Oversikt over relevante kurs innen IT-kurs i Slätta-Korsarvet

ITIL® 4 Foundation-kurset er en introduksjon til ITIL® 4. Kurset lar kandidater se på IT-tjenestestyring gjennom en ende-til-ende driftsmodell, som inkluderer oppretting, levering og kontinuerlig forbedring av IT-relaterte produkter og tjenester. E-læringskurset inneholder 12 timer med undervisning, og er delt inn i 8 moduler. Les mer om ITIL® 4 på AXELOS sine websider. Inkluderer: Tilgang til ITIL® 4 Foundation e-læring (engelsk) i 12 måneder. ITIL® Foundation online voucher til sertifiseringstest + digital ITIL Foundation bok Du vil motta en e-post med tilgang til e-læringen, sertifiseringsvoucher og digital bok fra Peoplecert. Du avtaler tid for sertifiseringen som beskrevet i e-posten fra Peoplecert. Sertifiseringen består av: 40 spørsmål Multiple Choice 60 minutter + 15 minutter til rådighet dersom du ikke har engelsk som morsmål For å bestå må du ha minimum 26 riktige (65%) Ingen hjelpemidler tillatt ITIL®/PRINCE2®/MSP®/MoP® are registered trademarks of AXELOS Limited, used under permission of AXELOS Limited. All rights reserved. [-]

Kurset fokuserer på konvertering av etterspørsel til verdi via IT-relaterte tjenester. Modulen dekker sentrale emner som SLA-design, styring av flere leverandører, kommunikasjon, relasjonsstyring, CX- og UX-design, kartlegging av kunder og mer. E-læringskurset inneholder 18 timer med undervisning, og er delt inn i 8 moduler. Les mer om ITIL® 4 på  AXELOS sine websider. Du vil motta en e-post med tilgang til e-læringen, sertifiseringsvoucher og digital bok fra Peoplecert. Du avtaler tid for sertifiseringen som beskrevet i e-posten fra Peoplecert. [-]

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

Modulen dekker bruk og effekt av Lean og agile arbeidsmåter, og hvordan dette kan utnyttes til fordel for organisasjonen. Kurset vil gi kandidatene en praktisk og strategisk metode for å planlegge og levere kontinuerlig forbedring med nødvendig smidighet.  E-læringskurset inneholder 18 timer med undervisning, og er delt inn i 12 moduler. Les mer om ITIL® 4 på AXELOS sine websider Du vil motta en e-post med tilgang til e-læringen, sertifiseringsvoucher og digital bok fra Peoplecert. Du avtaler tid for sertifiseringen som beskrevet i e-posten fra Peoplecert.   [-]

 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   [-]

