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Duration 3 Days 18 CPD hours This course is intended for Typical candidates for this course are IT Professionals who deploy small-to-medium scale enterprise network solutions based on Aruba products and technologies. Overview After you successfully complete this course, expect to be able to: Explain how Aruba's wireless networking solutions meet customers' requirements Explain fundamental WLAN technologies, RF concepts, and 802.11 Standards Learn to configure the Mobility Conductor and Mobility Controller to control access to the Employee and Guest WLAN Control secure access to the WLAN using Aruba Firewall Policies and Roles Recognize and explain Radio Frequency Bands and channels, and the standards used to regulate them Describe the concept of radio frequency coverage and interference and successful implementation and diagnosis of WLAN systems Identify and differentiate antenna technology options to ensure optimal coverage in various deployment scenarios Describe RF power technology including, signal strength, how it is measured and why it is critical in designing wireless networks Learn to configure and optimize Aruba ARM and Client Match and Client Insight features Learn how to perform network monitoring functions and troubleshooting This course teaches the knowledge, skills and practical experience required to set up and configure a basic Aruba WLAN utilizing the AOS 8.X architecture and features. Using lecture and labs, this course provides the technical understanding and hands-on experience of configuring a single Mobility Conductor with one controller and AP Aruba WLAN. Participants will learn how to use Aruba hardware and AOS8 to install and build a complete, secure controller network with multiple SSIDs. WLAN Fundamentals Describes the fundamentals of 802.11, RF frequencies and channels Explain RF Patterns and coverage including SNR Roaming Standards and QOS requirements Mobile First Architecture An introduction to Aruba Products including controller types and modes OS 8.X Architecture and features License types and distribution Mobility Conductor Mobility Controller Configuration Understanding Groups and Subgroups Different methods to join Mobility Controller with Mobility Conductor Understanding Hierarchical Configuration Secure WLAN configuration Identifying WLAN requirements such as SSID name, encryption, authentication Explain AP groups structure and profiles Configuration of WLAN using the Mobility Conductor GUI AP Provisioning Describes the communication between AP and Mobility controller Explain the AP booting sequence and requirements Explores the APs controller discovery mechanisms Explains how to secure AP to controller communication using CPSec Describes AP provisioning and operations WLAN Security Describes the 802.11 discovery, authentication and association Explores the various authentication methods, 802.1x with WPA/WPA2, Mac auth Describes the authentication server communication Explains symmetric vs asymmetric Keys, encryption methods WIPS is described along with rogue discovery and protection Firewall Roles and Policies An introduction into Firewall Roles and policies Explains Aruba?s Identity based Firewall Configuration of Policies and Rules including aliases Explains how to assign Roles to users Dynamic RF Management Explain how ARM calibrates the network selecting channels and power settings Explores OS 8.X Airmatch to calibrate the network How Client Match and Client Insight match steers clients to better APs Guest Access Introduces Aruba?s solutions for Guest Access and the Captive portal process Configuration of secure guest access using the internal Captive portal The configuration of Captive portal using Clearpass and its benefits Creating a guest provisioning account Troubleshooting guest access Network Monitoring and Troubleshooting Using the Mobility Conductor dashboard to monitor and diagnose client, WLAN and AP issues Traffic analysis using APPrf with filtering capabilities A view of AirWave's capabilities for monitoring and diagnosing client, WLAN and AP issues

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

