IT-kurs
IT kompetanse
Rogaland
Du har valgt: Haugesund
Nullstill
Filter
Ferdig

-

27 treff ( i Haugesund ) i IT kompetanse
 

5 000 kr
5G Security [+]
5G Security [-]
Les mer
Virtuelt klasserom 5 dager 28 500 kr
This course teaches developers how to create end-to-end solutions in Microsoft Azure. Students will learn how to implement Azure compute solutions, create Azure Functions... [+]
Agenda Module 1: Creating Azure App Service Web Apps -Azure App Service core concepts-Creating an Azure App Service Web App-Configuring and Monitoring App Service apps-Scaling App Service apps-Azure App Service staging environments Module 2: Implement Azure functions -Azure Functions overview-Developing Azure Functions-Implement Durable Functions Module 3: Develop solutions that use blob storage -Azure Blob storage core concepts-Managing the Azure Blob storage lifecycle-Working with Azure Blob storage Module 4: Develop solutions that use Cosmos DB storage -Azure Cosmos DB overview-Azure Cosmos DB data structure-Working with Azure Cosmos DB resources and data Module 5: Implement IaaS solutions -Provisioning VMs in Azure-Create and deploy ARM templates-Create container images for solutions-Publish a container image to Azure Container Registry-Create and run container images in Azure Container Instances Module 6: Implement user authentication and authorization -Microsoft Identity Platform v2.0-Authentication using the Microsoft Authentication Library-Using Microsoft Graph-Authorizing data operations in Azure Storage Module 7: Implement secure cloud solutions -Manage keys, secrets, and certificates by using the KeyVault API-Implement Managed Identities for Azure resources-Secure app configuration data by using Azure App Configuration Module 8: Implement API Management -API Management overview-Defining policies for APIs-Securing your APIs Module 9: Develop App Service Logic Apps -Azure Logic Apps overview-Creating custom connectors for Logic Apps Module 10: Develop event-based solutions -Implement solutions that use Azure Event Grid-Implement solutions that use Azure Event Hubs-Implement solutions that use Azure Notification Hubs Module 11: Develop message-based solutions -Implement solutions that use Azure Service Bus-Implement solutions that use Azure Queue Storage queues Module 12: Monitor and optimize Azure solutions -Overview of monitoring in Azure-Instrument an app for monitoring-Analyzing and troubleshooting apps-Implement code that handles transient faults Module 13: Integrate caching and content delivery within solutions -Develop for Azure Cache for Redis-Develop for storage on CDNs [-]
Les mer
Bedriftsintern 2 dager 11 500 kr
This course begins with an overview of the different cloud computing models and services provided by the major public cloud providers. Several cloud computing concerns li... [+]
Course Description This course then focuses on enterprise application to cloud concerns including planning and executing a migration, building the business case, managing application dependencies, selecting a proof of concept, and serverless/managed services. A series of instructor-led demonstrations and hands-on activities provide students with practical, hands-on experience. Learning Objectives Learn what technologies enable cloud computing Understand the definition and characteristics of cloud computing Compare service models: IaaS, PaaS, SaaS, Serverless Develop the business case for a cloud migration Plan a successful cloud migration Decipher the risks of both development and security with cloud computing Analyze the costs of using cloud computing and an approach to calculating them Objection handling when dealing with projects situations around risk All courses will be delivered in partnership with ROI Training, Google Cloud Premier Partner, using a Google Authorized Trainer. Course Outline Unit 1: Enabling Technologies -Networking-Virtualization-Overview of Virtualization-Hypervisors and Containers-Security and Virtualization-Multi-tenancy Unit 2: Cloud Computing Concepts -Cloud Definition-Characteristics of Clouds-Cloud Service and Deployment Models-Public Cloud Products and Services Unit 3: Cloud Service Models -Comparing Services Offered by Google Cloud Platform (GCP), Amazon Web Services (AWS), and Azure-Compute Services-Storage Services-Kubernetes Services-Serverless and Managed Services-Big Data and Machine Learning Unit 4: Building a Business Case for the Cloud -Economic and Financial-Understand the Cloud Cost Model-Calculating the Cost of a Cloud Solution-Transform Capital Expenditures to Operating Expenditures-Agility-Lower Risk of Adopting and Evaluating New Technology-Reduce Time to Market-Quickly React as Markets