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This 1 day course is designed for the end user who is using or will use Office 365. This course will provide delegates with the knowledge and skills to efficiently use Office 365 on a day-to-day basis. The course is designed with real world scenarios in mind. Delegates will learn how to use Outlook Online, Skype for Business, OneDrive for Business, SharePoint Online, and OneNote. At the end of this course delegates will be able to effectively navigate Office 365 and make use of all of the features of Office 365 Office 365 Overview Introducing Cloud Computing Identify and Outline the Component Products in Office 365 including Outlook Web App, Office Online Apps, OneDrive and Skype for Business Navigating around Office 365 Customising the Office 365 Nav Bar Updating your Profile in Office 365 Using the Outlook Online Application Overview of Outlook Online Working with Email and Folders Outlook People and IM Contacts Using the Calendar Shared Calendars Outlook Tasks in the Web App Setting Outlook Options, Signatures, Automatic Replies and Rules Using Skype for Business Overview of Skype for Business Viewing and Setting Presence Status Using Instant Messages in Business Understanding the Interactive Contact Card in Microsoft Office Applications Integration with Outlook Using Skype for Business for Online Presentations including Content Sharing, Polls and a Virtual Whiteboard Working with OneDrive for Business What is OneDrive for Business? Navigating around OneDrive Accessing Content in OneDrive Using the Office Online Apps Sharing Documents and Collaborating Connecting Microsoft Office to OneDrive Creating Office Documents and Saving Directly to OneDrive Using Groups and Delve Introduction to Groups Collaborating using Groups Getting to Content using Delve Requirements Requirements Before attending this course, students must have: Basic understanding of Microsoft Office Basic understanding of Microsoft Windows Operating systems

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Microsoft Project Blue Belt® 2016: In-House Training This course introduces Project Server 2016 features that expedite scheduling projects and simplify managing tasks within an enterprise environment. Learn different aspects of Project Server and their benefits to varying roles in the enterprise, and gain hands-on experience and insights on best practices from SMEs around the world. This course introduces Project Server 2016 features that expedite scheduling projects and simplify managing tasks within an enterprise environment. Learn different aspects of Project Server and their benefits to varying roles in the enterprise, and gain hands-on experience and insights on best practices from SMEs around the world. Users in Project online will get the same benefits of this program. What you Will Learn You'll learn how to: Describe the Enterprise Project Management (EPM) environment Apply the basic project management principles of, initiating, planning, executing, monitoring and controlling, and closing your project schedules Discuss new features Explain PWA views project sites Meet deadlines and budget restrictions Keep the workloads of your resources within their available limits Explain tracking methods and manage task assignments Update the schedule Differentiate between updating tasks and timesheets Use standard reports, custom views, and visual reports for your projects Recognize the potential of the Business Intelligence features Getting Started with Microsoft® Project Server 2016 Describing the EPM context Discovering Project Web App Differentiating the users of PWA Working with Project Professional and PWA Initiating Projects New projects with Project Professional, SharePoint lists, Enterprise Projects Importing schedules and managing project owner and permissions Customize the ribbon with enterprise commands Planning Projects - Scope and Schedule Management Scheduling in PWA Using the Deliverables feature Developing components of the risk management plan and issues tracking Linking planning documents Planning Projects - Staffing Management Plan Building a project team Managing resource availability Reviewing the assignment cycle Managing resource engagements Resolving resource overallocation Executing, Monitoring and Controlling Baselines Working with timesheets Reporting administrative time Tracking methods (% work, actual work, single entry mode) Assignment progress and updates in PWA Task progress and updates in Project Professional and PWA Monitor and Control Projects - Measuring Performance and Reporting Progress Reviewing performance metrics and progress reports Using the preloaded reports at the Business Intelligence Center Considerations for defining custom reports Closing Projects Reviewing the closing processes and closing tasks to updates Supporting the closing process

