Prosjektledelse og IT-kompetanse

AI-3002: Create document intelligence solutions with Azure AI Document Intelligence [-]

Modern Service Oriented Architecture [-]

AI-102: Designing and Implementing a Microsoft Azure AI Solution [-]

AI-050: Develop Generative AI Solutions with Azure OpenAI Service [-]

 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   [-]

Modern Application Architecture [-]

Her lærer du hvordan du på en moderne måte kan skaffe deg kontroll og oversikt over tilstanden på maskiner og utstyr, finne kritiske måleparametere i komponenter og systemer, finne sammenhenger og definere KPIer, samt organisere dataene og lage dashboard. Skjematikk for å skisse opp et overvåkingssystem P&ID Tagge sensorer   Definere/lage hierarkier Datafangst Sensorer, typer og måleområder Dataoverføring OPC-UA og MQTT Lage og sette opp systemer for tilstandsovervåking Finne sammenhenger og strategisk viktige målepunkter i KRMs pumpe & ventilstasjon og hydraulikkanlegg Tegne opp forslag til tilstandsovervåking av hydraulikkanlegget Tegne opp forslag til tilstandsovervåking av pumpe & ventilstasjonen  Lage dashboards i Grafana for tilstandsovervåking av hydraulikkanlegget Lage dashboards i Grafana for tilstandsovervåking av pumpe & ventilstasjonen Avsluttende gruppeoppgave   Lage og sette opp et effektivt system for tilstandsovervåking   [-]

CEH: Certified Ethical Hacker v13 [-]

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 [-]

Objectives Describe specialized data classifications on Azure Identify Azure data protection mechanisms Implement Azure data encryption methods Secure Internet protocols and how to implement them on Azure Describe Azure security services and features Agenda Module 1: Identity and Access -Configure Azure Active Directory for Azure workloads and subscriptions-Configure Azure AD Privileged Identity Management-Configure security for an Azure subscription Module 2: Platform Protection -Understand cloud security-Build a network-Secure network-Implement host security-Implement platform security-Implement subscription security Module 3: Security Operations -Configure security services-Configure security policies by using Azure Security Center-Manage security alerts-Respond to and remediate security issues-Create security baselines Module 4: Data and applications -Configure security policies to manage data-Configure security for data infrastructure-Configure encryption for data at rest-Understand application security-Implement security for application lifecycle-Secure applications-Configure and manage Azure Key Vault       [-]

Kursinnhold• Systemutvikling, prosess og metode• Datahåndteringe og databaser  • Programmering• Brukergrensesnitt og webdesign   UndervisningsformKlasseromsundervisning med prosjektor hvor deltakerne får tildelt PC med nødvendig programvare installert. Praktisk trening med øvingsoppgaver for å aktivisere kunnskapen.   InstruktørerVi har erfarne instruktørene med høy kompetanse, lang erfaring og dyktige pedagogiske evner.   Målsetning Modul Build består av de tradisjonelle, tekniske aspektene ved design, spesifikasjon, utvikling, testing, integrering og anvendelse av IT-systemer. Det krever at kandidaten forstår systemutviklingens livssyklus, kjenner til den typiske utviklingsprosessen og er klar over de nyeste utviklingstrekk innen programvare. Kandidaten skal også kunne benytte relevante metoder og verktøy. Kandidaten skal kjenne til designprinsipper knyttet til brukergrensesnitt, websider og hypermedia, så vel som de som brukes i relasjonsdatabaser og datavarehus. Kandidaten vil også få en grunnleggende kunnskap om søkespråk og være oppmerksom på noen viktige databaseadministrasjonsproblemer. I tillegg skal kandidaten være i stand til å anvende typiske datastrukturer og algoritmer, gjenkjenne ulike programmeringsspråk og konstruksjoner, vurdere problemer med å opprettholde systemer, samt kjenne til dokumentasjon og testing av software systemer. [-]

