Oversikt over relevante kurs innen IT-kurs i Agia Marina

Kurset fokuserer på integrering av forskjellige verdistrømmer og aktiviteter for å lage, levere og støtte IT-aktiverte produkter og tjenester, samtidig som den dekker støtte for praksis, metoder og verktøy. Kurset gir kandidatene forståelse for tjenestekvalitet og forbedringsmetoder. E-læringskurset inneholder 18 timer med undervisning, og er delt inn i 8 moduler. Les mer om ITIL® 4 på AXELOS sine websider. Inkluderer: Tilgang til ITIL® 4 Specialist: Create, Deliver and Support e-læring (engelsk) i 12 måneder. ITIL® 4 Specialist: Create, Deliver and Support online voucher til sertifiseringstest.   ITIL®/PRINCE2®/MSP®/MoP® are registered trademarks of AXELOS Limited, used under permission of AXELOS Limited. All rights reserved. [-]

Kurset retter seg til dere som jobber med kummer og rør, enten som prosjekterende, kravstiller, regulerende eller besluttende. Kurset passer nye brukere, eller for deg som bare behøver litt oppfriskning. Hensikten med kurset er å gi deltagerne en god forståelse i bruken av Focus VARDAK som design-, modellering- og kommunikasjonsverktøy. Kurset er nødvendig for å komme raskt i gang med Focus VARDAK, og for å få den nødvendige forståelse for de mulighetene programmet gir. Kursinnhold: Du vil lære grunnleggende teknikk for bruk av programmet, og skal kunne bruke programmet til å lese, endre og produsere 2D og 3D-modeller av kummer og bruke Focus VARDAK-biblioteket. Bli kjent med brukergrensesnittet i Focus VARDAK Bruk av VARDAK-verktøy Behandling av nodepunkter (tilkoblingspunkter) Navigering i VARDAK-katalogen Redigering av innsatte produkter (blokker) Produksjon av 2D-tegninger Opptegning av kum systemer i 2D og 3D Tilegne produkter materialer Rendere ut 3D tegninger Tips og triks for hvordan jobbe smartere i Focus VARDAK For deg som jobber med VA. [-]

Oppdag den intuitive og robuste verdenen av 3D-modellering med "SketchUp: Komplett", et omfattende kurs ledet av Espen Faugstad hos Utdannet.no. SketchUp, populært blant arkitekter, ingeniører, snekkere og kreative fagfolk, er et gratis verktøy som lar deg designe i både to- og tredimensjonalt perspektiv. Dette kurset er designet for alle som ønsker å lære å bruke SketchUp effektivt, uavhengig av tidligere erfaring. Kurset vil guide deg gjennom SketchUps grunnleggende, inkludert oppsett, verktøy og paneler, og hvordan du skaper to- og tredimensjonale figurer. Du vil lære å kontrollere kameraet, anvende ulike visningsstiler og manipulere objekter med en rekke verktøy. Videre dekkes tegning av figurer, måling og merking av modeller, organisering av prosjekter, samt arbeid med komponenter, materialer og teksturer. Med dette kurset vil du utvikle ferdigheter for å lage detaljerte og nøyaktige 3D-modeller og bli i stand til å presentere dine design på en overbevisende måte. Ved kursets slutt vil du ha en solid forståelse av SketchUp, noe som gjør deg i stand til å bruke programmet for en rekke prosjekter, fra enkle skisser til komplekse arkitektoniske design.   Innhold: Kapittel 1: Introduksjon Kapittel 2: Kamera Kapittel 3: Visning Kapittel 4: Manipulere Kapittel 5: Tegne Kapittel 6: Måle og merke Kapittel 7: Organisere Kapittel 8: Komponenter Kapittel 9: Material og tekstur Kapittel 10: Presentasjon Kapittel 11: Avslutning   Varighet: 3 timer og 4 minutter   Om Utdannet.no: Utdannet.no tilbyr noen av landets beste digitale nettkurs. Tjenesten fungerer på samme måte som strømmetjenester for musikk eller TV-serier. Våre kunder betaler en fast månedspris og får tilgang til alle kursene som er produsert så langt. Plattformen har hatt en god vekst de siste årene og kan skilte med 30.000 registrerte brukere og 1,5 millioner videoavspillinger. Vårt mål er å gjøre kompetanseutvikling moro, spennende og tilgjengelig for alle – og med oss har vi Innovasjon Norge og Forskningsrådet. [-]

Mange av prinsippene for stor dokumenter kan med fordel også anvendes på de aller fleste dokumenter, inklusive brev og notater. Her går vi gjennom alt du trenger for å skrive bøker eller avhandlinger og også det du trenger for å skrive invitasjoner til nyttårsfest eller barnebursdag. Kursinnhold Fletting til brev, konvolutter, etiketter og e-post Bruk av stiler Bilder og bildetekster innholdsfortegnelse, stikkordliste og figurliste Spalter, marger, sidefarger, sidekantlinjer og dokumenttemaer. Deldokumenter kan samles i et hoved dokument  Topp- og bunntekster og side nummerering. Utklipp, figurer, SmartArt og diagram. Tekstbokser Tabeller: Formatering, sortering og beregninger. Maler med Felt, innholdskontroller og skjemakontroller Med makroer kan du effektivisere avanserte oppgaver som består av serie med handlinger. Det er fordelaktig å ha to skjermer - en til å følge kurset og en til å gjøre det kursholder demonstrerer. Kurset gjennomføres i sanntid med nettundervisning via Teams. Det blir mulighet for å stille spørsmål, ha diskusjoner, demonstrasjoner og øvelser. Du vil motta en invitasjon til Teams fra kursholder. [-]

Kubernetes Administration (LFS458) [-]

