Kurs i Nore og Uvdal

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%  [-]

Case samling 2: «Konflikter og rolleutfordringer på generalforsamlingen»    Deltakelse på denne CASE-kvelden er inkludert i prisen for alle som er med i styrenettverksgrupper arrangert av Styreforeningen. For andre deltakere som er medlemmer av Styreforeningen koster seminaret kr. 4.990,-.For deltakere som ikke er med i styrenettverksgrupper og som ikke er medlemmer av Styreforeningen er prisen kr. 5.990,-   Overskriften for kvelden er: Konflikter og rolleutfordringer på generalforsamlingen Denne kvelden skal vi planlegge, innkalle og gjennomføre en krevende generalforsamling i selskapet Extender AS.   Selskapet har 122 aksjonærer og aksjer fordelt mellom 3 klasser. Det er opprør blant en gruppe av aksjonærene som mener at en av de større aksjonærene med 36% eierskap skaper urimelige fordeler for egne nærstående på de andre aksjonærers bekostning. Det er videre kommet inn ulike forslag til vedtektsendringer som styret må fremme til behandling, herunder utradisjonelle varianter som styret er i tvil om kan innføres i et selskapsvedtekter. Det er krasse fronter hva gjelder styresammensettingen i selskapet, og det har kommet forslag til ny styresammensetting fra valgkomiteen, samt fra hele 3 andre aksjonærgrupperinger. Det er også varslet at det kan komme benkeforslag på enkeltkandidater. En emisjon bør komme i orden og det bør fremlegges en instruks for valgkomiteen da denne føler det vanskelig å vite sitt mandat og sine mulige måter og rammer og arbeide på, eller innenfor. Bli med oss for å løse opp knuter og finne fornuftige løsninger for hvordan håndtere de ulike situasjonene og sakene.   [-]

Case kveld Del 1: Geronimo Management AS   Deltakelse på denne CASE-kvelden er inkludert i prisen for alle som er med i styrenettverksgrupper arrangert av Styreforeningen. For andre deltakere som er medlemmer av Styreforeningen koster seminaret kr. 4.990,-.For deltakere som ikke er med i styrenettverksgrupper og som ikke er medlemmer av Styreforeningen er prisen kr. 5.990,-   Overskriften for kvelden er: Konflikter og rolleutfordringer i styrerommet   Denne kvelden beveger vi oss inn i kulissene for selskapet: Gerinomo Management AS som er et SMB selskap, opprinnelig grundet og eiet av 2 tidligere kamerater: Halvor og Jonas. De to har hatt 30 års fartstid sammen i og med oppbyggingen av selskapet. I løpet av kort tid oppstår to situasjoner: Halvor får akutt hjerteproblemer på vei hjem fra arbeid og dør kort tid etter. Det kommer videre frem at Jonas sin tilstand de siste månedene har vært holdt skjult, men det blir nå klart at han har hurtig eskalerende demens og ikke er istand til å ta vare på seg selv eller selskapet. Vedtektene og aksjonæravtalen har noen bestemmelser om situasjoner som her oppstår, men er ikke dekkende nå som begge eiere er «ute av bildet». Neste generasjon på begge sider ønsker å «rykke inn» for å overta den veldrevne virksomheten. Et styre må på plass og håndtere både utfordringer og muligheter som står foran selskapet. Her må vi inn å analysere: hva som skjer, hva som burde gjøres, hvordan vi kan gå frem for å håndtere ulike situasjoner etc?   Bli med oss for å løse opp knuter og finne veien videre!   [-]

 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   [-]