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Duration 4 Days 24 CPD hours This course is intended for Software engineers concerned with building, managing and deploying AI solutions that leverage Azure AI Services, Azure AI Search, and Azure OpenAI. They are familiar with C# or Python and have knowledge on using REST-based APIs to build computer vision, language analysis, knowledge mining, intelligent search, and generative AI solutions on Azure. AI-102 Designing and Implementing an Azure AI Solution is intended for software developers wanting to build AI infused applications that leverage?Azure AI Services,?Azure AI Search, and?Azure OpenAI. The course will use C# or Python as the programming language. Prerequisites Before attending this course, students must have: Knowledge of Microsoft Azure and ability to navigate the Azure portal Knowledge of either C# or Python Familiarity with JSON and REST programming semantics Recommended course prerequisites AI-900T00: Microsoft Azure AI Fundamentals course 1 - Prepare to develop AI solutions on Azure Define artificial intelligence Understand AI-related terms Understand considerations for AI Engineers Understand considerations for responsible AI Understand capabilities of Azure Machine Learning Understand capabilities of Azure AI Services Understand capabilities of the Azure Bot Service Understand capabilities of Azure Cognitive Search 2 - Create and consume Azure AI services Provision an Azure AI services resource Identify endpoints and keys Use a REST API Use an SDK 3 - Secure Azure AI services Consider authentication Implement network security 4 - Monitor Azure AI services Monitor cost Create alerts View metrics Manage diagnostic logging 5 - Deploy Azure AI services in containers Understand containers Use Azure AI services containers 6 - Analyze images Provision an Azure AI Vision resource Analyze an image Generate a smart-cropped thumbnail 7 - Classify images Provision Azure resources for Azure AI Custom Vision Understand image classification Train an image classifier 8 - Detect, analyze, and recognize faces Identify options for face detection analysis and identification Understand considerations for face analysis Detect faces with the Azure AI Vision service Understand capabilities of the face service Compare and match detected faces Implement facial recognition 9 - Read Text in images and documents with the Azure AI Vision Service Explore Azure AI Vision options for reading text Use the Read API 10 - Analyze video Understand Azure Video Indexer capabilities Extract custom insights Use Video Analyzer widgets and APIs 11 - Analyze text with Azure AI Language Provision an Azure AI Language resource Detect language Extract key phrases Analyze sentiment Extract entities Extract linked entities 12 - Build a question answering solution Understand question answering Compare question answering to Azure AI Language understanding Create a knowledge base Implement multi-turn conversation Test and publish a knowledge base Use a knowledge base Improve question answering performance 13 - Build a conversational language understanding model Understand prebuilt capabilities of the Azure AI Language service Understand resources for building a conversational language understanding model Define intents, utterances, and entities Use patterns to differentiate similar utterances Use pre-built entity components Train, test, publish, and review a conversational language understanding model 14 - Create a custom text classification solution Understand types of classification projects Understand how to build text classification projects 15 - Create a custom named entity extraction solution Understand custom named entity recognition Label your data Train and evaluate your model 16 - Translate text with Azure AI Translator service Provision an Azure AI Translator resource Specify translation options Define custom translations 17 - Create speech-enabled apps with Azure AI services Provision an Azure resource for speech Use the Azure AI Speech to Text API Use the text to speech API Configure audio format and voices Use Speech Synthesis Markup Language 18 - Translate speech with the Azure AI Speech service Provision an Azure resource for speech translation Translate speech to text Synthesize translations 19 - Create an Azure AI Search solution Manage capacity Understand search components Understand the indexing process Search an index Apply filtering and sorting Enhance the index 20 - Create a custom skill for Azure AI Search Create a custom skill Add a custom skill to a skillset 21 - Create a knowledge store with Azure AI Search Define projections Define a knowledge store 22 - Plan an Azure AI Document Intelligence solution Understand AI Document Intelligence Plan Azure AI Document Intelligence resources Choose a model type 23 - Use prebuilt Azure AI Document Intelligence models Understand prebuilt models Use the General Document, Read, and Layout models Use financial, ID, and tax models 24 - Extract data from forms with Azure Document Intelligence What is Azure Document Intelligence? Get started with Azure Document Intelligence Train custom models Use Azure Document Intelligence models Use the Azure Document Intelligence Studio 25 - Get started with Azure OpenAI Service Access Azure OpenAI Service Use Azure OpenAI Studio Explore types of generative AI models Deploy generative AI models Use prompts to get completions from models Test models in Azure OpenAI Studio's playgrounds 26 - Build natural language solutions with Azure OpenAI Service Integrate Azure OpenAI into your app Use Azure OpenAI REST API Use Azure OpenAI SDK 27 - Apply prompt engineering with Azure OpenAI Service Understand prompt engineering Write more effective prompts Provide context to improve accuracy 28 - Generate code with Azure OpenAI Service Construct code from natural language Complete code and assist the development process Fix bugs and improve your code 29 - Generate images with Azure OpenAI Service What is DALL-E? Explore DALL-E in Azure OpenAI Studio Use the Azure OpenAI REST API to consume DALL-E models 30 - Use your own data with Azure OpenAI Service Understand how to use your own data Add your own data source Chat with your model using your own data 31 - Fundamentals of Responsible Generative AI Plan a responsible generative AI solution Identify potential harms Measure potential harms Mitigate potential harms Operate a responsible generative AI solution