Dette kurset gir deg en grunnleggende innføring i to-dimensjonal Datamaskin Assistert Konstruksjon (DAK). Du får et grunnlag for videre studier, og kompetanse som gjør tegnearbeidet både utfordrende og interessant. Du lærer å bli fortrolig med å bruke denne type hjelpemiddel til tegnearbeid, teknisk tegning og revidering av tegninger.   Studentlisens for AutoCAD og Revit Structure/Architecture er inkludert. Kurset er på norsk, men AutoCAD-programmet er på engelsk. Programvaren er gratis. Du lærer å bruke de grunnleggende kommandoene slik at du kan utføre enklere tegnearbeid. Du blir fortrolig med å bruke denne type hjelpemiddel til tegnearbeid, teknisk tegning og revidering av tegninger. Du lærer å jobbe rasjonelt og å velge enkle løsninger. Bruk av flere lag med ulike farger gir god visualisering og bedre lesing av tegningene. Målsetting og teksting er viktig, og må utføres tydelig og på en riktig måte. Flater fylles med skravur og elementer kan lagres separat for senere bruk i andre tegninger. Kurset gir deg inngående informasjon gjennom studieveiledningen om hvordan du skal bruke de enkelte kommandoene. Det stilles krav til 100 % nøyaktighet, noe du oppnår når du jobber riktig. Du får øvelser med tegneoppgaver innen bygg, elektro, elkraft og maskin.   [-]

Kurs beskrivelse Er det vanskelig å skjønne hvordan Access fungerer? Har du databaser med bare 1 tabell og ikke flere som de skal ha? Får du ikke orden på dine data? Må du skrive data inn i tabellen istedenfor gjennom et skjema? Er det vanskelig å få data ut fra databasen din? Blir databasen din lite brukervennlig? Dette er vanlige problemstillinger mange sliter med og som blir borte etter endt kurs! Kurset passer for deg med liten erfaring og som ønsker å lære Access fra grunnen av. Kurset passer også for deg som er selvlært og som ønsker å jobbe mer effektivt. På kun 2 dager vil du mestre de vanligste arbeidsoppgavene i Access. Du lærer gode rutiner du trenger for å kunne arbeide raskt og effektivt. Du vil kunne lage og strukturere alt fra enkle til mer avanserte databaser og vil føle deg trygg på at det er du som kontrollerer Access og ikke omvendt! Du lærer også hvordan du skal få data ut fra databasen din gjennom spørringer og rapporter. I tillegg får du en rekke tips og triks du kan bruke i din arbeidsdag.  Alt du lærer får du repetert gjennom aktiv oppgaveløsning slik at du husker det du har lært når du kommer tilbake på jobb. Kursdokumentasjon, lunsj og pausemat er selvsagt inkludert! Kursholderne har mer enn 20 års Access erfaring som de gjerne deler med deg! Meld deg på Access-kurs allerede i dag og sikre deg plass!   Kursinnhold Grunnleggende begreper Introduksjon til databasedesign/datamodellering Planlegging av en ny databaseOrganisering av data i en database Opprette en database   Tabeller Tabellens utformingsvisningTabellens data arkvisning Spørringer/Queries UtvalgsspørringerHandlingsspørringer/ActionqueriesLag tabell-spørringSlettespørring-Føy til-spørringOppdateringsspørringParameterspørringer Skjemaer Opprette skjemaer med skjemaveivisereSkjemavisningUformingsvisning for skjemaerGjennomgang av designverktøyVerktøyboksenFargepalett, fonter, tekstplasseringBundne, ubundne og kalkulerte kontrollerPostkilde og feltliste for et skjemaEn-til-mange-skjema Rapporter Opprette rapporter med RapportveiviserenUtformingsvisning for en rapportGruppering og sortering i rapporter Etiketter Spesialtilpassede etikettmalerEtikettveiviseren   [-]

Studieår: 2013-2014   Gjennomføring: Høst og vår Antall studiepoeng: 5.0 Forutsetninger: Grunnleggende kunnskaper i HTML, CSS tilsvarende emnet IINI1002 Webutvikling 1. Kunnskaper om grunnleggende programmering og helst litt Javascript er en fordel. Innleveringer: Større eller mindre øvinger tilsvarende 8 øvinger, hvor 6 må være godkjent før endelig karakter settes. Personlig veileder: ja Vurderingsform: Prosjektoppgave som vurderes til bestått/ikke bestått. Karakteren i faget settes på grunnlag av en individuell 4-timers nettbasert hjemmeeksamen. Klageadgang i dette faget gjelder hver enkelt vurderingsdel. Ansvarlig: Atle Nes Eksamensdato: 09.12.13 / 12.05.14         Læremål: Etter å ha gjennomført emnet skal studenten ha følgende samlede læringsutbytte: KUNNSKAPER:Kandidaten:- forstår problemstillinger knyttet til bruk av ikke-standardisert teknologi- har kjennskap til nyttige rammeverk for HTML5 og fallback-løsninger- har kjennskap til problemstillinger knyttet til bruk av ulike medieformater FERDIGHETER:Kandidaten:- kan ta i bruk nye semantiske elementer fra HTML5- kan ta i bruk ny funksjonalitet fra CSS3 og JavaScript på nettstedet- kan ta i bruk nye skjemaelementer og -attributter fra HTML5- kan tegne på et canvas-element med JavaScript- kan legge til multimedia ved hjelp av video- og audio-elementet- kan lage nettsider som tilpasser seg mobile enheter og utnytter egenskaper hos disse- kan bruke lokal lagring til å lagre og hente fram data- kan bruke XMLHttpRequest2 til kommunikasjon med webtjeneren- kan lage en større HTML5-basert webløsning GENERELL KOMPETANSE:Kandidaten:- får et overblikk over ny webteknologi som er i ferd med å bli standardisert Innhold:Introduksjon til HTML5, grunnleggende syntaks og struktur, nye semantiske elementer, dynamiske websider med JavaScript og CSS3, nye skjemaelementer (forms), HTML5 canvas til grafikk og tegning, HTML5 video og audio, mobile enheter og device access, lokal lagring av applikasjoner og data, dataoverføring med Web SocketsLes mer om faget her Påmeldingsfrist: 25.08.13 / 25.01.14         Velg semester:  Høst 2013    Vår 2014     Fag HTML5 4980,-         Semesteravgift og eksamenskostnader kommer i tillegg.    [-]