Duration 5 Days 30 CPD hours This course is intended for Ideal candidates include network professionals who are looking to build their foundational knowledge of the ClearPass product portfolio. Overview After you successfully complete this course, expect to be able to: Implement a ClearPass network access solution Design and apply effective services and enforcement in ClearPass Troubleshoot a ClearPass solution This course provides participants the foundational skills in Network Access Con-trol using the Aruba ClearPass product portfolio. This 5-day course includes both instructional modules and labs to teach participants about the major fea-tures in the ClearPass portfolio. Participants will learn how to setup ClearPass as an AAA server, and configure the Policy Manager, Guest, OnGuard and Onboard feature sets. In addition, this course covers integration with external Active Directory servers, Monitoring and Reporting, as well as deployment best practices. The student will gain insight into configuring authentication with ClearPass on both wired and wireless networks. Introduction to ClearPass BYOD High Level Overview Posture and Profiling Guest and Onboard AAA Policy Service Rules Authentication Authorization and Roles Enforcement Policy and Profiles External Devices Network Access Devices Messaging Services External Context Servers Endpoint Profiling Introduction to profiling Profiling collectors External collectors and profilers Roles and Enforcement Services Enforcement ClearPass roles Configuring Services Service selection rules Creating services Configuring Web Services Content Manager Skins Web page editor Guest Authentication Captive portal process Web logins Guest accounts Guest access with MAC authentication Guest Access Self-Registration Self-registration experience Configuring self-registration Self-registration process Wired Authentication Wired access Wired enforcement Profiling on wired ports Downloadable User Roles Requirements for DUR DUR enforcement profiles Dynamic Segmentation What is Dynamic Segmentation? How it works Configuring Dynamic Segmentation OnGuard Configuration Postures Posture policies OnGuard Agent Monitoring OnGuard OnGuard in Enforcement OnGuard workflow OnGuard enforcement Onboard Provisioning Configuring certificate authorities Portal setup Configuring services Onboard Administration Managing certificates Managing users and devices Certificate revocation and OCSP Administrative Operations Licenses Certificates Updates and upgrades Gathering logs ClearPass Cluster Cluster structure Policy Manager zones High availability Administrative Access Admin access to NADs Admin access to Policy Manager Guest operator logins Insight Reports Alerts Reports Administration Additional course details: Nexus Humans Aruba ClearPass Configuration training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Aruba ClearPass Configuration course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

About this training course Business Impact: The main aim is to provide insight and understanding of data analytics and machine learning principles through applications. Field data is used to explain data-analysis workflows. Using easy to follow solution scripts, the participants will assess and extract value from the data sets. Hands-on solution approach will give them confidence to try out applicable techniques on data from their field assets. Data analysis means cleaning, inspecting, transforming, and modeling data with the goal of discovering new, useful information and supporting decision-making. In this hands-on 2-day training course, the participants learn some data analysis and data science techniques and workflows applied to petroleum production (specifically artificial lift) while reviewing code and practicing. The focus is on developing data-driven models while keeping our feet closer to the underlying oil and gas production principles. Unique Features: Eight business use cases covering their business impact, code walkthroughs for most all and solution approach. Industry data sets for participants to practice on and take home. No software or complicated Python frameworks required. Training Objectives After the completion of this training course, participants will be able to: Understand digital oil field transformation and its impact on business Examine machine learning methods Review workflows and code implementations After completing the course, participants will have a set of tools and some pathways to model and analyze their data in the cloud, find trends, and develop data-driven models Target Audience This training course is suitable and will greatly benefit the following specific groups: Artificial lift, production and facilities engineers and students to enhance their knowledge base, increase technology awareness, and improve the facility with different data analysis techniques applied on large data sets Course Level Intermediate Advanced Training Methods The course discusses several business use-cases that are amenable to data-driven workflows. For each use case, the instructor will show the solution using a data analysis technique with Python code deployed in the Google cloud. Trainees will solve a problem and tweak their