Kursinnhold     * Organisasjoner og bruk av IT    * IT- ledelse                     * Verdsettelse av IT    * Den globale nettverksøkonomien    * Prosjektledelse    * Samarbeid og kommunikasjon    * Juridiske og etiske problemstillinger   UndervisningsformKlasseromsundervisning med prosjektor hvor deltakerne får tildelt PC med nødvendig programvare installert. Praktisk trening med øvingsoppgaver for å aktivisere kunnskapen.     InstruktørerVi har erfarne instruktørene med høy kompetanse, lang erfaring og dyktige pedagogiske evner.     MålsetningModul Plan, ser på organisasjoner og deres bruk av IT, både som en tilrettelegger for effektive informasjonsystemer, og som en plattform for innovasjon. Modulen krever at kandidaten skal ha en grundig forståelse av organisasjoner, deres strategier og forretningsprosesser, samt de globale trender og muligheter som er involvert. Kandidaten skal kjenne igjen de viktigste problemstillinger knyttet til styringen av IT, som for eksempel å velge riktig teknologi, eller å velge mellom utvikling av interne systemer eller outsourcing. Kandidaten skal også kunne begrunne IT-investeringer og få kjennskap til noen av de juridiske og etiske aspekter ved bruken av IT.   Kandidaten skal bli oppmerksom på kravet om en profesjonell tilnærming til prosjektledelse og kvalitetsikring. Kandidaten skal også forstå betydningen av teambygging og effektivt kommunikasjon når man presenterer sin analyse eller beslutning for organisasjonen.   [-]

  An Azure IoT Developer is responsible for implementing and then maintaining the cloud and edge portions of an Azure IoT solution. In addition to configuring and maintaining devices by using Azure IoT services and other Microsoft tools, the IoT Developer also sets up the physical devices and is responsible for maintaining the devices throughout the life cycle. The IoT Developer implements designs for IoT solutions, including device topology, connectivity, debugging and security. For Edge device scenarios, the IoT Developer also deploys compute/containers and configures device networking, which could include various edge gateway implementations. The IoT Developer implements designs for solutions to manage data pipelines, including monitoring and data transformation as it relates to IoT. The IoT Developer works with data engineers and other stakeholders to ensure successful business integration. IoT Developers should have a good understanding of Azure services, including data storage options, data analysis, data processing, and the Azure IoT PaaS versus SaaS options. After completing this course, students will be able to: Create, configure, and manage an Azure IoT hub. Provision devices by using IoT Hub and DPS, including provisioning at scale. Establish secure 2-way communication between devices and IoT Hub. Implement message processing by using IoT Hub routing and Azure Stream Analytics. Configure the connection to Time Series Insights and support business integration requirements. Implement IoT Edge scenarios using marketplace modules and various edge gateway patterns. Implement IoT Edge scenarios that require developing and deploying custom modules and containers. Implement device management using device twins and direct methods. Implement solution monitoring, logging, and diagnostics testing. Recognize and address security concerns and implement Azure Security Center for IoT. Build an IoT Solution by using Azure IoT Central and recongize SaaS opportunities for IoT. Course prerequisites IoT Developers should have basic programming skills in at least one Azure-supported language, including C#, Node.js, C, Python, or Java. Software development experience is a prerequisite for this course, but no specific software language is required, and the experience does not need to be at a professional level. Data Processing Experience: General understanding of data storage and data processing is a recommended but not required.  Cloud Solution Awareness: Students should have a basic understanding of PaaS, SaaS, and IaaS implementations. Microsoft Azure Fundamentals (M-AZ-900T00/M-AZ900), or equivalent skills, is recommended.  This course helps to prepare for exam AZ-220.   Agenda Module 1: Introduction to IoT and Azure IoT Services -Business Opportunities for IoT-Introduction to IoT Solution Architecture-IoT Hardware and Cloud Services Module 2: Devices and Device Communication -IoT Hub and Devices-IoT Developer Tools-Device Configuration and Communication Module 3: Device Provisioning at Scale -Device Provisioning Service Terms and Concepts-Configure and Manage the Device Provisioning Service-Device Provisioning Tasks Module 4: Message Processing and Analytics -Messages and Message Processing-Data Storage Options-Azure Stream Analytics Module 5: Insights and Business Integration -Business Integration for IoT Solutions-Data Visualization with Time Series Insights-Data Visualization with Power BI Module 6: Azure IoT Edge Deployment Process -Introduction to Azure IoT Edge-Edge Deployment Process-Edge Gateway Devices Module 7: Azure IoT Edge Modules and Containers -Develop Custom Edge Modules-Offline and Local Storage Module 8: Device Management -Introduction to IoT Device Management-Manage IoT and IoT Edge Devices-Device Management at Scale Module 9: Solution Testing, Diagnostics, and Logging -Monitoring and Logging-Troubleshooting Module 10: Azure Security Center and IoT Security Considerations -Security Fundamentals for IoT Solutions-Introduction to Azure Security Center for IoT-Enhance Protection with Azure Security Center for IoT Agents Module 11: Build an IoT Solution with IoT Central -Introduction to IoT Central-Create and Manage Device Templates-Manage Devices in Azure IoT Central [-]