SIAM® Foundation [-]

UTDANNINGSMÅLKursdeltakerne skal etter endt kurs kunne bruke AutoCAD til å: Opprette nye tegninger Gjøre forandringer i eksisterende tegninger Bruke og forstå de vanligste tegne- og editeringskommandoer Målsette og påføre tekst i en tegning Bruke og forstå lagoppbyggingen i AutoCAD Symbol- og blokkhåndtering Enkel layout/plotting Lagre tegninger KURSINNHOLD: Introduksjon av AutoCAD 2-dimensjonal tegning Zoom og skjermbehandlingsteknikk Redigeringsfunksjoner Målsettingsfunksjoner Laghåndtering Symbol- og blokkhåndtering Hente symboler og parametere (målsettings- og tekststiler, osv.) fra eksisterende tegninger Layout/plotting av tegninger Lagrings- og avslutningsrutiner [-]

Power BI er et fleksibelt verktøy som dekker hele prosessen fra datafangst til deling av rapporter. Selv om det virker lett på overflaten har Power BI funksjoner som åpner uendelige muligheter. Det er flere triks som skaper en bedre brukeropplevelse av Power BI samtidig som at man bruker Power BI til å gjennomføre avanserte kalkulasjoner.   Dette kurset bygger på filmdata som deltakerne hentes direkte fra IMDB via funksjoner i Power Query, og brukes til å lage en lekker rapport i Power BI. Kurset bygger ikke direkte på «Selvbetjening og analyse med Power BI», men bygger videre på flere av teknikkene som ble gjennomgått her, og er myntet på de som allerede har litt erfaring med bruk av Power BI. Deltakerne vil få en grunnleggende innføring i:  Henter og renser ekte data fra IMDB via Power Query. Power Query funksjoner Avansert bruk av bokmerker Et dypdykk i design Dynamiske overskrifter med DAX Variabelsyntaks i DAX Filter- og radkontekst  Dynamisk link til nettsider med DAX Egendefinerte verktøytips Betinget formatering Målgruppe: Kurset er for folk som har jobbet med Power BI i minst et par måneder. Det er en fordel hvis man har gjennomført «Selvbetjening og analyse med Power BI», men det er ikke et krav. Ta med egen PC med nyeste versjon av Power BI. Du bør også ha signet opp med en 30-dagers gratislisens for Power BI Pro.    Kursinnhold Henter og renser ekte data fra IMDB via Power Query Vi henter data direkte fra nettsiden til IMDB (scraping) og transformerer dette til et format som er egnet i Power BI. Power Query funksjoner Vi bruker eksempeldata til å trene Power BI til å gjenkjenne mønstre i nettsiden slik at logikken kan transformeres til en funksjon og brukes på flere filmer. Avansert bruk av bokmerker Bokmerker er en funksjon som hjelper deg med å lage interaktive rapporter. I dette kurset bruker vi denne funksjonen til å lage et dynamisk filterpanel. Et dypdykk i design Design er med på å skape et helhetlig inntrykk av rapporten, og bidrar til at rapporten blir mer brukervennlig. Kurset gjennomgår generelle designtriks sammen med teori rundt universal utforming. Dynamiske overskrifter med DAX Du kan enkelt lage statiske overskrifter i Power BI, men med overskrifter som endrer seg etter innholdet, tar du rapporten din til et nytt nivå. Variabelsyntaks i DAX Variabelsyntaksen i DAX skaper en leservennlig struktur i koden samtidig som det kan forbedre ytelsen. Kurset gjennomgår hvordan man bruker variabelsyntaksen og fordelene med det.  Filter- og radkontekst Ord som filterkontekst og radkontekst brukes mye når man snakker om Power BI, men det er ikke alltid tydelig hva det betyr. Kurset gjennomgår forskjellene og illustrerer dette med å tvinge radkontekst i et mål via AVERAGEX funksjonen. Dynamisk link til nettsider med DAX Power BI kan linke til nettsider eller andre Power BI rapporter. I dette kurset lager vi en link som fører direkte til nettsiden for valgt film.  Egendefinerte verktøytips Vektøystips er viser ekstra informasjon når brukeren holder over et element i Power BI. Det finnes en standard visning hvor man kan gjøre et par justeringer, men i dette kurset lager vi et helt eget verktøystips slik at vi har full kontroll over størrelse og innhold. Betinget formatering Betinget formatering lar deg endre formateringen basert på en betinget verdi. I dette kurset lager vi datastolper og fargeendringer via både brukergrensesnittet og DAX [-]

