1244 AI courses in Manchester delivered Live Online

Duration 4 Days 24 CPD hours This course is intended for Successful students have experience and knowledge in IT operations, including networking, virtualization, identity, security, business continuity, disaster recovery, data platforms, and governance. Students also have experience designing and architecting solutions. Before attending this course, students must have previous experience deploying or administering Azure resources and strong conceptual knowledge of: Azure compute technologies such as VMs, containers and serverless solutions Azure virtual networking to include load balancers Azure Storage technologies (unstructured and databases) General application design concepts such as messaging and high availability This course teaches Azure Solution Architects how to design infrastructure solutions. Course topics cover governance, compute, application architecture, storage, data integration, authentication, networks, business continuity, and migrations. The course combines lecture with case studies to demonstrate basic architect design principles. Prerequisites Before attending this course, students must have previous experience deploying or administering Azure resources and conceptual knowledge of: Azure Active Directory Azure compute technologies such as VMs, containers and serverless solutions Azure virtual networking to include load balancers Azure Storage technologies (unstructured and databases) General application design concepts such as messaging and high availability AZ-104T00 - Microsoft Azure Administrator 1 - Design governance Design for governance Design for management groups Design for subscriptions Design for resource groups Design for resource tags Design for Azure Policy Design for role-based access control (RBAC) Design for Azure landing zones 2 - Design an Azure compute solution Choose an Azure compute service Design for Azure Virtual Machines solutions Design for Azure Batch solutions Design for Azure App Service solutions Design for Azure Container Instances solutions Design for Azure Kubernetes Service solutions Design for Azure Functions solutions Design for Azure Logic Apps solutions 3 - Design a data storage solution for non-relational data Design for data storage Design for Azure storage accounts Design for data redundancy Design for Azure Blob Storage Design for Azure Files Design for Azure managed disks Design for storage security 4 - Design a data storage solution for relational data Design for Azure SQL Database Design for Azure SQL Managed Instance Design for SQL Server on Azure Virtual Machines Recommend a solution for database scalability Recommend a solution for database availability Design security for data at rest, data in motion, and data in use Design for Azure SQL Edge Design for Azure Cosmos DB and Table Storage 5 - Design data integration Design a data integration solution with Azure Data Factory Design a data integration solution with Azure Data Lake Design a data integration and analytic solution with Azure Databricks Design a data integration and analytic solution with Azure Synapse Analytics Design strategies for hot, warm, and cold data paths Design an Azure Stream Analytics solution for data analysis 6 - Design an application architecture Describe message and event scenarios Design a messaging solution Design an Azure Event Hubs messaging solution Design an event-driven solution Design a caching solution Design API integration Design an automated app deployment solution Design an app configuration management solution 7 - Design authentication and authorization solutions Design for identity and access management (IAM) Design for Microsoft Entra ID Design for Microsoft Entra business-to-business (B2B) Design for Azure Active Directory B2C (business-to-customer) Design for conditional access Design for identity protection Design for access reviews Design service principals for applications Design managed identities Design for Azure Key Vault 8 - Design a solution to log and monitor Azure resources Design for Azure Monitor data sources Design for Azure Monitor Logs (Log Analytics) workspaces Design for Azure Workbooks and Azure insights Design for Azure Data Explorer 9 - Design network solutions Recommend a network architecture solution based on workload requirements Design patterns for Azure