5G Security [-]

    Du lærer å designe og implementere ML-Ops som kan skape verdi i praksis Bli med på et intensivt kurs som tar deg dypt inn i ML-Ops og lærer deg å identifisere de beste områdene for implementering av teknologien. Over tre dager blir du introdusert for håndgripelige eksempler, mønsterpraksis og metodikk. Fokusområdene inkluderer dataadministrasjon, ML-Ops-arbeidsflyter, modellutrulling, modellomskolering, modellarkiv, monitorering og A/B-testing. Det vil si: Alt du trenger å vite, så du ikke skal finne opp ML-hjulet på nytt hver gang. Men kurset handler ikke bare om å lære deg tekniske ferdigheter. Det er laget for deg som vil bruke ML-Ops til å skape målbare forretningsverdier. For hvor mye nytte har du av en modell som aldri blir satt i produksjon? Du får også innsikt i hvor viktig god kommunikasjon er for ML-Ops. Når du har fullført kurset, vil du være i stand til å designe og rulle ut ende-til-ende ML-Ops-kommandokøer i virkelige prosjekter. Du vil være i stand til å skape brukbare arkitekturer som kan ta virksomheten din til nye høyder.    Dette får du med deg hjem: Dyp forståelse for håndtering og vedlikehold av ML gjennom en full livssyklus. Kjennskap til hvordan du gjennomfører en case-studie for å identifisere prosjekter med høy avkastning, lav implementasjonskompleksitet og begrenset risiko. Evnen til å sette ut maskinlæringsmodeller i faktisk produksjon. Forståelse for en ML-Ops-arbeidsflyt, og alle dens komponenter. Praktisk erfaring med ML-Ops-verktøy og -teknologier. Kunnskapen som trengs for å designe passende ML-Ops-arkitektur for prosjekter av ulik modenhetsgrad. En solid forståelse for viktigheten av samarbeid og kommunikasjon innen ML-Ops. Kjennskap til og kunnskap om ML-Ops-bestepraksis. En forståelse for «Model State»-, «Data Drift»- og «Model Drift»-monitorering. Kjennskap til A/B-testing med mål om å velge modellen som egner seg best. Kjennskap til «Shadow/Canary Mode»-utrulling og hvordan disse både kan brukes for å minimere risiko og for å bygge tillit til nye modeller. Forståelse for ulike teknikker når det gjelder modell-gjentrening. Evnen til å designe og implementere skalerbare, ende-til-ende ML-Ops-kommandokøer for reelle prosjekter.   Derfor skal du ta dette kurset: Brenner du etter å ta maskinlæringsferdighetene dine til neste nivå? Eller higer du etter å skape konkret forretningsverdi ved hjelp av ML-Ops? I 2019 rapporterte VentureBeat at så mye som 87 % av alle maskinlæringsmodeller aldri kommer så langt som til å bli satt i produksjon. Med andre ord: Det er bare drøye ti prosent av alle ML-modeller som faktisk kan brukes for å skape verdi!  Den største grunnen til at så få lykkes er at prosjektene ikke klarer å levere på forretningsmålene sine. De stiller feil spørsmål, og får feil svar. Som en direkte konsekvens av dette er kompetansen innen maskinlæring sterkt vektet mot prototyping, på bekostning av praktisk ML-Ops-erfaring.    Målet med dette kurset er å bygge en bro over dette gapet. Kurset gir deg kunnskapen til å sette modellene dine i produksjon – med høy suksessrate. Du kommer til å lære hvordan du skal identifisere prosjekter med høyt potensiale, lav kompleksitet og lav risiko. Med denne kunnskapen øker sannsynligheten for at du ikke bare vil levere på forretningsmålene dine, men at du kommer til å overlevere! Ikke kast bort tiden eller ekspertisen din – bli med på et kurs som gir deg innsikt i verktøyene og metodikken som bidrar til at du kan maksimere forretningsverdien i din organisasjon!   [-]