COURSE OVERVIEW Students will begin by understanding the core compute and storage technologies that are used to build an analytical solution. They will then explore how to design an analytical serving layers and focus on data engineering considerations for working with source files. The students will learn how to interactively explore data stored in files in a data lake. They will learn the various ingestion techniques that can be used to load data using the Apache Spark capability found in Azure Synapse Analytics or Azure Databricks, or how to ingest using Azure Data Factory or Azure Synapse pipelines. The students will also learn the various ways they can transform the data using the same technologies that is used to ingest data. The student will spend time on the course learning how to monitor and analyze the performance of analytical system so that they can optimize the performance of data loads, or queries that are issued against the systems. They will understand the importance of implementing security to ensure that the data is protected at rest or in transit. The student will then show how the data in an analytical system can be used to create dashboards, or build predictive models in Azure Synapse Analytics. TARGET AUDIENCE The primary audience for this course is data professionals, data architects, and business intelligence professionals who want to learn about data engineering and building analytical solutions using data platform technologies that exist on Microsoft Azure. The secondary audience for this course data analysts and data scientists who work with analytical solutions built on Microsoft Azure. COURSE OBJECTIVES   Explore compute and storage options for data engineering workloads in Azure Design and Implement the serving layer Understand data engineering considerations Run interactive queries using serverless SQL pools Explore, transform, and load data into the Data Warehouse using Apache Spark Perform data Exploration and Transformation in Azure Databricks Ingest and load Data into the Data Warehouse Transform Data with Azure Data Factory or Azure Synapse Pipelines Integrate Data from Notebooks with Azure Data Factory or Azure Synapse Pipelines Optimize Query Performance with Dedicated SQL Pools in Azure Synapse Analyze and Optimize Data Warehouse Storage Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link Perform end-to-end security with Azure Synapse Analytics Perform real-time Stream Processing with Stream Analytics Create a Stream Processing Solution with Event Hubs and Azure Databricks Build reports using Power BI integration with Azure Synpase Analytics Perform Integrated Machine Learning Processes in Azure Synapse Analytics COURSE CONTENT Module 1: Explore compute and storage options for data engineering workloads This module provides an overview of the Azure compute and storage technology options that are available to data engineers building analytical workloads. This module teaches ways to structure the data lake, and to optimize the files for exploration, streaming, and batch workloads. The student will learn how to organize the data lake into levels of data refinement as they transform files through batch and stream processing. Then they will learn how to create indexes on their datasets, such as CSV, JSON, and Parquet files, and use them for potential query and workload acceleration. Introduction to Azure Synapse Analytics Describe Azure Databricks Introduction to Azure Data Lake storage Describe Delta Lake architecture Work with data streams by using Azure Stream Analytics Lab 1: Explore compute and storage options for data engineering workloads Combine streaming and batch processing with a single pipeline Organize the data lake into levels of file transformation Index data lake storage for query and workload acceleration After completing module 1, students will be able to: Describe Azure Synapse Analytics Describe Azure Databricks Describe Azure Data Lake storage Describe Delta Lake architecture Describe Azure Stream Analytics Module 2: Design and implement the serving layer This module teaches how to design and implement data stores in a modern data warehouse to optimize analytical workloads. The student will learn how to design a multidimensional schema to store fact and dimension data. Then the student will learn how to populate slowly changing dimensions through incremental data loading from Azure Data Factory. Design a multidimensional schema to optimize analytical workloads Code-free transformation at scale with Azure Data Factory Populate slowly changing dimensions in Azure Synapse Analytics pipelines Lab 2: Designing and Implementing the Serving Layer Design a star schema for analytical workloads Populate slowly changing dimensions with Azure Data Factory and mapping data flows After completing module 2, students will be able to: Design a star schema for analytical workloads Populate a slowly changing dimensions with Azure Data Factory and mapping data flows Module 3: Data engineering considerations for source files This module explores data engineering considerations that are common when loading data into a modern data warehouse analytical from files stored in an Azure Data Lake, and understanding the security consideration associated with storing files stored in the data lake. Design a Modern Data Warehouse using Azure Synapse Analytics Secure a data warehouse in Azure Synapse Analytics Lab 3: Data engineering considerations Managing files in an Azure data lake Securing files stored in an Azure data lake After completing module 3, students will be able to: Design a Modern Data Warehouse using Azure Synapse Analytics Secure a data warehouse in Azure Synapse Analytics Module 4: Run interactive queries using Azure Synapse Analytics serverless SQL pools In this module, students will learn how to work with files stored in the data lake and external file sources, through T-SQL statements executed by a serverless SQL pool in Azure Synapse Analytics. Students will query Parquet files stored in a data lake, as well as CSV files stored in an external data store. Next, they will create Azure Active Directory security groups and enforce access to files in the data lake through Role-Based Access Control (RBAC) and Access Control Lists (ACLs). Explore Azure Synapse serverless SQL pools capabilities Query data in the lake using Azure Synapse serverless SQL pools Create metadata objects in Azure Synapse serverless SQL pools Secure data and manage users in Azure Synapse serverless SQL pools Lab 4: Run interactive queries using serverless SQL pools Query Parquet data with serverless SQL pools Create external tables for Parquet and CSV files Create views with serverless SQL pools Secure access to data in a data lake when using serverless SQL pools Configure data lake security using Role-Based Access Control (RBAC) and Access Control List After completing module 4, students will be able to: Understand Azure Synapse serverless SQL pools capabilities Query data in the lake using Azure Synapse serverless SQL pools Create metadata objects in Azure Synapse serverless SQL pools Secure data and manage users in Azure Synapse serverless SQL pools Module 5: Explore, transform, and load data into the Data Warehouse using Apache Spark This module teaches how to explore data stored in a data lake, transform the data, and load data into a relational data store. The student will explore Parquet and JSON files and use techniques to query and transform JSON files with hierarchical structures. Then the student will use Apache Spark to load data into the data warehouse and join Parquet data in the data lake with data in the dedicated SQL pool. Understand big data engineering with Apache Spark in Azure Synapse Analytics Ingest data with Apache Spark notebooks in Azure Synapse Analytics Transform data with DataFrames in Apache Spark Pools in Azure Synapse Analytics Integrate SQL and Apache Spark pools in Azure Synapse Analytics Lab 5: Explore, transform, and load data into the Data Warehouse using Apache Spark Perform Data Exploration in Synapse Studio Ingest data with Spark notebooks in Azure Synapse Analytics Transform data with DataFrames in Spark pools in Azure Synapse Analytics Integrate SQL and Spark pools in Azure Synapse Analytics After completing module 5, students will be able to: Describe big data engineering with Apache Spark in Azure Synapse Analytics Ingest data with Apache Spark notebooks in Azure Synapse Analytics Transform data with DataFrames in Apache Spark Pools in Azure Synapse Analytics Integrate SQL and Apache Spark pools in Azure Synapse Analytics Module 6: Data exploration and transformation in Azure Databricks This module teaches how to use various Apache Spark DataFrame methods to explore and transform data in Azure Databricks. The student will learn how to perform standard DataFrame methods to explore and transform data. They will also learn how to perform more advanced tasks, such as removing duplicate data, manipulate date/time values, rename columns, and aggregate data. Describe Azure Databricks Read and write data in Azure Databricks Work with DataFrames in Azure Databricks Work with DataFrames advanced methods in Azure Databricks Lab 6: Data Exploration and Transformation in Azure Databricks Use DataFrames in Azure Databricks to explore and filter data Cache a DataFrame for faster subsequent queries Remove duplicate data Manipulate date/time values Remove and rename DataFrame columns Aggregate data stored in a DataFrame After completing module 6, students will be able to: Describe Azure Databricks Read and write data in Azure Databricks Work with DataFrames in Azure Databricks Work with DataFrames advanced methods in Azure Databricks Module 7: Ingest and load data into the data warehouse This module teaches students how to ingest data into the data warehouse through T-SQL scripts and Synapse Analytics integration pipelines. The student will learn how to load data into Synapse dedicated SQL pools with PolyBase and COPY using T-SQL. The student will also learn how to use workload management along with a Copy activity in a Azure Synapse pipeline for petabyte-scale data ingestion. Use data loading best practices in Azure Synapse Analytics Petabyte-scale ingestion with Azure Data Factory Lab 7: Ingest and load Data into the Data Warehouse Perform