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6 treff ( i Oslo ) i Microsoft Azure
 

Oslo Og 2 andre steder 5 dager 22 500 kr
14 Aug
18 Sep
16 Oct
Implementing Microsoft Azure Infrastructure Solutions [+]
This course is intended for IT professionals who are familiar with managing on-premises IT deployments that include AD DS, virtualization technologies, and applications. The students typically work for organizations that are planning to locate some or all of their infrastructure services on Azure. This course also is intended for IT professionals who want to take the Microsoft Certification exam, 70-533, Implementing Azure Infrastructure Solutions. [-]
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Oslo Og 3 andre steder 5 dager 22 900 kr
04 Sep
11 Sep
18 Sep
Dette kurset sikter til .Net-utviklere som skal designe og utvikle tjenester som henter og modifiserer data lokalt og/eller mot eksterne kilder. [+]
In this course, students will learn how to design and develop services that access local and remote data from various data sources. Students will also learn how to develop and deploy services to hybrid environments, including on-premises servers and Windows Azure. This course can be used as part of your preparation for Microsoft Exam:70-487: Developing Windows Azure and Web Services.   At Course Completion After completing this course, students will be able to: Query and manipulate data with Entity Framework Use ASP.NET Web API to create HTTP-based services and consume them from .NET and non-.NET clients Extend ASP.NET Web API services using message handlers, model binders, action filters, and media type formatters Create SOAP-based services with the Windows Communication Foundation (WCF) and consume them from .NET clients Apply design principles to service contracts and extend WCF services using custom runtime components and behaviors Secure WCF services using transport and message security Use Windows Azure Service Bus for relayed messaging and brokered messaging using queues and topics Host services on on-premises servers, and on various Windows Azure environments, such as Web Roles, Worker Roles, and Web Sites Deploy services to both on-premises servers and Windows Azure Store and access data in Windows Azure Storage, and configure storage access rights Monitor and log services, both on-premises and in Windows Azure Implement federated authentication by using ACS with ASP.NET Web API services Create scalable, load-balanced services [-]
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Oslo Og 3 andre steder 5 dager 27 500 kr
11 Sep
25 Sep
02 Oct
The course introduces the student to Microsoft Azure and then teaches them how to manage their infrastructure in Azure rather than on premise. [+]
his course is aimed at experienced IT Professionals who currently administer their on-premise infrastructure. The course introduces the student to Microsoft Azure and then teaches them how to manage their infrastructure in Azure rather than on premise. This course is combination of standard MOC 20-533 training plus additional modules that extends the depth and knowledge of Microsoft Azure cloud solution. The course itself has a lot of Hands on labs that can provide in practice steps for configuring, managing, utilizing and monitoring Windows Azure, as extension of classical Datacenter. The course also present steps for creating/migrating SharePoint portals and SQL server databases in Windows Azure, as steps for monitoring, managing and provisioning Azure resources with System Center 2012 products. This course always cover the latest Microsoft Azure releases in time that the course is teaching, which means some of the topic might be modified to be relevant with latest technology changes. This course is directly relevant if you want to pass Exam 70-533: Implementing Microsoft Azure Infrastructure Solutions Følgende elementer er inkludert i Boot Campen: Utvidete kursdager fra kl 09.00 til kl 18.00. OBS! Mandag starter kurset kl 10.00. Fredag avsluttes kurset kl 16.00. Kursmateriale: Deltagerne vil få MOC 20533: Implementing Microsoft Azure Infrastructure Solutions (Offisiell Microsoft kursdokumentasjon), samt tilleggsdokumentasjon fra instruktør. Sertifiseringstest: Exam 70-533: Implementing Microsoft Azure Infrastructure Solutions Full bevertning i løpet av uken med buffet lunch og fri tilgang til kaffe, te, frukt og kjeks, samt baguett som serveres kl 16.00 mandag-torsdag.   After completing this course, students will be able to: Describe the Microsoft Azure Resource Manager Implement Resource Groups Configure Azure Automation Use OMS (Log Analytics) Describe Azure architecture components including infrastructure, tools, and portals. Implement and manage virtual networking within Azure and to connect to on-premises environments. Plan and create Azure virtual machines. Configure, manage, and monitor Azure virtual machines to optimize availability and reliability. Implement, manage, backup and monitor storage solutions. Implement Azure Site Recovery Services Plan and implement data services based on SQL Database to support applications. Plan and implement share point server farm in Microsoft Azure Deploy and configure websites. Deploy, configure, monitor, and diagnose cloud services. Publish content through CDNs and publish videos by using Media Services. Create