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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 [-]
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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 [-]
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5 000 kr
5G Security [+]
5G Security [-]
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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. [-]
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Virtuelt klasserom 3 timer
Skriv kursbeskrivelse her [+]
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Nettstudie 1 semester 4 980 kr
På forespørsel
Utviklingsprosesser. Modellering. UML. Verktøy. Objektorientert analyse Objektorientert design. Bruk av arkitektoniske stiler og design mønstre. Implementasjon og test. [+]
Studieår: 2013-2014   Gjennomføring: Høst Antall studiepoeng: 5.0 Forutsetninger: Erfaring fra et objektorientert programmeringsspråk, kjennskap til prosjektarbeid Innleveringer: Innleverte øvinger. Det blir gitt 10 øvinger, 8 må være godkjent for å kunne gå opp til eksamen. Personlig veileder: ja Vurderingsform: 4 timer skriftlig eksamen. Ansvarlig: Tore Berg Hansen Eksamensdato: 12.12.13         Læremål: Forventet læringsutbytte:Etter å ha gjennomført emnet Objektorientert systemutvikling skal studenten ha følgende samlete læringsutbytte: KUNNSKAPER:Kandidaten:- kan definere, gjenkjenne og forklare de grunnleggende konsepter for utvikling av store programvaresystemer basert på det objektorienterte paradigme- argumentere for betydningen av å følge en prosessmodell- argumentere for fordelene med smidige prosesser- argumentere for modellbasert utvikling- beskrive modellene som brukes i objektorientert systemutvikling og hvordan de henger sammen- forklare begrepene arkitektoniske stiler og designmønstre FERDIGHETER:Kandidaten:- kan demonstrere den systematiske gangen fra krav, via arkitektonisk og detaljert design, til ferdig kodet og implementert system GENERELL KOMPETANSE:Kandidaten:- er klar over at utvikling av store programvaresystemer er ingeniørarbeid- er seg bevisst at utvikling av komplekse programvaresystemer krever koordinert innsats av et velfungerende team som følger en definert, smidig prosess- er opptatt av tett kontakt med alle interessenter for å oppnå et godt resultat Innhold:Utviklingsprosesser. Modellering. UML. Verktøy. Objektorientert analyse Objektorientert design. Bruk av arkitektoniske stiler og design mønstre. Implementasjon og test.Les mer om faget her Påmeldingsfrist: 25.08.13 / 25.01.14         Dette faget går: Høst 2013    Fag Objektorientert systemutvikling 4980,-         Semesteravgift og eksamenskostnader kommer i tillegg.    [-]
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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 [-]
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Virtuelt klasserom 4 dager 24 500 kr
This course teaches Solutions Architects how to translate business requirements into secure, scalable, and reliable solutions. Lessons include design considerations relat... [+]
Recommend solutions to minimize costs Recommend a solution for Conditional Access, including multi-factor authentication Recommend a solution for a hybrid identity including Azure AD Connect and Azure AD Connect Recommend a solution for using Azure Policy Recommend a solution that includes KeyVault Recommend a solution that includes Azure AD Managed Identities Recommend a storage access solution Design and Azure Site Recovery solution Recommend a solution for autoscaling Recommend a solution for containers Recommend a solution for network security Recommend a solution for migrating applications and VMs Recommend a solution for migration of databases  Agenda Module 1: Design for Cost Optimization -Recommend Solutions for Cost Management-Recommended Viewpoints for Minimizing Costs Module 2: Design a Solution for Logging and Monitoring -Azure Monitoring Services-Azure Monitor Module 3: Design Authentication -Recommend a Solution for Multi-Factor Authentication-Recommend a Solution for Single-Sign On (SSO)-Five Steps for Securing Identity Infrastructure-Recommend a Solution for a Hybrid Identity-Recommend a Solution for B2B Integration Module 4: Design Authorization -Infrastructure Protection-Recommend a Hierarchical Structure for Management Groups, Subscriptions and Resource Groups Module 5: Design Governance -Recommend a Solution for using Azure Policy-Recommend a Solution for using Azure Blueprint Module 6: Design Security for Applications -Recommend a Solution using KeyVault-Recommend a Solution using Azure AD Managed Identities Module 7: Design a Solution for Databases