COURSE OVERVIEW This course is comprised of the following Microsoft Official Curriculum modules: MS-500T01 Managing Microsoft 365 Identity and Access, MS-500T02 Implementing Microsoft 365 Threat Protection, MS-500T03 Implementing Microsoft 365 Information Protection and MS-500T04 Administering Microsoft 365 Built-in Compliance.   MS-500T01 Managing Microsoft 365 Identity and Access Help protect against credential compromise with identity and access management. In this course you will learn how to secure user access to your organization’s resources. Specifically, this course covers user password protection, multi-factor authentication, how to enable Azure Identity Protection, how to configure Active Directory federation services, how to setup and use Azure AD Connect, and introduces you to Conditional Access. You will also learn about solutions for managing external access to your Microsoft 365 system.   MS500T02 Implementing Microsoft 365 Threat Protection Threat protection helps stop damaging attacks with integrated and automated security. In this course you will learn about threat protection technologies that help protect your Microsoft 365 environment. Specifically, you will learn about threat vectors and Microsoft’s security solutions for them. You will learn about Secure Score, Exchange Online protection, Azure Advanced Threat Protection, Windows Defender Advanced Threat Protection, and how to use Microsoft 365 Threat Intelligence. It also discusses securing mobile devices and applications. The goal of this course is to help you configure your Microsoft 365 deployment to achieve your desired security posture.   MS500T03 Implementing Microsoft 365 Information Protection Information protection is the concept of locating and classifying data anywhere it lives. In this course you will learn about information protection technologies that help secure your Microsoft 365 environment. Specifically, this course discusses information rights managed content, message encryption, as well as labels, policies and rules that support data loss prevention and information protection. Lastly, the course explains the deployment of Microsoft Cloud App Security.   MS500T04 Administering Microsoft 365 Built-in Compliance Internal policies and external requirements for data retention and investigation may be necessary for your organization. In this course you will learn about archiving and retention in Microsoft 365 as well as data governance and how to conduct content searches and investigations. Specifically, this course covers data retention policies and tags, in-place records management for SharePoint, email retention, and how to conduct content searches that support eDiscovery investigations. The course also helps your organization prepare for Global Data Protection Regulation (GDPR).   Virtual Learning   This interactive training can be taken from any location, your office or home and is delivered by a trainer. This training does not have any delegates in the class with the instructor, since all delegates are virtually connected. Virtual delegates do not travel to this course, Global Knowledge will send you all the information needed before the start of the course and you can test the logins. TARGET AUDIENCE This course is for the Microsoft 365 security administrator role. This role collaborates with the Microsoft 365 Enterprise Administrator, business stakeholders and other workload administrators to plan and implement security strategies and ensures that the solutions comply with the policies and regulations of the organization. COURSE CONTENT Module 1: User and Group Security This module explains how to manage user accounts and groups in Microsoft 365. It introduces you to Privileged Identity Management in Azure AD as well as Identity Protection. The module sets the foundation for the remainder of the course.   Module 2: Identity Synchronization This module explains concepts related to synchronizing identities. Specifically, it focuses on Azure AD Connect and managing directory synchronization to ensure the right people are connecting to your Microsoft 365 system.   Module 3: Federated Identities This module is all about Active Directory Federation Services (AD FS). Specifically, you will learn how to plan and manage AD FS to achieve the level of access you want to provide users from other directories.   Module 4: Access Management This module describes Conditional Access for Microsoft 365 and how it can be used to control access to resources in your organization. The module also explains Role Based Access Control (RBAC) and solutions for external access.   Module 5: Security in Microsoft 365 This module starts by explaining the various cyber-attack threats that exist. It then introduces you to the Microsoft solutions to thwart those threats. The module finishes with an explanation of Microsoft Secure Score and how it can be used to evaluate and report your organizations security posture.   Module 6: Advanced Threat Protection This module explains the various threat protection technologies and services available in Microsoft 365. Specifically, the module covers message protection through Exchange Online Protection, Azure Advanced Threat Protection and Windows Defender Advanced Threat Protection.   Module 7: Threat Intelligence This module explains Microsoft Threat Intelligence which provides you with the tools to evaluate and address cyber threats. You will learn how to use the Security Dashboard in the Microsoft 365 Security and Compliance Center. It also explains and configures Microsoft Advanced Threat Analytics.   Module 8: Mobility This module is all about securing mobile devices and applications. You will learn about Mobile Device Management and how it works with Intune. You will also learn about how Intune and Azure AD can be used to secure mobile applications.   Module 9: Information Protection This module explains information rights management in Exchange and SharePoint. It also describes encryption technologies used to secure messages. The module introduces how to implement Azure Information Protection and Windows Information Protection.   Module 10: Data Loss Prevention This module is all about data loss prevention in Microsoft 365. You will learn about how to create policies, edit rules, and customize user notifications.   Module 11: Cloud Application Security This module is all about cloud app security for Microsoft 365. The module will explain cloud discovery, app connectors, policies, and alerts.     [-]

DP-080: Querying Data with Microsoft Transact-SQL [-]

ITIL® 4 Practitioner: Incident Management [-]