and manage Azure AD directories, and configure application integration with Azure AD. Integrate on-premises Windows AD with Azure AD. Implementing Azure RMS and Multi-factor Authentication Automate operations in Azure management by using PowerShell runbooks. Monitor Azure resources by using System Center 2012 Operation Manager, with Azure Management Pack Hybrid management of public and private cloud using App Controller   MOC 20533: IMPLEMENTING MICROSOFT AZURE INFRASTRUCTURE SOLUTIONS - COURSE OUTLINE Module 1: Introduction to Azure Module 2: Implementing and Managing Virtual Networks Module 3: Implementing Virtual Machines Module 4: Managing Virtual Machines Module 5: Implementing Websites Module 6: Planning and Implementing Storage Module 7: Planning and Implementing Data Services Module 8: Implementing Cloud Services and Mobile Services Module 9: Implementing Content Delivery Networks and Media Services Module 10: Implementing Azure Active Directory Module 11: Managing Active Directory identities in a Hybrid Environment Module 12: Implement Automation   BOOT CAMP EXTRAS - IN ADDITION TO THE MOC CONTENT IN MS 20533 Module 13: Microsoft Azure Remote App Module 14: Deploying SharePoint Farm with Microsoft Azure Module 15: Federating Azure Active Directory with AD FS Module 16: Monitoring Microsoft Azure Module 17: Microsoft Azure Site Recovery Module 18: Hybrid Cloud with System Center [-]
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Oslo 5 dager 30 000 kr
20 Nov
Some of the topics will be introduced at level 200, without requiring you to have data science prerequisites, on the first day. [+]
This 5-day class is taught by Rafal Lukawiecki. Some of the topics will be introduced at level 200, without requiring you to have data science prerequisites, on the first day. However, as the class progresses, the level of the training will quickly increase to 300 and 400. You will learn machine learning, data mining, some statistics, data preparation, and how to interpret the results. You will see how to formulate business questions in terms of data science hypotheses and experiments, and how to prepare inputs to answer those questions. We will cover common issues and mistakes, how to resolve them, like overtraining, and how to cope with rare events, such as fraud. At the end of this course you will be able to plan and run data science projects. As a practicing data miner, Rafal will also share his decade of hands-on experience while teaching you about Azure Machine Learning (Azure ML) which is the foundation of Cortana Analytics Suite, and its highly-visual, on-premises companion, the SQL Server Analysis Services Data Mining engine, supplemented with the free open source and Cortana’s Revolution Analytics R software. We will use some Excel, however, most of our time will be spent in ML Studio, some in R, RStudio, SSDT, SSMS, and the Azure Portal.    MODULE 1: OVERVIEW OF PRACTICAL DATA SCIENCE FOR BUSINESS We begin the course with a quick, high-level introduction of all of the key concepts, terminology, components, and tools. Topics covered include: Introduction to data science and its components Machine learning vs data mining Statistics Big data Data wrangling Team, process, and tools MODULE 2: TOOLS & GETTING STARTED Configuring Cortana Analytics key component, cloud-based Azure ML is effortless. You need to pay a little bit more attention to on-premise R and SQL server environment, to make sure that you can easily access your modeling data. Topics covered include: Getting started with and using Cortana Analytics: Azure ML, SSAS DM, and R Structures, models, data flows Configuration concerns and pricing Azure requirements and dependencies Other components of Cortana Analytics Suite Using Rattle with R and RStudio Getting a feel for the data: interpreting notched boxplots in R MODULE 3: DATA Data science requires you to prepare your data into a rather unique, flat, and completely denormalised format. While inputs are always necessary, and you may need to engineer hundreds of them, we do not need predictive outputs in all cases. Topics covered include: Inputs and outputs, features and labels Data formats, discretization vs continuous Cases, observations, signatures Feature engineering Azure ML data preparation and manipulation modules Preparing unstructured text for text analysis Feature hashing Moving data around and its storage MODULE 4: PROCESS The analytical process consists of problem formulation, data preparation, modelling, validation, and deployment—all in an iterative fashion. You will learn about the CRISP-DM industry-standard approach, as well as the application of the scientific method of reasoning to experimentation, when solving real-world business problems. Topics covered include: Stating business question in data science term CRISP-DM Scientific method of reasoning Hypothesis testing and experiments Iterative hypothesis refinement MODULE 5: ALGORITHM OVERVIEW There are hundreds of machine learning algorithms, yet they belong to just a dozen of groups, of which 4-5 are in very common use. We will introduce those algorithm classes, and we will discuss some of the most often used examples in each class, while explaining which technology tools (Azure ML, SQL, or R) provide their most convenient implementation. Topics covered include: What does data mining