petabyte-scale ingestion with Azure Synapse Pipelines Import data with PolyBase and COPY using T-SQL Use data loading best practices in Azure Synapse Analytics After completing module 7, students will be able to: Use data loading best practices in Azure Synapse Analytics Petabyte-scale ingestion with Azure Data Factory Module 8: Transform data with Azure Data Factory or Azure Synapse Pipelines This module teaches students how to build data integration pipelines to ingest from multiple data sources, transform data using mapping data flowss, and perform data movement into one or more data sinks. Data integration with Azure Data Factory or Azure Synapse Pipelines Code-free transformation at scale with Azure Data Factory or Azure Synapse Pipelines Lab 8: Transform Data with Azure Data Factory or Azure Synapse Pipelines Execute code-free transformations at scale with Azure Synapse Pipelines Create data pipeline to import poorly formatted CSV files Create Mapping Data Flows After completing module 8, students will be able to: Perform data integration with Azure Data Factory Perform code-free transformation at scale with Azure Data Factory Module 9: Orchestrate data movement and transformation in Azure Synapse Pipelines In this module, you will learn how to create linked services, and orchestrate data movement and transformation using notebooks in Azure Synapse Pipelines. Orchestrate data movement and transformation in Azure Data Factory Lab 9: Orchestrate data movement and transformation in Azure Synapse Pipelines Integrate Data from Notebooks with Azure Data Factory or Azure Synapse Pipelines After completing module 9, students will be able to: Orchestrate data movement and transformation in Azure Synapse Pipelines Module 10: Optimize query performance with dedicated SQL pools in Azure Synapse In this module, students will learn strategies to optimize data storage and processing when using dedicated SQL pools in Azure Synapse Analytics. The student will know how to use developer features, such as windowing and HyperLogLog functions, use data loading best practices, and optimize and improve query performance. Optimize data warehouse query performance in Azure Synapse Analytics Understand data warehouse developer features of Azure Synapse Analytics Lab 10: Optimize Query Performance with Dedicated SQL Pools in Azure Synapse Understand developer features of Azure Synapse Analytics Optimize data warehouse query performance in Azure Synapse Analytics Improve query performance After completing module 10, students will be able to: Optimize data warehouse query performance in Azure Synapse Analytics Understand data warehouse developer features of Azure Synapse Analytics Module 11: Analyze and Optimize Data Warehouse Storage In this module, students will learn how to analyze then optimize the data storage of the Azure Synapse dedicated SQL pools. The student will know techniques to understand table space usage and column store storage details. Next the student will know how to compare storage requirements between identical tables that use different data types. Finally, the student will observe the impact materialized views have when executed in place of complex queries and learn how to avoid extensive logging by optimizing delete operations. Analyze and optimize data warehouse storage in Azure Synapse Analytics Lab 11: Analyze and Optimize Data Warehouse Storage Check for skewed data and space usage Understand column store storage details Study the impact of materialized views Explore rules for minimally logged operations After completing module 11, students will be able to: Analyze and optimize data warehouse storage in Azure Synapse Analytics Module 12: Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link In this module, students will learn how Azure Synapse Link enables seamless connectivity of an Azure Cosmos DB account to a Synapse workspace. The student will understand how to enable and configure Synapse link, then how to query the Azure Cosmos DB analytical store using Apache Spark and SQL serverless. Design hybrid transactional and analytical processing using Azure Synapse Analytics Configure Azure Synapse Link with Azure Cosmos DB Query Azure Cosmos DB with Apache Spark pools Query Azure Cosmos DB with serverless SQL pools Lab 12: Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link Configure Azure Synapse Link with Azure Cosmos DB Query Azure Cosmos DB with Apache Spark for Synapse Analytics Query Azure Cosmos DB with serverless SQL pool for Azure Synapse Analytics After completing module 12, students will be able to: Design hybrid transactional and analytical processing using Azure Synapse Analytics Configure Azure Synapse Link with Azure Cosmos DB Query Azure Cosmos DB with Apache Spark for Azure Synapse Analytics Query Azure Cosmos DB with SQL serverless for Azure Synapse Analytics Module 13: End-to-end security with Azure Synapse Analytics In this module, students will learn how to secure a Synapse Analytics workspace and its supporting infrastructure. The student will observe the SQL Active Directory Admin, manage IP firewall rules, manage secrets with Azure Key Vault and access those secrets through a Key Vault linked service and pipeline activities. The student will understand how to implement column-level security, row-level security, and dynamic data masking when using dedicated SQL pools. Secure a data warehouse in Azure Synapse Analytics Configure and manage secrets in Azure Key Vault Implement compliance controls for sensitive data Lab 13: End-to-end security with Azure Synapse Analytics Secure Azure Synapse Analytics supporting infrastructure Secure the Azure Synapse Analytics workspace and managed services Secure Azure Synapse Analytics workspace data After completing module 13, students will be able to: Secure a data warehouse in Azure Synapse Analytics Configure and manage secrets in Azure Key Vault Implement compliance controls for sensitive data Module 14: Real-time Stream Processing with Stream Analytics In this module, students will learn how to process streaming data with Azure Stream Analytics. The student will ingest vehicle telemetry data into Event Hubs, then process that data in real time, using various windowing functions in Azure Stream Analytics. They will output the data to Azure Synapse Analytics. Finally, the student will learn how to scale the Stream Analytics job to increase throughput. Enable reliable messaging for Big Data applications using Azure Event Hubs Work with data streams by using Azure Stream Analytics Ingest data streams with Azure Stream Analytics Lab 14: Real-time Stream Processing with Stream Analytics Use Stream Analytics to process real-time data from Event Hubs Use Stream Analytics windowing functions to build aggregates and output to Synapse Analytics Scale the Azure Stream Analytics job to increase throughput through partitioning Repartition the stream input to optimize parallelization After completing module 14, students will be able to: Enable reliable messaging for Big Data applications using Azure Event Hubs Work with data streams by using Azure Stream Analytics Ingest data streams with Azure Stream Analytics Module 15: Create a Stream Processing Solution with Event Hubs and Azure Databricks In this module, students will learn how to ingest and process streaming data at scale with Event Hubs and Spark Structured Streaming in Azure Databricks. The student will learn the key features and uses of Structured Streaming. The student will implement sliding windows to aggregate over chunks of data and apply watermarking to remove stale data. Finally, the student will connect to Event Hubs to read and write streams. Process streaming data with Azure Databricks structured streaming Lab 15: Create a Stream Processing Solution with Event Hubs and Azure Databricks Explore key features and uses of Structured Streaming Stream data from a file and write it out to a distributed file system Use sliding windows to aggregate over chunks of data rather than all data Apply watermarking to remove stale data Connect to Event Hubs read and write streams After completing module 15, students will be able to: Process streaming data with Azure Databricks structured streaming Module 16: Build reports using Power BI integration with Azure Synpase Analytics In this module, the student will learn how to integrate Power BI with their Synapse workspace to build reports in Power BI. The student will create a new data source and Power BI report in Synapse Studio. Then the student will learn how to improve query performance with materialized views and result-set caching. Finally, the student will explore the data lake with serverless SQL pools and create visualizations against that data in Power BI. Create reports with Power BI using its integration with Azure Synapse Analytics Lab 16: Build reports using Power BI integration with Azure Synpase Analytics Integrate an Azure Synapse workspace and Power BI Optimize integration with Power BI Improve query performance with materialized views and result-set caching Visualize data with SQL serverless and create a Power BI report After completing module 16, students will be able to: Create reports with Power BI using its integration with Azure Synapse Analytics Module 17: Perform Integrated Machine Learning Processes in Azure Synapse Analytics This module explores the integrated, end-to-end Azure Machine Learning and Azure Cognitive Services experience in Azure Synapse Analytics. You will learn how to connect an Azure Synapse Analytics workspace to an Azure Machine Learning workspace using a Linked Service and then trigger an Automated ML experiment that uses data from a Spark table. You will also learn how to use trained models from Azure Machine Learning or Azure Cognitive Services to enrich data in a SQL pool table and then serve prediction results using Power BI. Use the integrated machine learning process in Azure Synapse Analytics Lab 17: Perform Integrated Machine Learning Processes in Azure Synapse Analytics Create an Azure Machine Learning linked service Trigger an Auto ML experiment using data from a Spark table Enrich data using trained models Serve prediction results using Power BI After completing module 17, students will be able to: Use the integrated machine learning process in Azure Synapse Analytics     [-]