Fleksible kurs for fremtiden Ny kunnskap skal gi umiddelbar effekt, og samtidig være holdbar og bærekraftig på lang sikt. NTI AS har 30 års erfaring innen kurs og kompetanseheving, og utdanner årlig rundt 10.000 personer i Nord Europa innen CAD, BIM, industri, design og konstruksjon.   Best practice i Part og Assemblie, tips og triks Ulike metoder på Skjellett-modellering. (Derived VS Multibody) Hvordan bruke Parametere Frame generator Flere Assemblie-features, Design View/ Level of Detail En kjapp innføring i iLogic Kurset vil også i en viss grad bli styrt ut ifra deltagernes spørsmål og ønsker   Her er et utvalg av temaene du vil lære på kurset: Intelligente konstruksjoner Sammenstillingsfunksjoner Konstruksjonsverktøyer Etter endt kurs vil du ha kunnskap og forståelse for oppbygning av definerte mekaniske konstruksjoner med Inventors avanserte designverktøy som f.eks. rammegenerator.Dessuten oppnår du en grundig kunnskap om håndtering og oppbygning av skjelettmodeller med bruk av ”Multi Bodies”.Du lærer å utnytte funksjoner som kan effektivisere håndteringen av samlinger.   Tilpassete kurs for bedrifter Vi vil at kundene våre skal være best på det de gjør - hele tiden.  Derfor tenker vi langsiktig om kompetanseutvikling og ser regelmessig kunnskapsløft som en naturlig del av en virksomhet. Vårt kurskonsept bygger på et moderne sett av ulike læringsmiljøer, som gjør det enkelt å finne riktig løsning uansett behov. Ta kontakt med oss på telefon 483 12 300, epost: salg@nticad.no eller les mer på www.nticad.no [-]

IP1: Generell nettverksteknologi [-]

SFWIPA: Securing Data Center Networks and VPNs with Cisco Secure Firewall Threat Defense [-]

Python Data Science [-]

ArchiMate® 3 Training Course Combined Foundation and Practitioner [-]