and Requirements Change-Risk Mitigation-High Quality Infrastructure-Reduce Downtime-Cloud SLAs-Leveraging Hybrid and Multi-Cloud Solutions-Staff Utilization-Eliminate Mundane Operational Tasks-Harness Monitoring and Logging-Onboarding Applications and Users Unit 5: Migrating to the Public Cloud -Phases in a Successful Migration-Assessment-Proof of Concept-Data Migration-Application Migration-Employ Cloud Native Services-Cloud Native Development-Selecting Workloads-Backup / Disaster Recovery-Packaged Enterprise Software-Custom Applications-Open-Source Applications Unit 6: Security and the Cloud -Cloud-based Security Issues-Shared Responsibility Model-Security Auditing in the Cloud-Compliance with Regulatory Constraints [-]
Les mer
Virtuelt klasserom 3 timer
Skriv kursbeskrivelse her [+]
Skriv kursbeskrivelse her [-]
Les mer
Virtuelt klasserom 3 dager 23 650 kr
Due to the Coronavirus the course instructor is not able to come to Oslo. As an alternative we offer this course as a Blended Virtual Course. [+]
Blended Virtual Course The course is a hybrid of virtual training and self-study which will be a mixture of teaching using Microsoft Teams for short bursts at the beginning of the day, then setting work for the rest of the day and then coming back at the end of the day for another on-line session for any questions before setting homework in the form of practice exams for the evening. You do not have to install Microsoft Teams , you will receive a link and can access the course using the web browser.  Remote proctored examTake your exam from any location. Read about iSQI remote proctored exam here Requirements for the exam: The exam will be using Google Chrome and there is a plug-in that needs to be installed  You will need a laptop/PC with a camera and a microphone  A current ID with a picture  This 3-day course is aimed at anyone wishing to attain the ISTQB Advanced Test Automation Engineer qualification. This qualification builds upon the Foundation syllabus and provides essential skills for all those involved in test automation and who want to develop further their expertise in one or more specific areas. Bouvet sine kursdeltakeres testresultater vs ISTQB gjennomsnitt A Test Automation Engineer is one who has broad knowledge of testing in general, and an in-depth understanding in the special area of test automation. An in-depth understanding is defined as having sufficient knowledge of test automation theory and practice to be able to influence the direction that an organization and/or project takes when designing, developing and maintaining test automation solutions for functional tests. The modules offered at the Advanced Level Specialist cover a wide range of testing topics.   The course is highly practical addressing the following areas: Introduction and objectives for Test Automation This section provides an introduction to test automation explaining the objectives, advantages, disadvantages and limitations of test automation as well as technical success factors of a test automation project. Preparing for Test Automation Understanding the type of system is vital for determining the most appropriate automation solution and also how we can design systems and testing for more effective automation. This section also looks at how we can evaluate for the most appropriate tools. The generic Test Automation architecture A test automation engineer has the role of designing, developing, implementing, and maintaining test automation solutions. As each solution is developed, similar tasks need to be done, similar questions need to be answered, and similar issues need to be addressed and prioritized. These reoccurring concepts, steps, and approaches in automating testing become the basis of the generic test automation architecture, and this will be discussed in detail during this section Deployment risks and contingencies This section looks at the various risks associated with the deployment of test tools and how to avoid test automation failure. Test Automation reporting and metrics Providing information to stakeholders for them to make informed decisions about the quality of the software is a vital part of testing and this section looks at the various metrics that can be used to monitor test automation and what information should be supplied to the stakeholder and how it should be presented. Transitioning manual testing to an automated environment This section looks at the various criteria to apply to determine the suitability for automation and understanding the factors for transitioning from manual to automation testing Verifying the Test Automation solution To have justified confidence in the information we supply to the stakeholders regarding test automation we must have justified confidence in the test environment and test automation solution supporting the information Continuous improvement This section looks ahead and how we can improve the automation solution making it more effective and efficient The Exam The ISTQB Advanced Test Automation Engineer exam is a 1 hour 30 minute, 40 question multiple-choice exam totaling 75 points. The pass mark is 65% (49 out of 75). It is a pre-requisite that attendees hold the ISTQB Foundation Level certificate. [-]
