Oversikt over relevante kurs innen IT-kurs i Jönköpings län

AZ-1008: Administer Active Directory Domain Services [-]

Modulen dekker bruk og effekt av Lean og agile arbeidsmåter, og hvordan dette kan utnyttes til fordel for organisasjonen. Kurset vil gi kandidatene en praktisk og strategisk metode for å planlegge og levere kontinuerlig forbedring med nødvendig smidighet.  E-læringskurset inneholder 18 timer med undervisning, og er delt inn i 12 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.   [-]

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. [-]

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. [-]

Kursinnhold Hva slags data kan brukes som grunnlag for et diagram Stolpediagram Sektordiagram Kombinert diagram Formatering av diagrammer Tips og triks Smarte løsninger Sparkline Hurtiganalyse Bruk av Excels diagrammer i andre Office-programmer [-]

Kubernetes Administration (LFS458) [-]

Introduction to C# and .NET [-]

RH294: Red Hat System Administration III: Linux Automation with Ansible [-]

Automatisering i Microsoft 365 med Power Automate [-]

TARGET AUDIENCE Software engineers concerned with building, managing and deploying AI solutions that leverage Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework. They are familiar with C#, Python, or JavaScript and have knowledge on using REST-based APIs to build computer vision, language analysis, knowledge mining, intelligent search, and conversational AI solutions on Azure. COURSE OBJECTIVES After completing this course you should be able to: Describe considerations for creating AI-enabled applications Identify Azure services for AI application development Provision and consume cognitive services in Azure Manage cognitive services security Monitor cognitive services Use a cognitive services container Use the Text Analytics cognitive service to analyze text Use the Translator cognitive service to translate text Use the Speech cognitive service to recognize and synthesize speech Use the Speech cognitive service to translate speech Create a Language Understanding app Create a client application for Language Understanding Integrate Language Understanding and Speech Use QnA Maker to create a knowledge base Use a QnA knowledge base in an app or bot Use the Bot Framework SDK to create a bot Use the Bot Framework Composer to create a bot Use the Computer Vision service to analyze images Use Video Indexer to analyze videos Use the Custom Vision service to implement image classification Use the Custom Vision service to implement object detection Detect faces with the Computer Vision service Detect, analyze, and recognize faces with the Face service Use the Computer Vision service to read text in images and documents Use the Form Recognizer service to extract data from digital forms Create an intelligent search solution with Azure Cognitive Search Implement a custom skill in an Azure Cognitive Search enrichment pipeline Use Azure Cognitive Search to create a knowledge store   COURSE CONTENT Module 1: Introduction to AI on Azure Artificial Intelligence (AI) is increasingly at the core of modern apps and services. In this module, you'll learn about some common AI capabilities that you can leverage in your apps, and how those capabilities are implemented in Microsoft Azure. You'll also learn about some considerations for designing and implementing AI solutions responsibly. Introduction to Artificial Intelligence Artificial Intelligence in Azure Module 2: Developing AI Apps with Cognitive Services Cognitive Services are the core building blocks for integrating AI capabilities into your apps. In this module, you'll learn how to provision, secure, monitor, and deploy cognitive services. Getting Started with Cognitive Services Using Cognitive Services for Enterprise Applications Lab: Get Started with Cognitive Services Lab: Get Started with Cognitive Services Lab: Monitor Cognitive Services Lab: Use a Cognitive Services Container Module 3: Getting Started with Natural Language Processing  Natural Language processing (NLP) is a branch of artificial intelligence that deals with extracting insights from written or spoken language. In this module, you'll learn how to use cognitive services to analyze and translate text. Analyzing Text Translating Text Lab: Analyze Text Lab: Translate Text Module 4: Building Speech-Enabled Applications Many modern apps and services accept spoken input and can respond by synthesizing text. In this module, you'll continue your exploration of natural language processing capabilities by learning how to build speech-enabled applications. Speech Recognition and Synthesis Speech Translation Lab: Recognize and Synthesize Speech Lab: Translate Speech Module 5: Creating Language Understanding Solutions To build an application that can intelligently understand and respond to natural language input, you must define and train a model for language understanding. In this module, you'll learn how to use the Language Understanding service to create an app that can identify user intent from natural language input. Creating a Language Understanding App Publishing and Using a Language Understanding App Using Language Understanding with Speech Lab: Create a Language Understanding App Lab: Create a Language Understanding Client Application Use the Speech and Language Understanding Services Module 6: Building a QnA Solution One of the most common kinds of interaction between users and AI software agents is for users to submit questions in natural language, and for the AI agent to respond intelligently with an appropriate answer. In this module, you'll explore how the QnA Maker service enables the development of this kind of solution. Creating a QnA Knowledge Base Publishing and Using a QnA Knowledge Base Lab: Create a QnA Solution Module 7: Conversational AI and the Azure Bot Service Bots are the basis for an increasingly common kind of AI application in which users engage in conversations with AI agents, often as they would with a human agent. In this module, you'll explore the Microsoft Bot Framework and the Azure Bot Service, which together provide a platform for creating and delivering conversational experiences. Bot Basics Implementing a Conversational Bot Lab: Create a Bot with the Bot Framework SDK Lab: Create a Bot with a Bot Freamwork Composer Module 8: Getting Started with Computer Vision Computer vision is an area of artificial intelligence in which software applications interpret visual input from images or video. In this module, you'll start your exploration of computer vision by learning how to use cognitive services to analyze images and video. Analyzing Images Analyzing Videos Lab: Analyse Images with Computer Vision Lab: Analyze Images with Video Indexer Module 9: Developing Custom Vision Solutions While there are many scenarios where pre-defined general computer vision capabilities can be useful, sometimes you need to train a custom model with your own visual data. In this module, you'll explore the Custom Vision service, and how to use it to create custom image classification and object detection models. Image Classification Object Detection Lab: Classify Images with Custom Vision Lab: Detect Objects in Images with Custom Vision Module 10: Detecting, Analyzing, and Recognizing Faces Facial detection, analysis, and recognition are common computer vision scenarios. In this module, you'll explore the user of cognitive services to identify human faces. Detecting Faces with the Computer Vision Service Using the Face Service Lab:Destect, Analyze and Recognize Faces Module 11: Reading Text in Images and Documents Optical character recognition (OCR) is another common computer vision scenario, in which software extracts text from images or documents. In this module, you'll explore cognitive services that can be used to detect and read text in images, documents, and forms. Reading text with the Computer Vision Service Extracting Information from Forms with the Form Recognizer service Lab: Read Text in IMages Lab: Extract Data from Forms Module 12: Creating a Knowledge Mining Solution Ultimately, many AI scenarios involve intelligently searching for information based on user queries. AI-powered knowledge mining is an increasingly important way to build intelligent search solutions that use AI to extract insights from large repositories of digital data and enable users to find and analyze those insights. Implementing an Intelligent Search Solution Developing Custom Skills for an Enrichment Pipeline Creating a Knowledge Store Lab: Create and Azure Cognitive Search Solution Create a Custom Skill for Azure Cognitive Search Create a Knowledge Store with Azure Cognitive Search   TEST CERTIFICATION Recommended as preparation for the following exams: AI-102 - Designing and Implementing a Microsoft Azure AI Solution - Part of the requirements for the Microsoft Certified Azure AI Engineer Associate Certification.   HVORFOR VELGE SG PARTNER AS:  Flest kurs med Startgaranti Rimeligste kurs Beste service og personlig oppfølgning Tilgang til opptak etter endt kurs Partner med flere av verdens beste kursleverandører [-]