One-on-One English Homework Help – Primary, Lower Secondary, and Upper Secondary School At NLS Norwegian Language School, we offer personalized one-on-one English tutoring for children and teens at all school levels. Each lesson is adapted to your child’s academic needs, learning pace, and goals – helping them develop strong skills and greater confidence in English. Where can your child learn? Your child can receive tutoring at home, in our classrooms in Oslo, or online – whichever is most convenient for your family. What can your child expect? They will receive lessons tailored to their current level and pace of learning. Sessions cover reading, writing, grammar, pronunciation, and speaking, with a strong focus on comprehension and communication. Regardless of the format, your child will benefit from individual attention and support throughout the course. Who is this service for? This tutoring is ideal for students who want to improve their grades, need help understanding the curriculum, or want to feel more confident using English in school. Get started today Contact us to arrange tutoring and help your child succeed in English. [-]

AZ-400: Designing and Implementing Microsoft DevOps solutions [-]

  Studieår: 2013-2014   Gjennomføring: Høst og vår Antall studiepoeng: 5.0 Forutsetninger: Ingen Innleveringer: For å kunne gå opp til eksamen må 8 utvalgte øvingsoppgaver være godkjente. Personlig veileder: ja Vurderingsform: Skriftlig eksamen, individuell, 3 timer. Ansvarlig: Arne Bjørn Mikalsen Eksamensdato: 16.12.13 / 19.05.14         Læremål: KUNNSKAPERKandidaten:- kan gjøre rede for de mest brukte teknologiene for lokalnettverk- kan gjøre rede for teknisk oppbygning av nettverk- kan gjøre rede for ulike nettverkskomponenter, deres virkemåte og bruksområde- kan planlegge og vurdere sikkerhet i lokalnettverk FERDIGHETER:Kandidaten:- kan koble til og konfigurere en datamaskin slik at den fungerer i et nettverk med internettoppkobling- kan opprette brukerkontoer, tildele rettigheter, samt administrere nettverk med en ressursdatabase- kan planlegge, implementere og konfigurere et mindre lokalnettverk GENERELL KOMPETANSE:Kandidaten:- har kompetanse til selvstendig både å formidle og å ta i bruk sine kunnskaper og ferdigheter innen emnets tema i en driftssituasjon- kan i en praktisk driftssituasjon, forklare og gjøre bruk av sin kunnskap både innen hvert enkelt tema i faget og på tvers av temaene- kan kommunisere med andre om nettverksløsninger Innhold:Fysiske medier i bruk i lokalnettverk. Nettverkskomponenter. Design av nettverk (nettverk infrastruktur). Trådløse nettverk, design og sikkerhet. Generelt om forskjellige typer nettverksoperativsystem. Introduksjon til Active Directory og eDirectory. Prinsipper for konfigurasjon, installasjon, drift og sikkerhet og driftsfilosofi i lokalnettverk. Introduksjon til virtualisering. Driftsmodeller: Fjerndrift eller ASP (Application Service Provider)Les mer om faget her Påmeldingsfrist: 25.08.13 / 25.01.14         Velg semester:  Høst 2013    Vår 2014     Fag Drift av lokalnettverk 4980,-         Semesteravgift og eksamenskostnader kommer i tillegg.    [-]