do? Algorithm classes in Azure ML, R, and SSAS Supervised vs Unsupervised learning Classifiers Clustering Regression Similarity Matching Recommenders MODULE 6: SEGMENTATION Segmentation is the main application of unsupervised learning using clustering algorithms. While the action of the algorithm is usually quick and easy to configure, interpreting the results can take a lot of time and intuition. We will spend plenty of time practicing segmentation, interpreting the results and subsequently parameterising the algorithm to provide us with additional insight, and to help you apply it back to your own data. You will even learn how to apply this technique for anomaly (outlier) detection and text analytics! Topics covered include: Introduction to segmentation Clustering algorithms (k-means, EM, and others) Interpreting clusters Cluster characteristics Discrimination Tornado charts Using clustering for text analysis Anomaly detection with clustering, PCA and SVMs MODULE 7: CLASSIFICATION Without doubt, classifiers are the most important, and the most often used category of machine learning algorithms, and the foundation of algorithmic data science. We will focus on several variants of the most important classifier algorithm—decision tree—while progressively interpreting the results, and improving its performance. After introducing neural networks and logistic regression we will also compare the performance of all of these classifiers on our test dataset. Topics covered include: Introduction to classifiers Two-class (binary) vs multi-class Decision trees, forests, and boosting Decision jungles * Neural networks and logistic regression Overfitting (overtraining) concerns Using classifiers for text analysis Associative decision trees * MODULE 8: BASIC STATISTICS Basic concepts of statistics, notably: means, medians, modes, and variance or standard deviation, are essential to validating data and model quality. Probability, and the concept of p-values help you decide which of your inputs (features) are more important than others. R makes all of these powerful ideas accessible and visual, while Azure ML enables you to deploy them easily into production. Topics covered include: Basic concepts of statistics: population vs sample, measure types, means and deviations, distributions, confidence intervals, p-values Correlation Descriptive statistics with R Basic concepts of probability Finding important features using p-values, linear regression and ANOVA * MODULE 9: MODEL VALIDATION The most important aspect of any data science project is the iterative validation and improvement of the models. Without validation, your models cannot be used. There are several tests of model validity, and we will focus on accuracy and reliability, showing you different ways to measure it. Topics covered include: Testing accuracy Lift charts Testing reliability Testing usefulness MODULE 10: CLASSIFIER PRECISION Validation of classifiers is likely to be your main occupation as a data scientist, because classifiers are used so often, and because their precision is not always easy to balance with business requirements, such as restricted resources or required business performance. We will introduce the fundamentals of finding the balance between the acceptable number of false positives and false negatives by using classification (confusion) matrices, and plotting the options using ROC (Receiver Operating Characteristic) charts. Topics covered include: Testing classifiers False positives vs. false negatives Classification (confusion) matrix Precision Recall Balancing precision with recall vs business goals and constraints Charting precision-recall (sensitivity-specificity) ROC curves Other measures of accuracy Cross-validation Optimising binary classifier thresholds for a known business goal of prediction quality Refining models to improve accuracy and reliability Using parameter sweeps to fine-tune algorithm performance Class imbalance problem (fraud analytics and rare event prediction) * MODULE 11: REGRESSIONS Considered by some as the numerical equivalent of classifiers, regression is a large subject of its own. We will introduce its simple but a very popular form, linear regression, and the more precise, but also prone-to-overfitting, decision tree variant. Topics covered include: Introduction to simple regressions Linear regression (classic) Regression decision trees and other ensemble regression algorithms Relationship to ANOVA * Measuring linear regression quality (R-squared, predictor p-values, RMSE, MAE, RAE, RSE, and additional testing using R) * MODULE 12: SIMILARITY MATCHING & RECOMMENDERS From basic concepts of similarity matching, through model-based associative analysis, collaborative filtering, to hybrid systems, like the Matchbox algorithm, there are several techniques for building recommenders. You will get a good overview of this subject, as well as an understanding of how to use these techniques for advanced data exploration, such as Market Basket Analysis. Topics covered include: Introduction to recommender concepts Model-based, similarity-based, and hybrid recommenders Association rules Understanding itemsets and rules Rule importance vs. rule probability Data structures for association rules Market Basket Analysis Collaborative filtering Matchbox recommenders Validating recommenders