Students will learn how to plan for DevOps, use source control, scale Git for an enterprise, consolidate artifacts, design a dependency management strategy, manage secrets, implement continuous integration, implement a container build strategy, design a release strategy, set up a release management workflow, implement a deployment pattern, and optimize feedback mechanisms TARGET AUDIENCE Students in this course are interested in designing and implementing DevOps processes or in passing the Microsoft Azure DevOps Solutions certification exam. COURSE OBJECTIVES Plan for the transformation with shared goals and timelines Select a project and identify project metrics and Key Performance Indicators (KPI's) Create a team and agile organizational structure Design a tool integration strategy Design a license management strategy (e.g., Azure DevOps and GitHub users) Design a strategy for end-to-end traceability from work items to working software Design an authentication and access strategy Design a strategy for integrating on-premises and cloud resources Describe the benefits of using Source Control Describe Azure Repos and GitHub Migrate from TFVC to Git Manage code quality, including technical debt SonarCloud, and other tooling solutions Build organizational knowledge on code quality Explain how to structure Git repos Describe Git branching workflows Leverage pull requests for collaboration and code reviews Leverage Git hooks for automation Use Git to foster inner source across the organization Explain the role of Azure Pipelines and its components Configure Agents for use in Azure Pipelines Explain why continuous integration matters Implement continuous integration using Azure Pipelines Design processes to measure end-user satisfaction and analyze user feedback Design processes to automate application analytics Manage alerts and reduce meaningless and non-actionable alerts Carry out blameless retrospectives and create a just culture Define an infrastructure and configuration strategy and appropriate toolset for a release pipeline and application infrastructure Implement compliance and security in your application infrastructure Describe the potential challenges with integrating open-source software Inspect open-source software packages for security and license compliance Manage organizational security and compliance policies Integrate license and vulnerability scans into build and deployment pipelines Configure build pipelines to access package security and license ratings   COURSE CONTENT Module 1: Get started on a DevOps transformation journey Module 1 Lessons Introduction to DevOps Choose the right project Describe team structures Choose the DevOps tools Plan Agile with GitHub Projects and Azure Boards Introduction to source control Describe types of source control systems Work with Azure Repos and GitHub Lab 1: Agile planning and portfolio management with Azure Boards   Lab 2: Version controlling with Git in Azure Repos   After completing Module 1, students will be able to: Understand what DevOps is and the steps to accomplish it Identify teams to implement the process Plan for the transformation with shared goals and timelines Plan and define timelines for goals Understand different projects and systems to guide the journey Select a project to start the DevOps transformation Identify groups to minimize initial resistance Identify project metrics and Key Performance Indicators (KPI's) Understand agile practices and principles of agile development Create a team and agile organizational structure Module 2: Development for enterprise DevOps Module 2 Lessons Structure your Git Repo Manage Git branches and workflows Collaborate with pull requests in Azure Repos Explore Git hooks Plan foster inner source Manage Git repositories Identify technical debt Lab 3: Version controlling with Git in Azure Repos   After completing Module 2, students will be able to: Understand Git repositories Implement mono repo or multiple repos Explain how to structure Git Repos Implement a change log Describe Git branching workflows Implement feature branches Implement GitFlow Fork a repo Leverage pull requests for collaboration and code reviews Give feedback using pull requests Module 3: Implement CI with Azure Pipelines and GitHub Actions Module 3 Lessons Explore Azure Pipelines Manage Azure Pipeline agents and pools Describe pipelines and concurrency Explore Continuous integration Implement a pipeline strategy Integrate with Azure Pipelines Introduction to GitHub Actions Learn continuous integration with GitHub Actions Design a container build strategy Lab 4: Configuring agent pools and understanding pipeline styles   Lab 5: Enabling continuous integration with Azure Pipelines   Lab 6: Integrating external source control with Azure Pipelines   Lab 7: Implementing GitHub Actions by using DevOps Starter   Lab 8: Deploying Docker Containers to Azure App Service web apps   After completing Module 3, students will be able to: Describe Azure Pipelines Explain the role of Azure Pipelines and its components Decide Pipeline automation responsibility Understand Azure Pipeline key terms Choose between Microsoft-hosted and