Filer i SharePoint lagres i bibliotek. Her tar vi en grundig gjennomgang av bibliotek og tilpasningsmuligheter for disse, som versjonering, maler og Office-integrasjon. Webinaret varer i 2 timer og består av to økter à 45 min. Etter hver økt er det 10 min spørsmålsrunde. Mellom øktene er det 10 min pause. Webinaret kan også spesialtilpasses og holdes bedriftsinternt kun for din bedrift.   Kursinnhold:   Om bibliotek Møt biblioteksmalene i SharePoint Opplasting, nedlasting Office-programmene og bibliotek Områdepapirkurv   Tilpasse bibliotek Endre Office-mal for et bibliotek Tilpass kolonner og metadata   Tips til bibliotek Bruke kolonner i Word Bibliotek i Windows Utforsker   Utvidet om bibliotek Gjennomgang av versjonering Bli kjent med godkjenning Arkivering og Send til   Veien videre Introduksjon til innholdstyper Introduksjon til dokumentsenter og innholds-sortering 3 gode grunner til å delta 1. Møt SharePoint sine bibliotek-apper og lær måter å åpne og lagre i bibliotek og håndtere innholdet 2. Forstå mer om versjonering, godkjenning og arkivering 3. Bli kjent med dokumentsenter og innholds-sortering   [-]