COURSE OVERVIEW Learn how to investigate, respond to, and hunt for threats using Microsoft Azure Sentinel, Azure Defender, and Microsoft 365 Defender. In this course you will learn how to mitigate cyberthreats using these technologies. Specifically, you will configure and use Azure Sentinel as well as utilize Kusto Query Language (KQL) to perform detection, analysis, and reporting. The course was designed for people who work in a Security Operations job role and helps learners prepare for the exam SC-200: Microsoft Security Operations Analyst. TARGET AUDIENCE The Microsoft Security Operations Analyst collaborates with organizational stakeholders to secure information technology systems for the organization. Their goal is to reduce organizational risk by rapidly remediating active attacks in the environment, advising on improvements to threat protection practices, and referring violations of organizational policies to appropriate stakeholders. Responsibilities include threat management, monitoring, and response by using a variety of security solutions across their environment. The role primarily investigates, responds to, and hunts for threats using Microsoft Azure Sentinel, Azure Defender, Microsoft 365 Defender, and third-party security products. Since the Security Operations Analyst consumes the operational output of these tools, they are also a critical stakeholder in the configuration and deployment of these technologies. COURSE OBJECTIVES Explain how Microsoft Defender for Endpoint can remediate risks in your environment Create a Microsoft Defender for Endpoint environment Configure Attack Surface Reduction rules on Windows 10 devices Perform actions on a device using Microsoft Defender for Endpoint Investigate domains and IP addresses in Microsoft Defender for Endpoint Investigate user accounts in Microsoft Defender for Endpoint Configure alert settings in Microsoft Defender for Endpoint Explain how the threat landscape is evolving Conduct advanced hunting in Microsoft 365 Defender Manage incidents in Microsoft 365 Defender Explain how Microsoft Defender for Identity can remediate risks in your environment. Investigate DLP alerts in Microsoft Cloud App Security Explain the types of actions you can take on an insider risk management case. Configure auto-provisioning in Azure Defender Remediate alerts in Azure Defender Construct KQL statements Filter searches based on event time, severity, domain, and other relevant data using KQL Extract data from unstructured string fields using KQL Manage an Azure Sentinel workspace Use KQL to access the watchlist in Azure Sentinel Manage threat indicators in Azure Sentinel Explain the Common Event Format and Syslog connector differences in Azure Sentinel Connect Azure Windows Virtual Machines to Azure Sentinel Configure Log Analytics agent to collect Sysmon events Create new analytics rules and queries using the analytics rule wizard Create a playbook to automate an incident response Use queries to hunt for threats Observe threats over time with livestream COURSE CONTENT Module 1: Mitigate threats using Microsoft Defender for Endpoint Implement the Microsoft Defender for Endpoint platform to detect, investigate, and respond to advanced threats. Learn how Microsoft Defender for Endpoint can help your organization stay secure. Learn how to deploy the Microsoft Defender for Endpoint environment, including onboarding devices and configuring security. Learn how to investigate incidents and alerts using Microsoft Defender for Endpoints. Perform advanced hunting and consult with threat experts. You will also learn how to configure automation in Microsoft Defender for Endpoint by managing environmental settings.. Lastly, you will learn about your environment's weaknesses by using Threat and Vulnerability Management in Microsoft Defender for Endpoint. Lessons M1 Protect against threats with Microsoft Defender for Endpoint Deploy the Microsoft Defender for Endpoint environment Implement Windows 10 security enhancements with Microsoft Defender for Endpoint Manage alerts and incidents in Microsoft Defender for Endpoint Perform device investigations in Microsoft Defender for Endpoint Perform actions on a device using Microsoft Defender for Endpoint Perform evidence and entities investigations using Microsoft Defender for Endpoint Configure and manage automation using Microsoft Defender for Endpoint Configure for alerts and detections in Microsoft Defender for Endpoint Utilize Threat and Vulnerability Management in Microsoft Defender for Endpoint Lab M1: Mitigate threats using Microsoft Defender for Endpoint Deploy Microsoft Defender for Endpoint Mitigate Attacks using Defender for Endpoint After completing module 1, students will be able to: Define the capabilities of Microsoft Defender for Endpoint Configure Microsoft Defender for Endpoint environment settings Configure Attack Surface Reduction rules on Windows 10 devices Investigate alerts in Microsoft Defender for Endpoint Describe device forensics information collected by Microsoft Defender for Endpoint Conduct forensics data collection using Microsoft Defender for Endpoint Investigate user accounts in Microsoft Defender for Endpoint