Business Intelligence: In-House Training Business Intelligence (BI) refers to a set of technology-based techniques, applications, and practices used to aggregate, analyze, and present business data. BI practices provide historical and current views of vast amounts of data and generate predictions for business operations. The purpose of Business Intelligence is the support of better business decision making. This course provides an overview of the technology and application of BI and how it can be used to improve corporate performance. What you will Learn You will learn how to: Specify a data warehouse schema Identify the data and visualization to be used for data mining and Business Intelligence Design a Business Intelligence user interface Getting Started Introductions Agenda Expectations Foundation Concepts The challenge of decision making What is Business Intelligence? The Business Intelligence value proposition Business Intelligence taxonomy Business Intelligence management issues Sources of Business Intelligence Data warehousing Data and information Information architecture Defining the data warehouse and its relationships Facts and dimensions Modeling, meta-modeling, and schemas Alternate architectures Building the data warehouse Extracting Transforming Loading Setting up the data and relationships Dimensions and the Fact Table Implementing many-to-many relationships in data warehouse Data marts Online Analytical Processing (OLAP) What is OLAP? OLAP and OLTP OLAP functionality Multi-dimensions Thinking in more than two dimensions What are the possibilities? OLAP architecture Cubism Tools OLAP variations - MOLAP, ROLAP, HOLAP BI using SOA Applications of Business Intelligence Applying BI through OLAP Enterprise Resource Planning and CRM Business Intelligence and financial information Business Intelligence User Interfaces and Presentations Data access Push-pull data access Types of decision support systems Designing the front end Presentation formats Dashboards Types of dashboards Common dashboard features Briefing books and scorecards Querying and Reporting Reporting emphasis Retrofitting Talking back Key Performance Indicators Report Definition and Visualization Typical reporting environment Forms of visualization Unconstrained views Data mining What is in the mine? Applications for data mining Data mining architecture Cross Industry Standard Process for Data Mining (CISP-DM) Data mining techniques Validation The Business Intelligence User Experience The business analyst role Business analysis and data analysis Five-step approach Cultural impact Identifying questions Gathering information Understand the goals The strategic Business Intelligence cycle Focus of Business Intelligence Design for the user Iterate the access Iterative solution development process Review and validation questions Basic approaches Building ad-hoc queries Building on-demand self-service reports Closed loop Business Intelligence Coming attractions - future of Business Intelligence Best practices in Business Intelligence

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The course will be delivered through 9 online virtual classroom sessions. The 10th and last session will involve the additional practical work for the course as well as the written examination (multiple choice for City & Guilds and written for Hi-Tech Training) which will take place at our training centre at 43 North Great Georges Street, Dublin 1 (completed online for non ROI learners). During the virtual classroom sessions, trainees will have a live video feed with their instructor talking to them, doing practical live demonstrations on equipment being involved actively in the learning. We will send out a practical kit so that trainees can complete assignments and practical work at home. The kit is the property of Hi-Tech Training and will be returned to Hi-Tech Training on the last day of the course. (The kits may also be purchased by the learner….see the Kits Page Link for more details). The kit consists of power supply unit breadboard, multimeter, cables, The kit consists of breadboard, multimeter, oscilloscope, battery and connectors, components including resistors, LEDs and ICs (AND, NAND, OR, NOR, X-OR, X-NOR, Inverter, Buffer, Latches, Flip-Flops, Timers, etc). Trainees will build various projects as part of the course. The kit forms an integral part of the course, so full course fees need to be paid at least 7 days prior to course commencement to allow time to ship the kit in time for the course.