self-hosted agents Install and configure Azure pipelines Agents Configure agent pools Make the agents and pools secure Use and estimate parallel jobs Module 4: Design and implement a release strategy Module 4 Lessons Introduction to continuous delivery Create a release pipeline Explore release strategy recommendations Provision and test environments Manage and modularize tasks and templates Automate inspection of health Lab 9: Creating a release dashboard   Lab 10: Controlling deployments using Release Gates   After completing Module 4, students will be able to: Explain continuous delivery (CD) Implement continuous delivery in your development cycle Understand releases and deployment Identify project opportunities to apply CD Explain things to consider when designing your release strategy Define the components of a release pipeline and use artifact sources Create a release approval plan Implement release gates Differentiate between a release and a deployment Module 5: Implement a secure continuous deployment using Azure Pipelines Module 5 Lessons Introduction to deployment patterns Implement blue-green deployment and feature toggles Implement canary releases and dark launching Implement A/B testing and progressive exposure deployment Integrate with identity management systems Manage application configuration data Lab 11: Configuring pipelines as code with YAML   Lab 12: Setting up and running functional tests   Lab 13: Integrating Azure Key Vault with Azure DevOps   After completing Module 5, students will be able to: Explain the terminology used in Azure DevOps and other Release Management Tooling Describe what a Build and Release task is, what it can do, and some available deployment tasks Implement release jobs Differentiate between multi-agent and multi-configuration release job Provision and configure target environment Deploy to an environment securely using a service connection Configure functional test automation and run availability tests Setup test infrastructure Use and manage task and variable groups Module 6: Manage infrastructure as code using Azure and DSC Module 6 Lessons Explore infrastructure as code and configuration management Create Azure resources using Azure Resource Manager templates Create Azure resources by using Azure CLI Explore Azure Automation with DevOps Implement Desired State Configuration (DSC) Implement Bicep Lab 14: Azure deployments using Azure Resource Manager templates   After completing Module 6, students will be able to: Understand how to deploy your environment Plan your environment configuration Choose between imperative versus declarative configuration Explain idempotent configuration Create Azure resources using ARM templates Understand ARM templates and template components Manage dependencies and secrets in templates Organize and modularize templates Create Azure resources using Azure CLI Module 7: Implement security and validate code bases for compliance Module 7 Lessons Introduction to Secure DevOps Implement open-source software Software Composition Analysis Static analyzers OWASP and Dynamic Analyzers Security Monitoring and Governance Lab 15: Implement security and compliance in Azure Pipelines   Lab 16: Managing technical debt with SonarQube and Azure DevOps   After completing Module 7, students will be able to: Identify SQL injection attack Understand DevSecOps Implement pipeline security Understand threat modeling Implement open-source software Explain corporate concerns for open-source components Describe open-source licenses Understand the license implications and ratings Work with Static and Dynamic Analyzers Configure Microsoft Defender for Cloud Module 8: Design and implement a dependency management strategy Module 8 Lessons Explore package dependencies Understand package management Migrate, consolidate, and secure artifacts Implement a versioning strategy Introduction to GitHub Packages Lab 17: Package management with Azure Artifacts   After completing Module 8, students will be able to: Define dependency management strategy Identify dependencies Describe elements and componentization of a dependency management Scan your codebase for dependencies Implement package management Manage package feed Consume and create packages Publish packages Identify artifact repositories Migrate and integrate artifact repositories Module 9: Implement continuous feedback Module 9 Lessons Implement tools to track usage and flow Develop monitor and status dashboards Share knowledge within teams Design processes to automate application analytics Manage alerts, Blameless retrospectives and a just culture Lab 18: Monitoring application performance with Application Insights   Lab 19: Integration between Azure DevOps and Microsoft Teams   Lab 20: Sharing Team Knowledge using Azure Project Wikis   After completing Module 9, students will be able to: Implement tools to track feedback Plan for continuous monitoring Implement Application Insights Use Kusto Query Language (KQL) Implement routing for mobile applications Configure App Center Diagnostics Configure alerts Create a bug tracker Configure Azure Dashboards Work with View Designer in Azure Monitor [-]