Manage automation settings in Microsoft Defender for Endpoint Manage indicators in Microsoft Defender for Endpoint Describe Threat and Vulnerability Management in Microsoft Defender for Endpoint Module 2: Mitigate threats using Microsoft 365 Defender Analyze threat data across domains and rapidly remediate threats with built-in orchestration and automation in Microsoft 365 Defender. Learn about cybersecurity threats and how the new threat protection tools from Microsoft protect your organization’s users, devices, and data. Use the advanced detection and remediation of identity-based threats to protect your Azure Active Directory identities and applications from compromise. Lessons M2 Introduction to threat protection with Microsoft 365 Mitigate incidents using Microsoft 365 Defender Protect your identities with Azure AD Identity Protection Remediate risks with Microsoft Defender for Office 365 Safeguard your environment with Microsoft Defender for Identity Secure your cloud apps and services with Microsoft Cloud App Security Respond to data loss prevention alerts using Microsoft 365 Manage insider risk in Microsoft 365 Lab M2: Mitigate threats using Microsoft 365 Defender Mitigate Attacks with Microsoft 365 Defender After completing module 2, students will be able to: Explain how the threat landscape is evolving. Manage incidents in Microsoft 365 Defender Conduct advanced hunting in Microsoft 365 Defender Describe the investigation and remediation features of Azure Active Directory Identity Protection. Define the capabilities of Microsoft Defender for Endpoint. Explain how Microsoft Defender for Endpoint can remediate risks in your environment. Define the Cloud App Security framework Explain how Cloud Discovery helps you see what's going on in your organization Module 3: Mitigate threats using Azure Defender Use Azure Defender integrated with Azure Security Center, for Azure, hybrid cloud, and on-premises workload protection and security. Learn the purpose of Azure Defender, Azure Defender's relationship to Azure Security Center, and how to enable Azure Defender. You will also learn about the protections and detections provided by Azure Defender for each cloud workload. Learn how you can add Azure Defender capabilities to your hybrid environment. Lessons M3 Plan for cloud workload protections using Azure Defender Explain cloud workload protections in Azure Defender Connect Azure assets to Azure Defender Connect non-Azure resources to Azure Defender Remediate security alerts using Azure Defender Lab M3: Mitigate threats using Azure Defender Deploy Azure Defender Mitigate Attacks with Azure Defender After completing module 3, students will be able to: Describe Azure Defender features Explain Azure Security Center features Explain which workloads are protected by Azure Defender Explain how Azure Defender protections function Configure auto-provisioning in Azure Defender Describe manual provisioning in Azure Defender Connect non-Azure machines to Azure Defender Describe alerts in Azure Defender Remediate alerts in Azure Defender Automate responses in Azure Defender Module 4: Create queries for Azure Sentinel using Kusto Query Language (KQL) Write Kusto Query Language (KQL) statements to query log data to perform detections, analysis, and reporting in Azure Sentinel. This module will focus on the most used operators. The example KQL statements will showcase security related table queries. KQL is the query language used to perform analysis on data to create analytics, workbooks, and perform hunting in Azure Sentinel. Learn how basic KQL statement structure provides the foundation to build more complex statements. Learn how to summarize and visualize data with a KQL statement provides the foundation to build detections in Azure Sentinel. Learn how to use the Kusto Query Language (KQL) to manipulate string data ingested from log sources. Lessons M4 Construct KQL statements for Azure Sentinel Analyze query results using KQL Build multi-table statements using KQL Work with data in Azure Sentinel using Kusto Query Language Lab M4: Create queries for Azure Sentinel using Kusto Query Language (KQL) Construct Basic KQL Statements Analyze query results using KQL Build multi-table statements using KQL Work with string data using KQL statements After completing module 4, students will be able to: Construct KQL statements Search log files for security events using KQL Filter searches based on event time, severity, domain, and other relevant data using KQL Summarize data using KQL statements Render visualizations using KQL statements Extract data from unstructured string fields using KQL Extract data from structured string data using KQL Create Functions using KQL Module 5: Configure your Azure Sentinel environment Get started with Azure Sentinel by properly configuring the Azure Sentinel workspace. Traditional security information and event management (SIEM) systems typically take a long time to set up and configure. They're also not necessarily designed with cloud workloads in mind. Azure Sentinel enables you to start getting valuable security insights from your cloud and on-premises data quickly. This module helps you get started. Learn