Oversikt over relevante kurs innen IT-kurs i Våler kommune

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.   [-]

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

AZ-305: Microsoft Azure Architect Design [-]

AI-900: Microsoft Azure AI Fundamentals [-]

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 [-]

COURSE OVERVIEW The course is not designed to teach students to become professional data scientists or software developers, but rather to build awareness of common AI workloads and the ability to identify Azure services to support them. The course is designed as a blended learning experience that combines instructor-led training with online materials on the Microsoft Learn platform (https://azure.com/learn). The hands-on exercises in the course are based on Learn modules, and students are encouraged to use the content on Learn as reference materials to reinforce what they learn in the class and to explore topics in more depth. TARGET AUDIENCE The Azure AI Fundamentals course is designed for anyone interested in learning about the types of solution artificial intelligence (AI) makes possible, and the services on Microsoft Azure that you can use to create them. You don’t need to have any experience of using Microsoft Azure before taking this course, but a basic level of familiarity with computer technology and the Internet is assumed. Some of the concepts covered in the course require a basic understanding of mathematics, such as the ability to interpret charts. The course includes hands-on activities that involve working with data and running code, so a knowledge of fundamental programming principles will be helpful. COURSE OBJECTIVES  After completing this course, you will be able to: Describe Artificial Intelligence workloads and considerations Describe fundamental principles of machine learning on Azure Describe features of computer vision workloads on Azure Describe features of Natural Language Processing (NLP) workloads on Azure Describe features of conversational AI workloads on Azure   COURSE CONTENT Module 1: Introduction to AI In this module, you'll learn about common uses of artificial intelligence (AI), and the different types of workload associated with AI. You'll then explore considerations and principles for responsible AI development. Artificial Intelligence in Azure Responsible AI After completing this module you will be able to Describe Artificial Intelligence workloads and considerations Module 2: Machine Learning Machine learning is the foundation for modern AI solutions. In this module, you'll learn about some fundamental machine learning concepts, and how to use the Azure Machine Learning service to create and publish machine learning models. Introduction to Machine Learning Azure Machine Learning After completing this module you will be able to Describe fundamental principles of machine learning on Azure Module 3: Computer Vision Computer vision is a the area of AI that deals with understanding the world visually, through images, video files, and cameras. In this module you'll explore multiple computer vision techniques and services. Computer Vision Concepts Computer Vision in Azure After completing this module you will be able to Describe features of computer vision workloads on Azure Module 4: Natural Language Processing This module describes scenarios for AI solutions that can process written and spoken language. You'll learn about Azure services that can be used to build solutions that analyze text, recognize and synthesize speech, translate between languages, and interpret commands. After completing this module you will be able to Describe features of Natural Language Processing (NLP) workloads on Azure Module 5: Conversational AI Conversational AI enables users to engage in a dialog with an AI agent, or *bot*, through communication channels such as email, webchat interfaces, social media, and others. This module describes some basic principles for working with bots and gives you an opportunity to create a bot that can respond intelligently to user questions. Conversational AI Concepts Conversational AI in Azure After completing this module you will be able to Describe features of conversational AI workloads on Azure   TEST CERTIFICATION Recommended as preparation for the following exams: Exam AI-900: Microsoft Azure AI Fundamentals. 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 [-]

The course combines lecture with case studies to demonstrate basic architect design principles. Successful students have experience and knowledge in IT operations, including networking, virtualization, identity, security, business continuity, disaster recovery, data platforms, and governance. Students also have experience designing and architecting solutions. COURSE OBJECTIVES Skills gained Design a governance solution. Design a compute solution. Design an application architecture. COURSE CONTENT Module 1: Design compute and application solutions In this module you will learn about governance, compute, and application architectures. Lessons of Module 1 Design for governance Design for compute solutions Design for application architectures Lab : Case studies of Module 1 After completing this module, students will be able to: Design a governance solution. Design a compute solution. Design an application architecture. Module 2: Design storage solutions In this module, you will learn about non-relational storage, relational storage, and data integration solutions. Lessons of Module 2 Design a non-relational storage solution. Design a relational storage solution. Design a data integration solution. Lab : Case studies of Module 2 After completing this module, students will be able to: Design non-relational storage solutions. Design relational storage solutions. Design a data integration solution. Module 3: Design networking and access solutions In this module you will learn about authentication and authorization, identity and access for applications, and networking solutions. Lessons of Module 3 Design authentication and authorization solutions Design networking solutions Lab : Case studies of Module 3 After completing this module, students will be able to: Design authentication and authorization solutions. Design network solutions. Module 4: Design business continuity solutions Lessons of Module 4 Design for backup and disaster recovery Design monitoring solutions Design for migrations Lab : Case studies of Module 4 After completing this module, students will be able to: Design backup and disaster recovery. Design monitoring solutions. Design for migrations. [-]

Java SE Advanced Techniques (Course II for exam 1Z0-819) [-]

PL-300: Microsoft Power BI Data Analyst [-]