Select an Appropriate Data Platform Based on RequirementsOverview of Azure Data StorageRecommend Database Service Tier SizingDynamically Scale Azure SQL Database and Azure SQL Managed InstancesRecommend a Solution for Encrypting Data at Rest, Transmission, and In Use Module 8: Design Data Integration -Recommend a Data Flow-Recommend a Solution for Data Integration Module 9: Select an Appropriate Storage Account -Understanding Storage Tiers-Recommend a Storage Access Solution-Recommend Storage Management Tools Module 10: Design a Solution for Backup and Recovery -Recommend a Recovery Solution for Hybrid and On-Premises Workloads-Design and Azure Site Recovery Solution-Recommend a Solution for Recovery in Different Regions-Recommend a Solution for Azure Backup Management-Design a Solution for Data Archiving and Retention Module 11: Design for High Availability -Recommend a Solution for Application and Workload Redundancy-Recommend a Solution for Autoscaling-Identify Resources that Require High Availability-Identify Storage Tpes for High Availability-Recommend a Solution for Geo-Redundancy of Workloads Module 12: Design a Compute Solution -Recommend a Solution for Compute Provisioning-Determine Appropriate Compute Technologies-Recommend a Solution for Containers-Recommend a Solution for Automating Compute Management Module 13: Design a Network Solution -Recommend a Solution for Network Addressing and Name Resolution-Recommend a Solution for Network Provisioning-Recommend a Solution for Network Security-Recommend a Solution for iInternete Connectivity and On-Premises Networks,-Recommend a Solution for Automating Network Management-Recommend a Solution for Load Balancing and Rraffic Routing Module 14: Design an Application Architecture -Recommend a Microservices Architecture-Recommend an Orchestration Solution for Deployment of Applications-Recommend a Solution for API Integration Module 15: Design Migrations -Assess and On-Premises Servers and Applications for Migration-Recommend a Solution for Migrating Applications and VMs-Recommend a Solution for Migration of Databases [-]
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Virtuelt klasserom 4 dager 24 000 kr
This course provides students with the skills and knowledge required to successfully create and maintain the cloud and edge portions of an Azure IoT solution. The course ... [+]
  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 [-]
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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 [-]
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Nettkurs 375 kr
I dette kurset gir Inga Strümke deg en innføring i hva kunstig intelligens er, og hva du bør tenke på når din bedrift skal ta i bruk kunstig intelligens. [+]
Inga Strümke gir deg en innføring i kunstig intelligens og maskinlæring som gjør det lettere å ta bedre beslutninger. Kunstig intelligens (AI) er mer i vinden enn noensinne, men visste du at det har eksistert som akademisk fagfelt siden 1950-tallet? I dette kurset får du en innføring i hva kunstig intelligens egentlig er for noe, hvordan det brukes i dag og hvordan du kan anvende det for å ta bedre beslutninger. Du lærer om maskinlæring og nevrale nettverk, og hvordan dyp læring brukes til komplekse problemer som språkforståelse og bildegjenkjenning. Du får innsikt i fallgruver, hvorfor de oppstår og hvordan de kan unngås, og ikke minst – hva du bør tenke på når din bedrift skal ta i bruk kunstig intelligens.  HVA VIL DU LÆRE: Kunstig intelligens Maskinlæring, dyp læring og nevrale nettverk Data Bildegjenkjenning og språkforståelse Proxyvariabler og korrelasjon i modeller Forklaringer: Hva og for hvem? Integrering i bedriften Leksjoner Introduksjon til kurset Innføring i kunstig intelligens og algoritmer Maskinlæring Data  Nevrale nettverk og dyp læring Bildegjenkjenning Språkmodeller Proxy-variabler og et eksempel fra forsikring Korrelasjon og kausalitet  Forklaring - hva og for hvem? Eksempler på bruk Helhetlig integrering  Oppsummering [-]
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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. [-]
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Virtuelt klasserom 3 dager 20 000 kr
Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. [+]
 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   [-]
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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 [-]
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Oslo Bergen Og 1 annet sted 2 dager 16 900 kr
25 Apr
25 Apr
06 Jun
Modern Service Oriented Architecture [+]
Modern Service Oriented Architecture [-]
Les mer