about the architecture of Azure Sentinel workspaces to ensure you configure your system to meet your organization's security operations requirements. As a Security Operations Analyst, you must understand the tables, fields, and data ingested in your workspace. Learn how to query the most used data tables in Azure Sentinel. Lessons M5 Introduction to Azure Sentinel Create and manage Azure Sentinel workspaces Query logs in Azure Sentinel Use watchlists in Azure Sentinel Utilize threat intelligence in Azure Sentinel Lab M5 : Configure your Azure Sentinel environment Create an Azure Sentinel Workspace Create a Watchlist Create a Threat Indicator After completing module 5, students will be able to: Identify the various components and functionality of Azure Sentinel. Identify use cases where Azure Sentinel would be a good solution. Describe Azure Sentinel workspace architecture Install Azure Sentinel workspace Manage an Azure Sentinel workspace Create a watchlist in Azure Sentinel Use KQL to access the watchlist in Azure Sentinel Manage threat indicators in Azure Sentinel Use KQL to access threat indicators in Azure Sentinel Module 6: Connect logs to Azure Sentinel Connect data at cloud scale across all users, devices, applications, and infrastructure, both on-premises and in multiple clouds to Azure Sentinel. The primary approach to connect log data is using the Azure Sentinel provided data connectors. This module provides an overview of the available data connectors. You will get to learn about the configuration options and data provided by Azure Sentinel connectors for Microsoft 365 Defender. Lessons M6 Connect data to Azure Sentinel using data connectors Connect Microsoft services to Azure Sentinel Connect Microsoft 365 Defender to Azure Sentinel Connect Windows hosts to Azure Sentinel Connect Common Event Format logs to Azure Sentinel Connect syslog data sources to Azure Sentinel Connect threat indicators to Azure Sentinel Lab M6: Connect logs to Azure Sentinel Connect Microsoft services to Azure Sentinel Connect Windows hosts to Azure Sentinel Connect Linux hosts to Azure Sentinel Connect Threat intelligence to Azure Sentinel After completing module 6, students will be able to: Explain the use of data connectors in Azure Sentinel Explain the Common Event Format and Syslog connector differences in Azure Sentinel Connect Microsoft service connectors Explain how connectors auto-create incidents in Azure Sentinel Activate the Microsoft 365 Defender connector in Azure Sentinel Connect Azure Windows Virtual Machines to Azure Sentinel Connect non-Azure Windows hosts to Azure Sentinel Configure Log Analytics agent to collect Sysmon events Explain the Common Event Format connector deployment options in Azure Sentinel Configure the TAXII connector in Azure Sentinel View threat indicators in Azure Sentinel Module 7: Create detections and perform investigations using Azure Sentinel Detect previously uncovered threats and rapidly remediate threats with built-in orchestration and automation in Azure Sentinel. You will learn how to create Azure Sentinel playbooks to respond to security threats. You'll investigate Azure Sentinel incident management, learn about Azure Sentinel events and entities, and discover ways to resolve incidents. You will also learn how to query, visualize, and monitor data in Azure Sentinel. Lessons M7 Threat detection with Azure Sentinel analytics Threat response with Azure Sentinel playbooks Security incident management in Azure Sentinel Use entity behavior analytics in Azure Sentinel Query, visualize, and monitor data in Azure Sentinel Lab M7: Create detections and perform investigations using Azure Sentinel Create Analytical Rules Model Attacks to Define Rule Logic Mitigate Attacks using Azure Sentinel Create Workbooks in Azure Sentinel After completing module 7, students will be able to: Explain the importance of Azure Sentinel Analytics. Create rules from templates. Manage rules with modifications. Explain Azure Sentinel SOAR capabilities. Create a playbook to automate an incident response. Investigate and manage incident resolution. Explain User and Entity Behavior Analytics in Azure Sentinel Explore entities in Azure Sentinel Visualize security data using Azure Sentinel Workbooks. Module 8: Perform threat hunting in Azure Sentinel In this module, you'll learn to proactively identify threat behaviors by using Azure Sentinel queries. You'll also learn to use bookmarks and livestream to hunt threats. You will also learn how to use notebooks in Azure Sentinel for advanced hunting. Lessons M8 Threat hunting with Azure Sentinel Hunt for threats using notebooks in Azure Sentinel Lab M8 : Threat hunting in Azure Sentinel Threat Hunting in Azure Sentinel Threat Hunting using Notebooks After completing this module, students will be able to: Describe threat hunting concepts for use with Azure Sentinel Define a threat hunting hypothesis for use in Azure Sentinel Use queries to hunt for threats. Observe threats over time with livestream. Explore API libraries for advanced threat hunting in Azure Sentinel Create and use notebooks in Azure Sentinel [-]

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 [-]