solution. Course Duration: 2 days in total (14 hours). Training Schedule 0830 - Registration 0900 - Start of training 1030 - Morning Break 1045 - Training recommences 1230 - Lunch Break 1330 - Training recommences 1515 - Evening break 1530 - Training recommences 1700 - End of Training The maximum number of participants allowed for this training course is 20. This course is also available through our Virtual Instructor Led Training (VILT) format. Prerequisites: Understanding of petroleum production concepts Knowledge of Python is not a must but preferred to get the full benefit. The training will use the Google Collaboratory environment available in Google-Cloud for hands-on exercises Trainees will need to bring a computer with a Google Chrome browser and a Google email account (available for free) Trainer Your expert course leader has over 35 years' work-experience in multiphase flow, artificial lift, real-time production optimization and software development/management. His current work is focused on a variety of use cases like failure prediction, virtual flow rate determination, wellhead integrity surveillance, corrosion, equipment maintenance, DTS/DAS interpretation. He has worked for national oil companies, majors, independents, and service providers globally. He has multiple patents and has delivered a multitude of industry presentations. Twice selected as an SPE distinguished lecturer, he also volunteers on SPE committees. He holds a Bachelor's and Master's in chemical engineering from the Gujarat University and IIT-Kanpur, India; and a Ph.D. in Petroleum Engineering from the University of Tulsa, USA. Highlighted Work Experience: At Weatherford, consulted with clients as well as directed teams on digital oilfield solutions including LOWIS - a solution that was underneath the production operations of Chevron and Occidental Petroleum across the globe. Worked with and consulted on equipment's like field controllers, VSDs, downhole permanent gauges, multiphase flow meters, fibre optics-based measurements. Shepherded an enterprise-class solution that is being deployed at a major oil and gas producer for production management including artificial lift optimization using real time data and deep-learning data analytics. Developed a workshop on digital oilfield approaches for production engineers. Patents: Principal inventor: 'Smarter Slug Flow Conditioning and Control' Co-inventor: 'Technique for Production Enhancement with Downhole Monitoring of Artificially Lifted Wells' Co-inventor: 'Wellbore real-time monitoring and analysis of fracture contribution' Worldwide Experience in Training / Seminar / Workshop Deliveries: Besides delivering several SPE webinars, ALRDC and SPE trainings globally, he has taught artificial lift at Texas Tech, Missouri S&T, Louisiana State, U of Southern California, and U of Houston. He has conducted seminars, bespoke trainings / workshops globally for practicing professionals: Companies: Basra Oil Company, ConocoPhillips, Chevron, EcoPetrol, Equinor, KOC, ONGC, LukOil, PDO, PDVSA, PEMEX, Petronas, Repsol, , Saudi Aramco, Shell, Sonatrech, QP, Tatneft, YPF, and others. Countries: USA, Algeria, Argentina, Bahrain, Brazil, Canada, China, Croatia, Congo, Ghana, India, Indonesia, Iraq, Kazakhstan, Kenya, Kuwait, Libya, Malaysia, Oman, Mexico, Norway, Qatar, Romania, Russia, Serbia, Saudi Arabia, S Korea, Tanzania, Thailand, Tunisia, Turkmenistan, UAE, Ukraine, Uzbekistan, Venezuela. Virtual training provided for PetroEdge, ALRDC, School of Mines, Repsol, UEP-Pakistan, and others since pandemic. POST TRAINING COACHING SUPPORT (OPTIONAL) To further optimise your learning experience from our courses, we also offer individualized 'One to One' coaching support for 2 hours post training. We can help improve your competence in your chosen area of interest, based on your learning needs and available hours. This is a great opportunity to improve your capability and confidence in a particular area of expertise. It will be delivered over a secure video conference call by one of our senior trainers. They will work with you to create a tailor-made coaching program that will help you achieve your goals faster. Request for further information post training support and fees applicable Accreditions And Affliations

