførte regelsjekker i modellen Se på resultatet av utførte informasjons- og mengdeuttak fra modellen Høste informasjon og mengder fra modellen basert på eksisterende maler og klassifikasjoner Skape egne klassifikasjoner og definisjoner for megndeuttak   Dette er et populært kurs, meld deg på nå! Spesialtilpasset kurs: NTI anbefaler spesialtilpassede kurs for bedrifter som planlegger å sende to eller flere deltakere på Solibri-kurs. Grunnen til dette er at Solibri brukes av mange forskjellige aktører og profesjoner i BAE-bransjen, og følgelig blir de åpne kursene ofte for generelle for enkelte kursdeltakere. I et spesialtilpasset kurs vil vår kurskonsulent kartlegge fokusområdene i forkant av kurset, og gjennomføre kurset i henhold til selskapets behov, gjerne basert på kundens egne modeller. Utbyttet av kurset blir følgelig mye større.  Ta kontakt med oss på telefon 483 12 300, epost: salg-no@nti.biz eller les mer på www.nti.biz   [-]

 In Microsoft 365 security management, you will examine all the common types of threat vectors and data breaches facing organizations today, and you will learn how Microsoft 365’s security solutions address these security threats. Global Knowledge will introduce you to the Microsoft Secure Score, as well as to Azure Active Directory Identity Protection. You will then learn how to manage the Microsoft 365 security services, including Exchange Online Protection, Advanced Threat Protection, Safe Attachments, and Safe Links. Finally, you will be introduced to the various reports that monitor your security health. You will then transition from security services to threat intelligence; specifically, using the Security Dashboard and Advanced Threat Analytics to stay ahead of potential security breaches. TARGET AUDIENCE This course is designed for persons who are aspiring to the Microsoft 365 Enterprise Admin role and have completed one of the Microsoft 365 work load administrator certification paths. COURSE OBJECTIVES By actively participating in this course, you will learn about the following: Microsoft 365 Security Metrics Microsoft 365 Security Services Microsoft 365 Threat Intelligence Data Governance in Microsoft 365 Archiving and Retention in Office 365 Data Governance in Microsoft 365 Intelligence Search and Investigations Device Management Windows 10 Deployment Strategies Mobile Device Management COURSE CONTENT Module 1: Introduction to Microsoft 365 Security Metrics Threat Vectors and Data Breaches Security Solutions in Microsoft 365 Introduction to the Secure Score Introduction to Azure Active Directory Identity Protection Module 2: Managing Your Microsoft 365 Security Services Introduction to Exchange Online Protection Introduction to Advanced Threat Protection Managing Safe Attachments Managing Safe Links Monitoring and Reports Module 3: Lab 1 - Manage Microsoft 365 Security Services Exercise 1 - Set up a Microsoft 365 Trial Tenant Exercise 2 - Implement an ATP Safe Links policy and Safe Attachment policy Module 4: Microsoft 365 Threat Intelligence Overview of Microsoft 365 Threat Intelligence Using the Security Dashboard Configuring Advanced Threat Analytics Implementing Your Cloud Application Security Module 5: Lab 2 - Implement Alert Notifications Using the Security Dashboard Exercise 1 - Prepare for implementing Alert Policies Exercise 2 - Implement Security Alert Notifications Exercise 3 - Implement Group Alerts Exercise 4 - Implement eDiscovery Alerts Module 6: Introduction to Data Governance in Microsoft 365 Introduction to Archiving in Microsoft 365 Introduction to Retention in Microsoft 365 Introduction to Information Rights Management Introduction to Secure Multipurpose Internet Mail Extension Introduction to Office 365 Message Encryption Introduction to Data Loss Prevention Module 7: Archiving and Retention in Office 365 In-Place Records Management in SharePoint Archiving and Retention in Exchange Retention Policies in the SCC Module 8: Lab 3 - Implement Archiving and Retention Exercise 1 - Initialize Compliance in Your Organization Exercise 2 - Configure Retention Tags and Policies Exercise 3 - Implement Retention Policies Module 9: Implementing Data Governance in Microsoft 365 Intelligence Planning Your Security and Complaince Needs Building Ethical Walls in Exchange Online Creating a Simple DLP Policy from a Built-in Template Creating a Custom DLP Policy Creating a DLP Policy to Protect Documents Working with Policy Tips Module 10: Lab 4 - Implement DLP Policies Exercise 1 - Manage DLP Policies Exercise 2 - Test MRM and DLP Policies Module 11: Managing Data Governance in Microsoft 365 Managing Retention in Email Troubleshooting Data Governance Implementing Azure Information Protection Implementing Advanced Features of AIP Implementing Windows Information Protection Module 12: Lab 5 - Implement AIP and WIP Exercise 1 - Implement Azure Information Protection Exercise 2 - Implement Windows Information Protection Module 13: Managing Search and Investigations Searching for Content in the Security and Compliance Center Auditing Log Investigations Managing Advanced eDiscovery Module 14: Lab 6 - Manage Search and Investigations Exercise 1 - Investigate Your Microsoft 365 Data Exercise 2 - Configure and Deploy a Data Subject Request Module 15: Planning for Device Management Introduction to Co-management Preparing Your Windows 10 Devices for Co-management Transitioning from Configuration Manager to Intune Introduction to Microsoft Store for Business Planning for Mobile Application Management Module 16: Lab 7 - Implement the Microsoft Store for Business Exercise 1 - Configure the Microsoft Store for Business Exercise 2 - Manage the Microsoft Store for Business Module 17: Planning Your Windows 10 Deployment Strategy Windows 10 Deployment Scenarios Implementing Windows Autopilot Planning Your Windows 10 Subscription Activation Strategy Resolving Windows 10 Upgrade Errors Introduction to Windows Analytics Module 18: Implementing Mobile Device Management Planning Mobile Device Management Deploying Mobile Device Management Enrolling Devices to MDM Managing Device Compliance Module 19: Lab 8 - Manage Devices with Intune Exercise 1 - Enable Device Management Exercise 2 - Configure Azure AD for Intune Exercise 3 - Create Intune Policies Exercise 4 - Enroll a Windows 10 Device Exercise 5 - Manage and Monitor a Device in Intune TEST CERTIFICATION This course helps you to prepare for exam MS101. 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