Duration 4 Days 24 CPD hours This course is intended for 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. Overview 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. 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 with Azure Machine Learning and MLflow. Prerequisites Creating cloud resources in Microsoft Azure. Using Python to explore and visualize data. Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow. Working with containers AI-900T00: Microsoft Azure AI Fundamentals is recommended, or the equivalent experience. 1 - Design a data ingestion strategy for machine learning projects Identify your data source and format Choose how to serve data to machine learning workflows Design a data ingestion solution 2 - Design a machine learning model training solution Identify machine learning tasks Choose a service to train a machine learning model Decide between compute options 3 - Design a model deployment solution Understand how model will be consumed Decide on real-time or batch deployment 4 - Design a machine learning operations solution Explore an MLOps architecture Design for monitoring Design for retraining 5 - Explore Azure Machine Learning workspace resources and assets Create an Azure Machine Learning workspace Identify Azure Machine Learning resources Identify Azure Machine Learning assets Train models in the workspace 6 - Explore developer tools for workspace interaction Explore the studio Explore the Python SDK Explore the CLI 7 - Make data available in Azure Machine Learning Understand URIs Create a datastore Create a data asset 8 - Work with compute targets in Azure Machine Learning Choose the appropriate compute target Create and use a compute instance Create and use a compute cluster 9 - Work with environments in Azure Machine Learning Understand environments Explore and use curated environments Create and use custom environments 10 - Find the best classification model with Automated Machine Learning Preprocess data and configure featurization Run an Automated Machine Learning experiment Evaluate and compare models 11 - Track model training in Jupyter notebooks with MLflow Configure MLflow for model tracking in notebooks Train and track models in notebooks 12 - Run a training script as a command job in Azure Machine Learning Convert a notebook to a script Run a script as a command job Use parameters in a command job 13 - Track model training with MLflow in jobs Track metrics with MLflow View metrics and evaluate models 14 - Perform hyperparameter tuning with Azure Machine Learning Define a search space Configure a sampling method Configure early termination Use a sweep job for hyperparameter tuning 15 - Run pipelines in Azure Machine Learning Create components Create a pipeline Run a pipeline job 16 - Register an MLflow model in Azure Machine Learning Log models with MLflow Understand the MLflow model format Register an MLflow model 17 - Create and explore the Responsible AI dashboard for a model in Azure Machine Learning Understand Responsible AI Create the Responsible AI dashboard Evaluate the Responsible AI dashboard 18 - Deploy a model to a managed online endpoint Explore managed online endpoints Deploy your MLflow model to a managed online endpoint Deploy a model to a managed online endpoint Test managed online endpoints 19 - Deploy a model to a batch endpoint Understand and create batch endpoints Deploy your MLflow model to a batch endpoint Deploy a custom model to a batch endpoint Invoke and troubleshoot batch endpoints

Microsoft Project Blue Belt® 2013: Virtual In-House Training This course introduces Project Server 2013 features that expedite scheduling projects and simplify managing tasks within an enterprise environment. Learn different aspects of Project Server and their benefits to varying roles in the enterprise, and gain hands-on experience and insights on best practices from SMEs around the world. This course introduces Project Server 2013 features that expedite scheduling projects and simplify managing tasks within an enterprise environment. Learn different aspects of Project Server and their benefits to varying roles in the enterprise, and gain hands-on experience and insights on best practices from SMEs around the world. What you Will Learn You'll learn how to: Identify the project's life cycle Understand the Enterprise Project Management (EPM) environment Apply the basic project management principles to selecting, initiating, planning, executing, monitoring and controlling, and closing your Project 2013 schedules Take advantage of new features Explain Project Server 2013 views and project sites Meet deadlines and budget restrictions Keep the workloads of your resources within their available limits Efficiently update your schedule Take advantage of the standard reports, custom views, and visual reports for your projects Take a brief look at the Business Intelligence potential Efficiently and effectively manage your project(s) and programs Work comfortably within Project Server 2013 or Project Online Getting Started with Microsoft® Project Server 2013 Describing the EPM context Discovering Project Server 2013 and Project Online Differentiating the users of Project Server 2013 Working with Project Professional 2013 and Project Server 2013 Recognizing the Life Cycle within EPM Projects and Portfolio Management Portfolio management and governance Originating new initiatives within EPTs and workflows Prioritizing initiatives, analyzing scenarios, optimizing, and selecting the portfolio Initiating Projects Initiating processes with Project Professional, SharePoint lists, Enterprise Projects, and Resource Plans Importing projects and managing project owner and permissions Planning Projects - Scope and Schedule Management Planning context and framework Scheduling in PWA Using the Deliverables feature Planning Projects - Staffing Management Plan Building a project team Managing resource availability Reviewing the assignment cycle Resolving resource overallocation Planning Projects - Cost Components, Baseline, and Consolidated Schedules Developing components of the Cost Management Planning processes Working with the baseline in projects and programs or master schedules Improving the Collaboration in the Project Sites Creating the Project Sites Developing components of the Risk Management Plan and Issues Tracking Linking planning documents Additional apps and customization Executing Projects Understanding executing processes Managing resources using Build Team and other features Working with timesheets Reporting administrative time Configuring personal settings Monitoring and Controlling Projects - Tracking Task and Project Progress Understanding the Monitoring and Controlling processes Task progress and updates in PWA including considerations for different tracking methods Task progress and updates in Project Professional 2013 Monitor and Control Projects - Measuring Performance and Reporting Progress Understanding status reports Reviewing performance metrics and progress reports Taking advantage of preloaded reports at the Business Intelligence Center Considerations for defining custom reports Closing Projects Reviewing the closing processes and closing tasks to updates Supporting the closing process