Les mer
Nettstudie 1 semester 4 980 kr
På forespørsel
Datamaskinarkitektur: De viktigste komponentene og deres virkemåte og oppbygging: CPU, buss, lagerteknologier (cache og ulike typer primær- og sekundærlager), kontrollere... [+]
  Studieår: 2013-2014   Gjennomføring: Vår Antall studiepoeng: 5.0 Forutsetninger: Ingen Innleveringer: For å kunne gå opp til eksamen må 8 utvalgte øvingsoppgaver være godkjente. Det settes krav til at studenten har tilgang til en PC som kan brukes til praktiske maskinvare- og programvareendringer for å trene på feildiagnostisering og feilretting. Maskinen kan gjerne være en eldre og utdatert maskin, men den må virke. Personlig veileder: ja Vurderingsform: Skriftlig eksamen, individuell, 3 timer. Ansvarlig: Geir Ove Rosvold Eksamensdato: 20.12.13 / 23.05.14         Læremål: KUNNSKAPER:Kandidaten:- har innsikt i datamaskinens virkemåte både fra et teoretisk og praktisk ståsted- kjenner godt til de enkelte komponenter i datamaskinen og hvordan de virker sammen- kjenner til de grunnleggende matematikk- og informatikktema (tallsystemer, datarepresentasjon, lokalitet) som er relevante for emnets tekniske hovedtemaer FERDIGHETER:Kandidaten:- kan gjøre nytte av sine teoretiske kunnskaper inne emnets tema i relevant praktisk problemløsing- kan optimalisere, oppgradere og holde ved like en datamaskin, samt diagnostisere, feilsøke og reparere en datamaskin ved de vanligste feilsituasjoner GENERELL KOMPETANSE:Kandidaten:- har kompetanse til selvstendig både å formidle og å ta i bruk sine kunnskaper og ferdigheter innen emnets tema- kan i en praktisk driftssituasjon, forklare og gjøre bruk av sin kunnskap både innen hvert enkelt tema i faget og på tvers av temaene Innhold:Datamaskinarkitektur: De viktigste komponentene og deres virkemåte og oppbygging: CPU, buss, lagerteknologier (cache og ulike typer primær- og sekundærlager), kontrollere og io-utstyr, avbruddsmekanismen, DMA, brikkesett og moderne systemarkitektur, ulike maskinklasser. Prosessorarkitektur: Pipeline, superskalaritet, dynamisk utføring, mikrooperasjoner, kontrollenheten, hardkoding kontra mikroprogrammering, RISC og CISC. Teori-tema: Tallsystemer. Datarepresentasjon og -aritmetikk. Buss- og lagerhierarki. Cache og lokalitet. Høynivåspråk kontra assembly. Praktisk driftsarbeid: Kabinett, hovedkort, ulike prosessorer, buss, RAM, cache, BIOS. Lyd-, nettverks-og skjermkort. Sekundærminne (Harddisk, CD-ROM, DVD, tape og andre typer). Avbruddsmekanismen, I/O, DMA og busmastering. Å oppdage og rette feil. Boot-prosessen. Formatering, partisjonering.Les mer om faget her [-]
Les mer
4 dager 25 000 kr
AI-102 Designing and Implementing an Azure AI Solution is intended for software developers wanting to build AI infused applications that leverage Azure Cognitive Services... [+]
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 [-]
Les mer
Virtuelt klasserom 3 dager 20 000 kr
In this course, the students will implement various data platform technologies into solutions that are in line with business and technical requirements including on-premi... [+]
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 [-]
Les mer
Oslo 5 dager 30 000 kr
22 Apr
22 Apr
https://www.glasspaper.no/kurs/dp-203-data-engineering-on-microsoft-azure/ [+]
DP-203: Data Engineering on Microsoft Azure [-]
Les mer
Virtuelt klasserom 5 dager 28 500 kr
This course teaches Solutions Architects how to translate business requirements into secure, scalable, and reliable solutions. Lessons include virtualization, automation,... [+]