COURSE OVERVIEW The course teaches IT Professionals how to implement and manage on-premises and hybrid solutions such as identity, management, compute, networking, and storage in a Windows Server hybrid environment. TARGET AUDIENCE This four-day course is intended for Windows Server Hybrid Administrators who have experience working with Windows Server and want to extend the capabilities of their on-premises environments by combining on-premises and hybrid technologies. Windows Server Hybrid Administrators implement and manage on-premises and hybrid solutions such as identity, management, compute, networking, and storage in a Windows Server hybrid environment. COURSE OBJECTIVES After you complete this course you will be able to: Use administrative techniques and tools in Windows Server. Identify tools used to implement hybrid solutions, including Windows Admin Center and PowerShell. Implement identity services in Windows Server. Implement identity in hybrid scenarios, including Azure AD DS on Azure IaaS and managed AD DS. Integrate Azure AD DS with Azure AD. Manage network infrastructure services. Deploy Azure VMs running Windows Server, and configure networking and storage. Administer and manage Windows Server IaaS Virtual Machine remotely. Manage and maintain Azure VMs running Windows Server. Configure file servers and storage. Implement File Services in hybrid scenarios, using Azure Files and Azure File Sync. COURSE CONTENT Module 1: Identity services in Windows Server This module introduces identity services and describes Active Directory Domain Services (AD DS) in a Windows Server environment. The module describes how to deploy domain controllers in AD DS, as well as Azure Active Directory (AD) and the benefits of integrating Azure AD with AD DS. The module also covers Group Policy basics and how to configure group policy objects (GPOs) in a domain environment. Lessons for module 1 Introduction to AD DS Manage AD DS domain controllers and FSMO roles Implement Group Policy Objects Manage advanced features of AD DS Lab : Implementing identity services and Group Policy Deploying a new domain controller on Server Core Configuring Group Policy After completing module 1, students will be able to: Describe AD DS in a Windows Server environment. Deploy domain controllers in AD DS. Describe Azure AD and benefits of integrating Azure AD with AD DS. Explain Group Policy basics and configure GPOs in a domain environment. Module 2: Implementing identity in hybrid scenarios This module discusses how to configure an Azure environment so that Windows IaaS workloads requiring Active Directory are supported. The module also covers integration of on-premises Active Directory Domain Services (AD DS) environment into Azure. Finally, the module explains how to extend an existing Active Directory environment into Azure by placing IaaS VMs configured as domain controllers onto a specially configured Azure virtual network (VNet) subnet. Lessons for module 2 Implement hybrid identity with Windows Server Deploy and manage Azure IaaS Active Directory domain controllers in Azure Lab : Implementing integration between AD DS and Azure AD Preparing Azure AD for AD DS integration Preparing on-premises AD DS for Azure AD integration Downloading, installing, and configuring Azure AD Connect Verifying integration between AD DS and Azure AD Implementing Azure AD integration features in AD DS After completing module 2, students will be able to: Integrate on-premises Active Directory Domain Services (AD DS) environment into Azure. Install and configure directory synchronization using Azure AD Connect. Implement and configure Azure AD DS. Implement Seamless Single Sign-on (SSO). Implement and configure Azure AD DS. Install a new AD DS forest on an Azure VNet. Module 3: Windows Server administration This module describes how to implement the principle of least privilege through Privileged Access Workstation (PAW) and Just Enough Administration (JEA). The module also highlights several common Windows Server administration tools, such as Windows Admin Center, Server Manager, and PowerShell. This module also describes the post-installation confguration process and tools available to use for this process, such as sconfig and Desired State Configuration (DSC). Lessons for module 3 Perform Windows Server secure administration Describe Windows Server administration tools Perform post-installation configuration of Windows Server Just Enough Administration in Windows Server Lab : Managing Windows Server Implementing and using remote server administration After completing module 3, students will be able to: Explain least privilege administrative models. Decide when to use privileged access workstations. Select the most appropriate Windows Server administration tool for a given situation. Apply different methods to perform post-installation configuration of Windows Server. Constrain privileged administrative operations by using Just Enough Administration (JEA). Module 4: Facilitating hybrid management This module covers tools that facilitate managing Windows IaaS VMs remotely. The module also covers how to use Azure Arc with on-premises server instances, how to deploy Azure policies with Azure Arc, and how to use role-based access control (RBAC) to restrict access to Log Analytics data. Lessons for module 4 Administer and manage Windows Server IaaS virtual machines remotely Manage hybrid workloads with Azure Arc Lab : Using Windows Admin Center in hybrid scenarios Provisioning Azure VMs running Windows Server Implementing hybrid connectivity by using the Azure Network Adapter Deploying Windows Admin Center gateway in Azure Verifying functionality of the Windows Admin Center gateway in Azure After completing module 4, students will be able to: Select appropriate tools and techniques to manage Windows IaaS VMs remotely. Explain how to onboard on-premises Windows Server instances in Azure Arc. Connect hybrid machines to Azure from the Azure portal. Use Azure Arc to manage devices. Restrict access using RBAC. Module 5: Hyper-V virtualization in Windows Server This modules describes how to implement and configure Hyper-V VMs and containers. The module covers key features of Hyper-V in Windows Server, describes VM settings, and how to configure VMs in Hyper-V. The module also covers security technologies used with virtualization, such as shielded VMs, Host Guardian Service, admin-trusted and TPM-trusted attestation, and Key Protection Service (KPS). Finally, this module covers how to run containers and container workloads, and how to orchestrate container workloads on Windows Server using Kubernetes. Lessons for module 5 Configure and manage Hyper-V Configure and manage Hyper-V