network connectivity services Design outbound connectivity and routing Design for on-premises connectivity to Azure Virtual Network Choose an application delivery service Design for application delivery services Design for application protection services 10 - Design a solution for backup and disaster recovery Design for backup and recovery Design for Azure Backup Design for Azure blob backup and recovery Design for Azure files backup and recovery Design for Azure virtual machine backup and recovery Design for Azure SQL backup and recovery Design for Azure Site Recovery 11 - Design migrations Evaluate migration with the Cloud Adoption Framework Describe the Azure migration framework Assess your on-premises workloads Select a migration tool Migrate your structured data in databases Select an online storage migration tool for unstructured data Migrate offline data 12 - Introduction to the Microsoft Azure Well-Architected Framework Azure Well-Architected Framework pillars Cost optimization Operational excellence Performance efficiency Reliability Security 13 - Microsoft Azure Well-Architected Framework - Cost Optimization Develop cost-management discipline Design with a cost-efficiency mindset Design for usage optimization Design for rate optimization Monitor and optimize over time 14 - Microsoft Azure Well-Architected Framework - Operational excellence Embrace DevOps culture Establish development standards Evolve operations with observability Deploy with confidence Automate for efficiency Adopt safe deployment practices 15 - Microsoft Azure Well-Architected Framework - Performance efficiency Negotiate realistic performance targets Design to meet capacity requirements Achieve and sustain performance Improve efficiency through optimization 16 - Microsoft Azure Well-Architected Framework - Reliability Design for business requirements Design for resilience Design for recovery Design for operations Keep it simple 17 - Microsoft Azure Well-Architected Framework - Security Plan your security readiness Design to protect confidentiality Design to protect integrity Design to protect availability Sustain and evolve your security posture 18 - Getting started with the Microsoft Cloud Adoption Framework for Azure Customer narrative Common blockers 19 - Prepare for successful cloud adoption with a well-defined strategy Customer narrative Capture strategic motivation Define objectives and key results Evaluate financial considerations Understand technical considerations Create a business case 20 - Prepare for cloud adoption with a data-driven plan Customer narrative 21 - Choose the best Azure landing zone to support your requirements for cloud operations Customer narrative Common operating models Design areas for Azure landing zones Design principles for Azure landing zones Journey to the target architecture Choose an Azure landing zone option Deploy the Azure landing zone accelerator Enhance your landing zone 22 - Migrate to Azure through repeatable processes and common tools Customer narrative Migration process Migration tools Common tech platforms 23 - Address tangible risks with the Govern methodology of the Cloud Adoption Framework for Azure Customer narrative Govern methodology Corporate policies Governance disciplines Deploy a cloud governance foundation The Cost Management discipline 24 - Ensure stable operations and optimization across all supported workloads deployed to the cloud Establish business commitments Deploy an operations baseline Protect and recover Enhance an operations baseline Manage platform and workload specialization 25 - Innovate applications by using Azure cloud technologies Follow the innovation lifecycle Azure technologies for the build process Infuse your applications with AI Azure technologies for measuring business impact Azure technologies for the learn process 26 - Prepare for cloud security by using the Microsoft Cloud Adoption Framework for Azure Customer narrative Methodology Security roles and responsibilities Simplify compliance and security Simplify security implementation Security tools and policies Additional course details: Nexus Humans AZ-305T00: Designing Microsoft Azure Infrastructure Solutions 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 AZ-305T00: Designing Microsoft Azure Infrastructure Solutions 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.