Agenda Module 1: Implement VMs for Windows and Linux -Select Virtual Machine Size-Configure High Availability-Implement Azure Dedicated Hosts-Deploy and Configure Scale Sets-Configure Azure Disk Encryption Module 2: Automate Deployment and Configuration of Resources -Azure Resource Manager Templates-Save a Template for a VM-Evaluate Location of New Resources-Configure a Virtual Hard Disk Template-Deploy from a Template-Create and Execute an Automation Runbook Module 3: Implement Virtual Networking -Virtual Network Peering-Implement VNet Peering Module 4: Implement Load Balancing and Network Security -Implement Azure Load Balancer-Implement an Application Gateway-Understand Web Application Firewall-Implement Azure Firewall-Implement Azure Front Door-Implementing Azure Traffice Manager-Implement Network Security Groups and Application Security Grou-Implement Azure Bastion Module 5: Implement Storage Accounts -Storage Accounts-Blob Storage-Storage Security-Managing Storage-Accessing Blobs and Queues using AAD-Configure Azure Storage Firewalls and Virtual Networks Module 6: Implement Azure Active Directory -Overview of Azure Active Directory-Users and Groups-Domains and Custom Domains-Azure AD Identity Protection-Implement Conditional Access-Configure Fraud Alerts for MFA-Implement Bypass Options-Configure Trusted IPs-Configure Guest Users in Azure AD-Manage Multiple Directori Module 7: Implement and Manage Azure Governance -Create Management Groups, Subscriptions, and Resource Groups-Overview of Role-Based Access Control (RBAC)-Role-Based Access Control (RBAC) Roles-Azure AD Access Reviews-Implement and Configure an Azure Policy-Azure Blueprints Module 8: Implement and Manage Hybrid Identities -Install and Configure Azure AD Connect-Configure Password Sync and Password Writeback-Configure Azure AD Connect Health Module 9: Manage Workloads in Azure -Migrate Workloads using Azure Migrate-VMware - Agentless Migration-VMware - Agent-Based Migration-Implement Azure Backup-Azure to Azure Site Recovery-Implement Azure Update Management Module 10: Implement Cloud Infrastructure Monitoring -Azure Infrastructure Security Monitoring-Azure Monitor-Azure Workbooks-Azure Alerts-Log Analytics-Network Watcher-Azure Service Health-Monitor Azure Costs-Azure Application Insights-Unified Monitoring in Azure Module 11: Manage Security for Applications -Azure Key Vault-Azure Managed Identity Module 12: Implement an Application Infrastructure -Create and Configure Azure App Service-Create an App Service Web App for Containers-Create and Configure an App Service Plan-Configure Networking for an App Service-Create and Manage Deployment Slots-Implement Logic Apps-Implement Azure Functions Module 13: Implement Container-Based Applications -Azure Container Instances-Configure Azure Kubernetes Service Module 14: Implement NoSQL Databases -Configure Storage Account Tables-Select Appropriate CosmosDB APIs Module 15: Implement Azure SQL Databases -Configure Azure SQL Database Settings-Implement Azure SQL Database Managed Instances-High-Availability and Azure SQL Database [-]
Les mer
Bedriftsintern 1 dag 7 500 kr
Data science og maskinlæring er blitt en viktig drivkraft bak mange forretnings beslutninger. Men fortsatt er mange usikre på hva begrepene innebærer og hvilke muligheter... [+]
Dette kurset tilbys som bedriftsinternt kurs   Maskinlæring handler om sette datamaskiner i stand til å lære fra og utvikle atferd basert på data. Det vil si at en datamaskin kan løse en oppgave den ikke er eksplisitt programmert for å håndtere. I stedet er den i stand til å automatisk lære gjenkjenning av komplekse mønstre i data og gjøre beslutninger basert på dette disse. Maskinlæring gir store muligheter, men mange bedrifter har problemer med å ta teknologien i bruk. Nøyaktig hvilke oppgaver kan maskinlæring utføre, og hvordan kommer man i gang? Dette kurset gir oversikt over mulighetene som ligger i maskinlæring, og hvordan i tillegg til kunnskap om hvordan teknologien kan løse oppgaver og skape resultater i praksis. Hva er maskinlæring, datavitenskap og kunstig intelligens og hvordan det er relatert til statistikk og dataanalyse? Hvordan å utvinne kunnskap fra dataene dine? Hva betyr Big data og hvordan analyseres det? Hvor og hvordan skal du bruke maskinlæring til dine daglige forretningsproblemer? Hvordan bruke datamønstre til å ta avgjørelser og spådommer med eksempler fra den virkelige verden? Hvilke typer forretningsproblemer kan en maskinen lære å håndtere Muligheter som maskinlæring gir din bedrift Hva er de teoretiske aspekter på metoder innen maskinlæring? Hvilke ML-metoder som er relevante for ulike problemstillinger innen dataanalyse? Hvordan evaluere styrker og svakheter mellom disse algoritmene og velge den beste? Anvendt data science og konkrete kunde eksempler i praksis   Målsetning Kurset gir kunnskap om hvordan maskinlæring kan løse et bestemt problem og hvilke metoder som egner seg i en gitt situasjon. Du blir i stand til å kan skaffe deg innsikt i data, og vil kunne identifisere egenskapene som representerer dem best. Du kjenner de viktigste maskinlæringsalgoritmene og hvilke metoder som evaluerer ytelsen deres best. Dette gir grunnlag for kontinuerlig forbedring av løsninger basert på maskinlæring.   [-]
Les mer
Virtuelt klasserom 2 dager 14 000 kr