virtual machines Secure Hyper-V workloads Run containers on Windows Server Orchestrate containers on Windows Server using Kubernetes Lab : Implementing and configuring virtualization in Windows Server Creating and configuring VMs Installing and configuring containers After completing module 5, students will be able to: Install and configure Hyper-V on Windows Server. Configure and manage Hyper-V virtual machines. Use Host Guardian Service to protect virtual machines. Create and deploy shielded virtual machines. Configure and manage container workloads. Orchestrate container workloads using a Kubernetes cluster. Module 6: Deploying and configuring Azure VMs This module describes Azure compute and storage in relation to Azure VMs, and how to deploy Azure VMs by using the Azure portal, Azure CLI, or templates. The module also explains how to create new VMs from generalized images and use Azure Image Builder templates to create and manage images in Azure. Finally, this module describes how to deploy Desired State Configuration (DSC) extensions, implement those extensions to remediate noncompliant servers, and use custom script extensions. Lessons for module 6 Plan and deploy Windows Server IaaS virtual machines Customize Windows Server IaaS virtual machine images Automate the configuration of Windows Server IaaS virtual machines Lab : Deploying and configuring Windows Server on Azure VMs Authoring Azure Resource Manager (ARM) templates for Azure VM deployment Modifying ARM templates to include VM extension-based configuration Deploying Azure VMs running Windows Server by using ARM templates Configuring administrative access to Azure VMs running Windows Server Configuring Windows Server security in Azure VMs After completing module 6, students will be able to: Create a VM from the Azure portal and from Azure Cloud Shell. Deploy Azure VMs by using templates. Automate the configuration of Windows Server IaaS VMs. Detect and remediate noncompliant servers. Create new VMs from generalized images. Use Azure Image Builder templates to create and manage images in Azure. Module 7: Network infrastructure services in Windows Server This module describes how to implement core network infrastructure services in Windows Server, such as DHCP and DNS. This module also covers how to implement IP address managment and how to use Remote Access Services. Lessons for module 7 Deploy and manage DHCP Implement Windows Server DNS Implement IP address management Implement remote access Lab : Implementing and configuring network infrastructure services in Windows Server Deploying and configuring DHCP Deploying and configuring DNS After completing module 7, students will be able to: Implement automatic IP configuration with DHCP in Windows Server. Deploy and configure name resolution with Windows Server DNS. Implement IPAM to manage an organization’s DHCP and DNS servers, and IP address space. Select, use, and manage remote access components. Implement Web Application Proxy (WAP) as a reverse proxy for internal web applications. Module 8: Implementing hybrid networking infrastructure This module describes how to connect an on-premises environment to Azure and how to configure DNS for Windows Server IaaS virtual machines. The module covers how to choose the appropriate DNS solution for your organization’s needs, and run a DNS server in a Windows Server Azure IaaS VM. Finally, this module covers how to manage manage Microsoft Azure virtual networks (VNets) and IP address configuration for Windows Server infrastructure as a service (IaaS) virtual machines. Lessons for module 8 Implement hybrid network infrastructure Implement DNS for Windows Server IaaS VMs Implement Windows Server IaaS VM IP addressing and routing Lab : Implementing Windows Server IaaS VM networking Implementing virtual network routing in Azure Implementing DNS name resolution in Azure After completing module 8, students will be able to: Implement an Azure virtual private network (VPN). Configure DNS for Windows Server IaaS VMs. Run a DNS server in a Windows Server Azure IaaS VM. Create a route-based VPN gateway using the Azure portal. Implement Azure ExpressRoute. Implement an Azure wide area network (WAN). Manage Microsoft Azure virtual networks (VNets). Manage IP address configuration for Windows Server IaaS virtual machines (VMs). Module 9: File servers and storage management in Windows Server This module covers the core functionality and use cases of file server and storage management technologies in Windows Server. The module discusses how to configure and manage the Windows File Server role, and how to use Storage Spaces and Storage Spaces Direct. This module also covers replication of volumes between servers or clusters using Storage Replica. Lessons for module 9 Manage Windows Server file servers Implement Storage Spaces and Storage Spaces Direct Implement Windows Server Data Deduplication Implement Windows Server iSCSI Implement Windows Server Storage Replica Lab : Implementing storage solutions in Windows Server Implementing Data Deduplication Configuring iSCSI storage Configuring redundant Storage Spaces Implementing Storage Spaces Direct After completing module 9, students will be able to: Configure and manage the Windows Server File Server role. Protect data from drive failures using Storage Spaces. Increase scalability and performance of storage management using Storage Spaces Direct. Optimize disk utilization using Data DeDuplication. Configure high availability for iSCSI. Enable replication of volumes between clusters using Storage Replica. Use Storage Replica to provide resiliency for data hosted on Windows Servers volumes. Module 10: Implementing a hybrid file server infrastructure This module introduces Azure file services and how to configure connectivity to Azure Files. The module also covers how to deploy and implement Azure File Sync to cache Azure file shares on an on-premises Windows Server file server. This module also describes how to manage cloud tiering and how to migrate from DFSR to Azure File Sync. Lessons for module 10 Overview of Azure file services Implementing Azure File Sync Lab : Implementing Azure File Sync Implementing DFS Replication in your on-premises environment Creating and configuring a sync group Replacing DFS Replication with File Sync–based replication Verifying replication and enabling cloud tiering Troubleshooting replication issues After completing module 10, students will be able to: Configure Azure file services. Configure connectivity to Azure file services. Implement Azure File Sync. Deploy Azure File Sync Manage cloud tiering. Migrate from DFSR to Azure File Sync.   [-]

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 [-]