Online Course now available via Teachable Platform – How do People Learn? Learn at your own pace… with text and videos to support your learning experience. Inclusive Education: Neurodiversity Course Presented by Inclusive Solutions  About This Course: Designed for educators, psychologists, and teachers, this course offers an immersive experience in inclusive education strategies specifically for neurodivergent children. Two leading experts—Dennis Rollins MBE, celebrated trombonist and teacher, and Colin Newton, an experienced inclusive educational psychologist—will guide you through practical approaches and proven techniques to improve inclusive education.  Key Benefits of the Course:  Understand learning processes tailored to neurodivergent students with learning differences. Explore dominant psychological models of learning and their practical, real-world applications. Participate in engaging, hands-on activities to reinforce inclusive teaching strategies. Gain strategies to foster motivation, confidence, and a sense of autonomy in learners Meet the Instructors: Dennis Rollins MBE Dennis Rollins MBE is a renowned trombone player and award-winning educator with over three decades of experience. Known for his unique approach, Dennis has collaborated with top artists worldwide and has received prestigious awards for both performance and jazz education.   Some of Dennis’ recognised lifetime achievements are as follows:  Worked with artists such as Jamiroquai, Courtney Pine, Maceo Parker, The Brand New Heavies, Blur, US3 and Monty Alexander. Won BBC Best Band with Badbone & Co, 2006. Trombonist of the Year, British Jazz Awards and Ronnie Scott’s Jazz Awards, 2007. X2 nominated in the 2008 Parliamentary Jazz Awards: Musician of the Year and Jazz Education, winning the latter category. JazzYorkshire Award Best Band for his band Velocity Trio, 2012. British Trombone Society (BTS) Player of the year Award, 2013. Honorary Fellowship from the Royal Welsh College of Music and Drama, 2022. Recently honoured with an MBE, Dennis brings a creative, real-world perspective to teaching music, particularly for learners with complex needs.  Learn more about Dennis Rollins: Dennis Rollins Website   Colin Newton Colin Newton is a seasoned educational psychologist and passionate advocate for inclusive education. With over 40 years in the field, working across the UK and internationally. Colin is known for his passion for promoting the inclusion of all children in mainstream schools, and for his practical applied work around person-centred planning, restorative justice and in-depth problem-solving. His work has inspired a shift toward inclusive practices across the UK, as he brings both experience and innovative thinking to the world of education.  Colin says: ‘Instinctively from an early age I was against children being sent away to boarding schools and later to special schools and settings. Later I was inspired by disabled activists and Canadian and North American inclusionists who were working to change the world of education with radical innovative thinking and approaches - the work was political, social and spiritual - I was in and never looked back!’  Colin’s career has been values-driven, but also practical using applied psychology in training and real-life problem-solving with schools, families and individual children and young people.     Learning Objectives: By participating in this course, you’ll be able to:  Gain a deeper understanding of inclusive learning processes for all children and adults Differentiate approaches to address the emotional complexities of teaching and learning Master techniques to support effective teaching and meaningful learning Explore the role of relationships and engagement in successful learning Analyse psychological models and identify their real-world applications in education Reflect on the practices of outstanding teachers to inspire deeper learning   Course Topics: Learning Through Imitation Learn how social learning theory (Bandura) highlights the power of imitation. Discover how learners naturally gravitate toward peers and key adults as role models, absorbing skills and behaviours. Contextual Learning Explore Jerome Bruner’s view on context in learning. Understand how learning is always connected to a broader context, which makes it meaningful and applicable. Motivation and Structured Steps Discover techniques to ignite motivation in learners. We’ll discuss how structured, step-by-step support and person-centred encouragement can make a profound difference, particularly for those with learning barriers. Building Self-Efficacy in Learners Understand how educators can nurture self-efficacy, encouraging learners to set their own goals and take charge of their learning journey. Who Should Attend? This course is ideal for:  Educators and psychologists aiming to enhance their understanding of learning processes Teachers who want to develop inclusive teaching strategies for neurodivergent and learning-different students Format A dynamic, hands-on workshop blending lectures, real-world insights, and practical exercises. 