. This course includes identity content for Azure AD, enterprise application registration, conditional access, identity governance, and other identity tools.   TARGET AUDIENCE This course is for the Identity and Access Administrators who are planning to take the associated certification exam, or who are performing identity and access administration tasks in their day-to-day job. This course would also be helpful to an administrator or engineer that wants to specialize in providing identity solutions and access management systems for Azure-based solutions; playing an integral role in protecting an organization. COURSE OBJECTIVES Implement an identity management solution Implement an authentication and access management solutions Implement access management for apps Plan and implement an identity governancy strategy COURSE CONTENT Module 1: Implement an identity management solution Learn to create and manage your initial Azure Active Directory (Azure AD) implementation and configure the users, groups, and external identities you will use to run your solution. Lessons M1 Implement Initial configuration of Azure AD Create, configure, and manage identities Implement and manage external identities Implement and manage hybrid identity Lab 1a: Manage user roles Lab 1b: Setting tenant-wide properties Lab 1c: Assign licenses to users Lab 1d: Restore or remove deleted users Lab 1e: Add groups in Azure AD Lab 1f: Change group license assignments Lab 1g: Change user license assignments Lab 1h: Configure external collaboration Lab 1i: Add guest users to the directory Lab 1j: Explore dynamic groups After completing module 1, students will be able to: Deploy an initail Azure AD with custom settings Manage both internal and external identities Implement a hybrid identity solution Module 2: Implement an authentication and access management solution Implement and administer your access management using Azure AD. Use MFA, conditional access, and identity protection to manager your identity solution. Lessons M2 Secure Azure AD user with MFA Manage user authentication Plan, implement, and administer conditional access Manage Azure AD identity protection Lab 2a: Enable Azure AD MFA Lab 2b: Configure and deploy self-service password reset (SSPR) Lab 2c: Work with security defaults Lab 2d: Implement conditional access policies, roles, and assignments Lab 2e: Configure authentication session controls Lab 2f: Manage Azure AD smart lockout values Lab 2g: Enable sign-in risk policy Lab 2h: Configure Azure AD MFA authentication registration policy After completing module 2, students will be able to: Configure and manage user authentication including MFA Control access to resources using conditional access Use Azure AD Identity Protection to protect your organization Module 3: Implement access management for Apps Explore how applications can and should be added to your identity and access solution with application registration in Azure AD. Lessons M3 Plan and design the integration of enterprise for SSO Implement and monitor the integration of enterprise apps for SSO Implement app registration Lab 3a: Implement access management for apps Lab 3b: Create a custom role to management app registration Lab 3c: Register an application Lab 3d: Grant tenant-wide admin consent to an application Lab 3e: Add app roles to applications and recieve tokens After completing module 3, students will be able to: Register a new application to your Azure AD Plan and implement SSO for enterprise application Monitor and maintain enterprise applications Module 4: Plan and implement an identity governancy strategy Design and implement identity governance for your identity solution using entitlement, access reviews, privileged access, and monitoring your Azure Active Directory (Azure AD). Lessons M4 Plan and implement entitlement management Plan, implement, and manage access reviews Plan and implement privileged access Monitor and maintain Azure AD Lab 4a: Creat and manage a resource catalog with Azure AD entitlement Lab 4b: Add terms of use acceptance report Lab 4c: Manage the lifecycle of external users with Azure AD identity governance Lab 4d: Create access reviews for groups and apps Lab 4e: Configure PIM for Azure AD roles Lab 4f: Assign Azure AD role in PIM Lab 4g: Assign Azure resource roles in PIM Lab 4h: Connect data from Azure AD to Azure Sentinel After completing module 4, students will be able to: Mange and maintain Azure AD from creation to solution Use access reviews to maintain your Azure AD Grant access to users with entitlement management [-]

MS-500: Microsoft 365 Security Administrator [-]

Administering Microsoft SQL Server [-]

MD-102 : Microsoft 365 Endpoint Administrator [-]

DO180: Red Hat OpenShift Administration I: Operating a Production Cluster [-]

DP-300: Administering Microsoft Azure SQL Solutions [-]

AZ-700: Designing and Implementing Microsoft Azure Networking Solutions [-]

Målsetting Hensikten med kurset er å oppdatere kontrollører innen G11 Løfteredskap innen standarder, forskrifter osv. som berører en kontrollørs hverdag Emneliste Innledning Regelverk/standarder Krav til sakkyndig virksomhet Praktisk kontroll Evaluering Dokumentasjon Sertifikater Samsvarserklæring osv. Avslutningsprøve Teoretisk test Kompetansebevis / sertifikat Et kursbevis vil bli utstedt til hver kandidat som har gjennomført og bestått opplæringen. Kursbeviset vil inneholde informasjon om opplæringssted, kursinnhold, dato for gjennomføring, kandidatens navn og fødselsdato og være signert av daglig leder/kurs koordinator. [-]