In this course, the students will design various data platform technologies into solutions that are in line with business and technical requirements. This can include on-... [+]
The students will also explore how to design data security including data access, data policies and standards. They will also design Azure data solutions which includes the optimization, availability and disaster recovery of big data, batch processing and streaming data solutions. Agenda Module 1: Data Platform Architecture Considerations. -Core Principles of Creating Architectures-Design with Security in Mind-Performance and Scalability-Design for availability and recoverability-Design for efficiency and operations-Case Study Module 2: Azure Batch Processing Reference Architectures. -Lambda architectures from a Batch Mode Perspective-Design an Enterprise BI solution in Azure-Automate enterprise BI solutions in Azure-Architect an Enterprise-grade Conversational Bot in Azure Module 3: Azure Real-Time Reference Architectures. -Lambda architectures for a Real-Time Perspective-Lambda architectures for a Real-Time Perspective-Design a stream processing pipeline with Azure Databricks-Create an Azure IoT reference architecture Module 4: Data Platform Security Design Considerations. -Defense in Depth Security Approach-Network Level Protection-Identity Protection-Encryption Usage-Advanced Threat Protection Module 5: Designing for Resiliency and Scale. -Design Backup and Restore strategies-Optimize Network Performance-Design for Optimized Storage and Database Performance-Design for Optimized Storage and Database Performance-Incorporate Disaster Recovery into Architectures-Design Backup and Restore strategies Module 6: Design for Efficiency and Operations. -Maximizing the Efficiency of your Cloud Environment-Use Monitoring and Analytics to Gain Operational Insights-Use Automation to Reduce Effort and Error [-]
Les mer
1 dag 6 200 kr
Data genereres i stadig større mengder - av mennesker, av sensorer og av innebygde dataenheter. Mottak, behandling og analyse av store datamengder krever distribuerte tek... [+]
Data genereres i stadig større mengder - av mennesker, av sensorer og av innebygde dataenheter. Mottak, behandling og analyse av store datamengder krever distribuerte teknologier og lagringsformater. Big Data er blitt et fellesbegrep på disse teknologiene og dataene de behandler. Det er i dag forretningskritisk innenfor flere og flere bransjer å kunne håndtere Big Data. Men hvor skal man begynne? Kursinnhold Hvordan defineres Big Data? Hvilke problemstillinger kan løses med Big Data Hvilke Big Data teknologier finnes og hvilke bør vi satse på? Hva er hovedutfordringene med å ta i bruk Big Data? Kurset gjennomføres som en serie foredrag med rom for spørsmål og utdypninger innen hvert emne. De mest brukte teknologiene innen Big Data lagring, datahåndtering og analyse blir gjennomgått og vurdert, inkludert Hadoop, Spark, Hive, HBase, Cassandra, Kafka, MongoDB og en rekke andre. [-]
Les mer
1 dag 8 000 kr
This course introduces fundamentals concepts related to artificial intelligence (AI), and the services in Microsoft Azure that can be used to create AI solutions. [+]
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 [-]
Les mer
Virtuelt klasserom 2 dager 15 000 kr
This course will provide foundational level knowledge of cloud services and how those services are provided with Microsoft Azure. The course can be taken as an optional f... [+]
The course will cover general cloud computing concepts as well as general cloud computing models and services such as Public, Private and Hybrid cloud and Infrastructure-as-a-Service (IaaS), Platform-as-a-Service(PaaS) and Software-as-a-Service (SaaS). It will also cover some core Azure services and solutions, as well as key Azure pillar services concerning security, privacy, compliance and trust. It will finally cover pricing and support services available.   Agenda Module 1: Cloud Concepts -Learning Objectives-Why Cloud Services?-Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS) and Software-as-a-Service (SaaS)-Public, Private, and Hybrid cloud models Module 2: Core Azure Services -Core Azure architectural components-Core Azure Services and Products-Azure Solutions-Azure management tools Module 3: Security, Privacy, Compliance and Trust -Securing network connectivity in Azure-Core Azure Identity services-Security tools and features-Azure governance methodologies-Monitoring and Reporting in Azure-Privacy, Compliance and Data Protection standards in Azure Module 4: Azure Pricing and Support -Azure subscriptions-Planning and managing costs-Support options available with Azure-Service lifecycle in Azure [-]
Les mer