Course Objectives At the end of this course you will be able to: Record and edit a Macro Assign macros to keyboard shortcuts, Quick Access Toolbar, Buttons and Shape objects. Use the Visual Basic Editor; change the properties of an object; add a module to a project; write the code for a procedure and then run it; and use the Object Browser to search procedures Manipulate data by declaring variables of different data types; combine data by using expressions; use functions to accept input and display output; and declare variables and procedures with the appropriate scope Use decision structures to create procedures that make decisions; and use loop structures to perform repetitive tasks Create an error handling routine in case things go wrong with VBA code ' 1 year email support service Take a closer look at the consistent excellent feedback from our growing corporate clients visiting our site ms-officetraining co uk Customer Feedback Best Training Ever! Just finished a bespoke 1-1 training course in Excel Advanced, Macros & VBA. Pedro is an excellent trainer, imparting his skills and knowledge in the best way - appropriately to audience skills, knowledge and ability. Pedro is always approachable, encouraging and supportive, giving delegates the optimum learning environment. I would not hesitate to recommend Pedro as a trainer, whatever your level of ability. Amanda Morris - Treasury & Systems Accountant at Reall - Real Equity for All The trainer was very knowledgeable, kept everyone involved and was enthusiastic. A great experience.. Simon Harper - Lloyd's of London My learning experience was awesome. Perdinand Reagan - Subsea7 Very nice and relaxed approach to teaching. Was definitely a good learning experience. Jerome Pupe - S5 Agency World The Trainer – Very positive + patient + helpful + thorough Agnes Souza - Direct Wines ' With more than 20 years experience, we deliver courses on all levels of the Desktop version of Microsoft Office and Office 365; ranging from Beginner, Intermediate, Advanced to the VBA level. Our trainers are Microsoft certified professionals with a proven track record with several years experience in delivering public, one to one, tailored and bespoke courses. Our competitive rates start from £550.00 per day of training Tailored training courses: You can choose to run the course exactly as they are outlined by us or we can customise it so that it meets your specific needs. A tailored or bespoke course will follow the standard outline but may be adapted to your specific organisational needs. Introduction to Macros and VBA Introducing Visual Basic for Applications Recording a Macro Naming conventions for Macro Procedures Running a Macro Absolute or Relative Cell Referencing Saving and Opening Files with Macros Making macros always available Adding Macros to Keyboard Shortcuts, Quick Access Toolbar, Buttons and Shapes Editing a Macro in the Visual Basic Editor Understanding the Development Environment Customising the Editor Tips for General Typing in VBA Using Visual Basic Help Working with Procedures Program Design Understanding Modules Naming Rules Creating a Module Understanding Procedures Programming Macro Concepts Creating a Subroutine Creating a Function Understanding Arguments Exiting Procedures Calling Procedures Objects, Properties, Methods and Events Understanding Objects Properties, Methods, and Events Navigating the Object Hierarchy Understanding Collections Accessing a Member of a Collection Understanding Hierarchy Using the Object Browser Using the With Statement Working with Properties Working with Methods Creating an Event Procedure Reserved Macro Names Using Expressions, Variables and Intrinsic Functions Understanding Expressions and Statements Declaring Variables Understanding Data Types Determining the Value of Variables Working with Variable Scope Using Built-in VBA Functions Understanding Constants Using Message Boxes Controlling the Answer to a Message Box Using Input Boxes Declaring and Using Object Variables Controlling Program Execution Understanding Control-of-Flow Structures Using the If...End If Decision Structures Nested If Statements Using the Select Case ... End Select Structure Using the Do ... Loop Structure Using the For ... Next Structure Using the For Each ... Next Structure Guidelines for Use of Control-of-Flow Structures Debugging and Handling Errors Understanding Errors Using Debugging Tools Setting Breakpoints and Using Break Mode Stepping Through Code Trapping Errors with the On Error Statement Understanding the Err Object Working with Inline Error Handling Writing an Error-Handling Routine Working with Forms and Controls Understanding UserForms Creating a Form Displaying and Removing a Form Aligning and Sizing Controls Using the Toolbox Working with a Form's Properties, Methods, and Events Working with Form and Control Properties, Methods and Event Setting the Tab Order Populating a Control Who is this course for? Who is this course for? This course is designed to give proficiency in the Visual Basic Editor (VBE), predominantly making use of Excel objects, understanding Object's Properties, Events and Methods, basic VBA Object oriented programming, event handling, control structures, and debugging tools. Requirements Requirements Preferably, delegates should have attended the Excel Advanced course. Certificates Certificates Certificate of completion Digital certificate - Included

Python Machine Learning algorithms can derive trends (learn) from data and make predictions on data by extrapolating on existing trends. Companies can take advantage of this to gain insights and ultimately improve business. Using Python Machine Learning scikit-learn, practice how to use Python Machine Learning algorithms to perform predictions on data. Learn the below listed algorithms, a small collection of available Python Machine Learning algorithms.