MS-500: Microsoft 365 Security Administrator [-]

AZ-140: Configuring and Operating Microsoft Azure Virtual Desktop [-]

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 [-]

Docker and Kubernetes Development [-]

Studieår: 2013-2014   Gjennomføring: Høst og vår Antall studiepoeng: 5.0 Forutsetninger: Ingen Innleveringer: Større og mindre øvinger tilsvarende 8 øvinger, hvor 6 må være godkjent før endelig karakter settes. Personlig veileder: ja Vurderingsform: Karakteren i faget settes på grunnlag av to eksamensdeler - et prosjekt (60 %) og en netteksamen (40 %). Prosjektet går over 5 uker og gjennomføres som gruppearbeid. I vurderingen av prosjektet teller prosess, dokumentasjon og produkt. Individuelle karakterer kan gis ved manglende deltagelse. Netteksamen varer 1 time og består av både flervalgs- og fritekstspørsmål. Både prosjekt, netteksamen og obligatoriske øvinger må være bestått for å få karakter i faget. Klageadgang gjelder for hver enkelt eksamensdel. Ansvarlig: Atle Nes Eksamensdato: 11.12.13 / 14.05.14         Læremål: Etter å ha gjennomført emnet Webutvikling 1 skal studenten ha følgende læringsutbytte: KUNNSKAPER:Kandidaten:- forstår klient-tjener-arkitektur i konteksten nettleser og webtjener.- kjenner til forskjellen på statiske og dynamiske websider.- kjenner til HTTP-protokollen og kryptert kommunikasjon med HTTPS.- forstår oppbygningen til en URL, domenenavn og porter.- vet forskjellen på absolutt og relativ adressering.- kjenner til virkemåten til søkemotorer.- forstår viktigheten av å følge web-standarder. FERDIGHETER:Kandidaten:- kan utvikle et funksjonelt nettsted ved bruk en enkel testeditor og HTML eller XHTML.- kan laste opp nettstedet til webtjener med SFTP.- kan endre utseendet på nettstedet med intern eller ekstern CSS.- kan bruke DOM og JavaScript til å lage dynamiske nettsider.- kan legge til multimedia (grafikk, bilder, lyd, video) på nettstedet.- kan integrere eksterne tjenester på nettstedet. GENERELL KOMPETANSE:Kandidaten:- får en grunnleggende forståelse av hvordan et moderne nettsted er oppbygd. Innhold:Introduksjon til webpublisering, HTML og XHTML, CSS, prinsipper for webdesign, DOM og JavaScript, XML (SVG og RSS), multimedia på web (grafikk, bilder, lyd og video), integrasjon av eksterne tjenester.Les mer om faget her Påmeldingsfrist: 25.08.13 / 25.01.14         Velg semester:  Høst 2013    Vår 2014     Fag Webutvikling 1 4980,-         Semesteravgift og eksamenskostnader kommer i tillegg.    [-]

The students will also explore how to implement data security including authentication, authorization, data policies and standards. They will also define and implement data solution monitoring for both the data storage and data processing activities. Finally, they will manage and troubleshoot Azure data solutions which includes the optimization and disaster recovery of big data, batch processing and streaming data solutions. Agenda Module 1: Azure for the Data Engineer -Explain the evolving world of data-Survey the services in the Azure Data Platform-Identify the tasks that are performed by a Data Engineer-Describe the use cases for the cloud in a Case Study Module 2: Working with Data Storage. -Choose a data storage approach in Azure-Create an Azure Storage Account-Explain Azure Data Lake storage-Upload data into Azure Data Lake Module 3: Enabling Team Based Data Science with Azure Databricks. -Explain Azure Databricks and Machine Learning Platforms-Describe the Team Data Science Process-Provision Azure Databricks and workspaces-Perform data preparation tasks Module 4: Building Globally Distributed Databases with Cosmos DB. -Create an Azure Cosmos DB database built to scale-Insert and query data in your Azure Cosmos DB database-Provision a .NET Core app for Cosmos DB in Visual Studio Code-Distribute your data globally with Azure Cosmos DB Module 5: Working with Relational Data Stores in the Cloud. -SQL Database and SQL Data Warehouse-Provision an Azure SQL database to store data-Provision and load data into Azure SQL Data Warehouse Module 6: Performing Real-Time Analytics with Stream Analytics. Module 7: Orchestrating Data Movement with Azure Data Factory. -Explain how Azure Data Factory works-Create Linked Services and datasets-Create pipelines and activities-Azure Data Factory pipeline execution and triggers Module 8: Securing Azure Data Platforms. -Configuring Network Security-Configuring Authentication-Configuring Authorization-Auditing Security Module 9: Monitoring and Troubleshooting Data Storage and Processing. -Data Engineering troubleshooting approach-Azure Monitoring Capabilities-Troubleshoot common data issues-Troubleshoot common data processing issues Module 10: Integrating and Optimizing Data Platforms. -Integrating data platforms-Optimizing data stores-Optimize streaming data-Manage disaster recovery [-]