Microsoft 365 Enterprise Administrator Expert (MCE) - Boot Camp [-]

COURSE OVERVIEW The Developing on Amazon Web Services AWS course is designed to help individuals design and build secure, reliable and scalable AWS-based applications. In this course, we cover fundamental concepts and baseline programming for developing applications on AWS. We also show you how to work with AWS code libraries, SDKs, and IDE toolkits so that you can effectively develop and deploy code on the AWS platform.   TARGET AUDIENCE This course is intended for Developers COURSE CONTENT Note: course outline may vary slightly based on the regional location and/or language in which the class is delivered. Day 1: Getting Started Working with the AWS code library, SDKs, and IDE toolkits Introduction to AWS security features Service object models and baseline concepts for working with Amazon Simple Storage Service (S3) and Amazon DynamoDB Day 2: Working with AWS Services Service object models and baseline concepts for working with the Amazon Simple Queue Service (SQS) and the Amazon Simple Notification Service (SNS) Applying AWS security features Day 3: Application Development and Deployment Best Practices Application deployment using AWS Elastic Beanstalk Best practices for working with AWS services   [-]

Vil du jobbe enklere og mer effektivt i InDesign? På dette kurset vil du lære å lage gode, avanserte og tidsbesparende maler for sider, tekst og objekter, samt gjenbruk via biblioteker. Etter kurset kan du lage egne maler som automatiserer mange arbeidsprosesser og sparer deg for mye tid og arbeid. Gode maler kvalitetsikrer produktetene dine og gir deg mere tid til å være kreativ. Hvem passer kurset for? Kurset passer for deg som jobber i Adobe InDesign og ønsker å utnytte programmets potensiale. Forhåndskunnskap i InDesign: «InDesign grunnkurs» eller tilsvarende kunnskap. Dette lærer du: God, effektiv og avansert bruk av maler for sider, tekst og objekter i Adobe InDesign Spar på elementer du lager med CC Libraries Lage automatisk innholdsfortegnelse Bruk av tabell Tekstlenker og registerlinjer Hvordan tilpasse en layout til ulike størrelser i samme dokument Lage egne tastatursnarveier https://igm.no/indesign-kurs-videregaende/ [-]

Kubernetes for App Developers (LFD459) [-]