The Jamf 200 course offers a core understanding of Jamf Pro. It also provides enterprise-level knowledge of macOS and iOS platforms. Rely on our expert trainers to help you pass the Jamf 200 certification first time. Topics: Introduction to the Jamf Pro server. Building and managing content (.pkg, .mpkg, and .dmg packages) for deployment to macOS devices. Enrolling macOS and iOS devices using automated MDM enrollment. Setup and configuration of macOS and iOS devices. Configuring the user environment (Configuration Profiles, .plist). Security for macOS and iOS devices. Purchasing and distributing App Store apps using Apple Business Manager or Apple School Manager. Scripting overview (Bash). Initial setup and refreshing/reimaging macOS and iOS devices. Ownership and permissions (POSIX, ACE/ACL) on macOS devices. Prerequisites: Abundant hands-on experience with macOS and iOS. What’s Included: Four days (9am-5pm each day) of lab-style interaction. Proctored certification exam during the afternoon of the fourth day. All hardware and software required for the course. Printed course materials. Jamf Certified Tech badge on your Jamf Nation profile (upon successfully completing the exam with a passing score). Free £100 Apple technical training credit

Introduction to Virtualization course description A comprehensive tour of virtualization. The course concentrates on the actual technologies involved as opposed to any one vendor solution. What will you learn Explain the concepts of virtualization. Partition servers. Create Virtual Machines. Introduction to Virtualization course details Who will benefit: Anyone looking for an introduction to Virtualization. Prerequisites: None. Duration 2 days Introduction to Virtualization course contents Virtualization Concepts What is Virtualisation? What are virtual machines (VMs)? Virtualisation Landscape. Network Virtualisation. Suitability for Organisations. Advantages of deploying Virtualisation. Downsides of deploying Virtualisation. Overview of Virtualisation products. Hypervisors What is a hypervisor? Difference between type 1 and 2 hypervisors. Available hypervisors. Hypervisors and device drivers. Hands on: Installing Oracle VirtualBox on Windows. Creating/Importing/Configuring VMs. Virtualization Hosts Hardware and resource requirements. Installation of the hypervisor. Hands on: Installing Hyper-V role into Windows Server. Creating/Importing/Configuring VMs. Virtual Machines Creating virtual machines. Resource requirements. Settings. Installation of the guest OS. Additional tools/ extensions for hypervisor integration. VM files and their uses. Virtual hard disk and their formats. Hardware pass through. Hands on: Connecting to VMWare ESXi via WebGUI and using ESXi to create/import/configure VMs. VM Snapshots/Checkpoints What is a snapshot? How to use them and how they impact performance? Creating/deleting/merging of snapshots. Hands on: Using ESXi to create/manage snapshots. Command Line use on the Hypervisor Interacting with the hypervisor through the command line. Simple commands to configure the hypervisor and VMs. Simple scripts. Hands on: Connecting to VMWare ESXi via PowerCLI to manipulate VMs and snaphshots. Virtualization Storage Different types of storage: local vs remote. Local and remote storage technologies. Configuring storage. Hands on: Using ESXi to deploy VMs on remote NFS storage. Virtual Networking How is networking done in virtualization environments. What is a virtual switch and vNIC and what are their performance characteristics? NIC teaming and trunking in the virtual world. Port groups and isolation. Physical NICs and their use in virtual switches. Hands on: Using ESXi to create and configure vswitches and networking. Templates and clones What is a template? What is a clone? When to use templates and clones to optimize VM deployment. Migrating/Importing VMs What is migration? Migrating compute and storage. Importing VMs from files or physical machines. Hands on: Using VMWare vCenter Server to clone/ template/migrate VMs, tag resources, create local user accounts and assign permissions. VMs and Backups Taking backups of your VMs. Restoring your VMs from backups. Virtualization and Licensing Different licensing models and costs. Containerization Concepts What is a container and how is it different from a virtual machine. When to use containers. Docker and Kubernetes

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