Artificial Intelligence brings exciting new opportunities to the field of Conversational User Interfaces (CUI). Learn key concepts and proven design methods to deliver cutting-edge experiences and reach better business outcomes. Silvia Podesta is a Designer in the Client Engineering Team at IBM Nordics. She leverages design thinking, service and UX design to help clients identify opportunities for innovation and pioneer transformational experiences through IBM technology.

The One Day - MHFA Champion Course teaches you how to identify when a person might be experiencing a mental health issue and how to guide them to get the help they need.

Duration 2 Days 12 CPD hours This course is intended for System architects, system administrators, IT managers, VMware partners, and individuals responsible for implementing and managing vSphere architectures who want to deploy vSphere 8.0 into their existing vSphere environment. Overview By the end of the course, you should be able to meet the following objectives: Recognize the importance of key features and enhancements in vSphere 8.0 Describe vCenter Server, VMware ESXi, storage, virtual machine, and security enhancements in vSphere 8.0 Describe the purpose of vSphere Distributed Services Engine Update an ESXi host equipped with a Data Processing Unit (DPU) using vSphere Lifecycle Manager Identify devices supported for system storage on ESXi 8.0 Recognize enhancements to VM hardware compatibility settings VMware vSphere Memory Monitoring and Remediation and the improvements to vSphere DRS Recognize the new Virtual Non-Uniform Memory Access (vNUMA) topology settings of a VM in vSphere Client Use vSphere Lifecycle Manager and Auto Deploy to manage the configuration specifications for the hosts in a cluster Recognize the vSphere Lifecycle Manager and Auto Deploy enhancements in vSphere 8.0 Recognize the cloud benefits that VMware vSphere+ brings to on-premises workloads Recognize technology that is discontinued or deprecated in vSphere 8.0 In this two-day course, you explore the new features and enhancements following VMware vCenter Server 8.0 and VMware ESXi 8.0. Real-world use-case scenarios, hands-on lab exercises, and lectures teach you the skills that you need to effectively implement and configure VMware vSphere 8.0. Course Introduction Introductions and course logistics Course objectives Artificial Intelligence and Machine Learning Describe how device groups support AI and ML in vSphere 8 Describe how device virtualization extensions support AI and ML in vSphere 8 vSphere Distributed Services Engine Describe the benefits of Distributed Services Engine Explain how Distributed Services Engine works Recognize use cases for Distributed Services Engine Install ESXi on a host equipped with a DPU View DPU information in vSphere Client Add an ESXi host equipped with a DPU to a cluster Update an ESXi host equipped with a DPU using vSphere Lifecycle Manager Create a vSphere Distributed Switch for network offloads Add a host with a DPU to the vSphere Distributed Switch Configure a VM to use Uniform Passthrough Mode vSphere and vCenter Management Review the improvements to the communication between vCenter and ESXi hosts Review the enhancements to the vCenter recovery process ESXi Enhancements Describe the function of the central configuration store in ESXi Explain how ConfigStore affects your interaction with ESXi configuration files Recognize the supported system storage partition configuration on ESXi 8.0 Identify devices supported for system storage on ESXi 8.0 Configure an RDMA host local device on ESXi vSphere Storage Describe the vSAN Express Storage Architecture Recognize the benefits of using vSAN Express Storage Architecture Describe the benefits of using NVMe Recognize the support for NVMe devices in vSphere Guest OS and Workloads Review the enhancements of the latest virtual hardware versions Describe the features introduced with virtual hardware version 20 Create a snapshot of a VM with an NVDIMM device Resource Management View energy and carbon emission metrics in vRealize Operations Manager Describe the VMware vSphere Memory Monitoring and Remediation (vMMR) functionality Describe how vMMR enhances the performance of vSphere DRS Security and Compliance Describe how to handle vTPM secrets when cloning a VM Manage OVF templates for VMs that are configured with vTPM Deploy an OVF template with vTPM Describe the enhancements to trusted binary enforcement in ESXi Describe ESXi 8 enhanced security features vSphere Lifecycle Manager Describe the enhancements to life cycle management of standalone ESXi hosts Manage the configuration profiles of ESXi hosts in a cluster with vSphere Lifecycle Manager Use Auto Deploy to boot a host with the desired image and configuration specifications Upgrade multiple ESXi hosts in a cluster in parallel Stage an ESXi host image prior to remediation Auto Deploy Manage custom host certificates using Auto Deploy vSphere with Tanzu Describe the features of the Tanzu Kubernetes Grid 2.0 offering Announcing vSphere+ Describe the functionality and benefits of vSphere+