MODULE 13: OTHER ALGORITHMS (BRIEF OVERVIEW) As the course is coming to its end, we will briefly overview some of the remaining and interesting algorithms and techniques, such as text analytics, without going into too much detail, but letting you have an understanding of the existing approaches. Topics covered include: Text analysis Sequence clustering and Markov chains SVM (Support Vector Machines) Time series Image analytics MODULE 14: PRODUCTION & MODEL MAINTENANCE If you plan on using your models for prediction, rather than just for the exploration of data, you need to deploy your models to production and maintain them on an on-going basis. You will learn about the easiest way to do so using Azure ML web services and its REST synchronous and asynchronous APIs, as well as how to deploy and invoke SSAS models by using DMX queries. Topics covered include: Deploying models to production SSAS models and DMX queries Azure ML web services: preparation and publishing Cortana Solutions Gallery REST APIs: request/response vs batch On-going maintenance and model updates   About Rafal Lukawiecki - the instructor Over the years, Rafal Lukawiecki has been recognised as one of the best technical speakers at the world’s most prestigous IT conferences, such as Microsoft TechEd. As a practicing data miner, Rafal will also share his decade of hands-on experience while teaching you about the entire Microsoft analytical toolkit: Azure Machine Learning (Azure ML) and its highly-visual, on-premise companion, the SQL Server Analysis Services Data Mining engine, supplemented with the free open source and Revolution Analytics R software. We will use some Excel, however, most of our time will be spent in ML Studio, some in R, RStudio, SSDT, SSMS, and the Azure Portal.  What did our students say about the last course? "Rafal er en utrolig dyktig formidler, og kurspresentasjonene var gjennomgående noe av det beste jeg har vært på av kursing." "Rafal er utrolig dyktig , godt forberedt og hyggelig/ hjelpsom." "Fantastisk kursholder! Venter med spenning på flere av hans kurs."    [-]
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Oslo Og 3 andre steder 5 dager 22 500 kr
11 Sep
25 Sep
02 Oct
This course is intended for IT professionals who are familiar with managing on-premises IT deployments that include AD DS, virtualization technologies, and applications. [+]
The students typically work for organizations that are planning to locate some or all of their infrastructure services on Azure. This course also is intended for IT professionals who want to take the Microsoft Certification exam, 70-533, Implementing Azure Infrastructure Solutions. This course is directly relevant if you want to pass exam 70-533 .  Betal ditt kurs med SATV kursvouchere Dersom din bedrift har Software Assurance avtale med Microsoft som inkluderer kursvouchere, kan du gå dette kurset gratis og bruke 5 stk Microsoft kursvouchere som betaling. Skriv inn i anmerkningsfeltet når du bestiller at kurset blir betalt med kursvouchere. Her kan du lese mer om administrasjon og bruk av Microsoft kursvouchere.   This course is intended for IT professionals who are familiar with managing on-premises IT deployments that include AD DS, virtualization technologies, and applications. The students typically work for organizations that are planning to locate some or all of their infrastructure services on Azure. This course also is intended for IT professionals who want to take the Microsoft Certification exam, 70-533, Implementing Azure Infrastructure Solutions.  Velg den opplæringsmetoden som passer deg best!     For instruktørledet klasseromskurs eller Connect2Classroom - velg ønsket dato i høyre kolonne. For Microsoft Training On Demand - Bestill her .  [-]
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Oslo Og 3 andre steder 2 dager 10 000 kr
24 Aug
28 Aug
30 Aug
Get hands-on instruction and practice implementing Microsoft Azure in this two day Microsoft Official Course. [+]
You will be presented with Basic cloud computing concepts as well as specific Microsoft Azure features used in day to day administration of cloud resources. You will learn key Microsoft Azure concepts and basic implementation of Azure subscriptions, websites, virtual machines, storage, virtual networks, databases and Microsoft Azure Active Directory. This course is intended for information technology (IT) professionals who have a limited knowledge of cloud technologies and want to learn more about Microsoft Azure. This course provides the underlying knowledge required by all IT professionals who will be using Microsoft Azure, regardless of whether they are an administrator, developer, or database administrator. While this course does not map to an exam, it does provide you with the prerequisite knowledge for taking courses 20532B:Developing Microsoft Azure Solutions & 20533B:Implementing Microsoft Azure Infrastructure Solutions.    Betal ditt kurs med SATV kursvouchere Dersom din bedrift har Software Assurance avtale med Microsoft som inkluderer kursvouchere, kan du gå dette kurset gratis og bruke 2 stk Microsoft kursvouchere som betaling. Skriv inn i anmerkningsfeltet når du bestiller at kurset blir betalt med kursvouchere. Her kan du lese mer om administrasjon og bruk av Microsoft kursvouchere.   [-]
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