Duration 3 Days 18 CPD hours This course is intended for The target audience for the SRE Practitioner course are professionals including: Anyone focused on large-scale service scalability and reliability Anyone interested in modern IT leadership and organizational change approaches Business Managers Business Stakeholders Change Agents Consultants DevOps Practitioners IT Directors IT Managers IT Team Leaders Product Owners Scrum Masters Software Engineers Site Reliability Engineers System Integrators Tool Providers Overview After completing this course, students will have learned: Practical view of how to successfully implement a flourishing SRE culture in your organization. The underlying principles of SRE and an understanding of what it is not in terms of anti-patterns, and how you become aware of them to avoid them. The organizational impact of introducing SRE. Acing the art of SLIs and SLOs in a distributed ecosystem and extending the usage of Error Budgets beyond the normal to innovate and avoid risks. Building security and resilience by design in a distributed, zero-trust environment. How do you implement full stack observability, distributed tracing and bring about an Observability-driven development culture? Curating data using AI to move from reactive to proactive and predictive incident management. Also, how you use DataOps to build clean data lineage. Why is Platform Engineering so important in building consistency and predictability of SRE culture? Implementing practical Chaos Engineering. Major incident response responsibilities for a SRE based on incident command framework, and examples of anatomy of unmanaged incidents. Perspective of why SRE can be considered as the purest implementation of DevOps SRE Execution model Understanding the SRE role and understanding why reliability is everyone's problem. SRE success story learnings This course introduces a range of practices for advancing service reliability engineering through a mixture of automation, organizational ways of working and business alignment. Tailored for those focused on large-scale service scalability and reliability. SRE Anti-patterns Rebranding Ops or DevOps or Dev as SRE Users notice an issue before you do Measuring until my Edge False positives are worse than no alerts Configuration management trap for snowflakes The Dogpile: Mob incident response Point fixing Production Readiness Gatekeeper Fail-Safe really? SLO is a Proxy for Customer Happiness Define SLIs that meaningfully measure the reliability of a service from a user?s perspective Defining System boundaries in a distributed ecosystem for defining correct SLIs Use error budgets to help your team have better discussions and make better data-driven decisions Overall, Reliability is only as good as the weakest link on your service graph Error thresholds when 3rd party services are used Building Secure and Reliable Systems SRE and their role in Building Secure and Reliable systems Design for Changing Architecture Fault tolerant Design Design for Security Design for Resiliency Design for Scalability Design for Performance Design for Reliability Ensuring Data Security and Privacy Full-Stack Observability Modern Apps are Complex & Unpredictable Slow is the new down Pillars of Observability Implementing Synthetic and End user monitoring Observability driven development Distributed Tracing What happens to Monitoring? Instrumenting using Libraries an Agents Platform Engineering and AIOPs Taking a Platform Centric View solves Organizational scalability challenges such as fragmentation, inconsistency and unpredictability. How do you use AIOps to improve Resiliency How can DataOps help you in the journey A simple recipe to implement AIOps Indicative measurement of AIOps SRE & Incident Response Management SRE Key Responsibilities towards incident response DevOps & SRE and ITIL OODA and SRE Incident Response Closed Loop Remediation and the Advantages Swarming ? Food for Thought AI/ML for better incident management Chaos Engineering Navigating Complexity Chaos Engineering Defined Quick Facts about Chaos Engineering Chaos Monkey Origin Story Who is adopting Chaos Engineering Myths of Chaos Chaos Engineering Experiments GameDay Exercises Security Chaos Engineering Chaos Engineering Resources SRE is the Purest form of DevOps Key Principles of SRE SREs help increase Reliability across the product spectrum Metrics for Success Selection of Target areas SRE Execution Model Culture and Behavioral Skills are key SRE Case study Post-class assignments/exercises Non-abstract Large Scale Design (after Day 1) Engineering Instrumentation- Instrumenting Gremlin (after Day 2)

Whetstone Communications and comms2point0 are pleased to bring you the Data Bites series of free webinars. Our aim is to boost interest and levels of data literacy among not-for-profit communicators.

This week long CPD accredited training is ideal for those Real Estate professionals, who are looking to bolster the DCF modelling skills. This course covers Real Estate Investment, Development, Finance & Risk.

Change and uncertainty - staying resilient in a shifting landscape Facilitated by Claire Warner Charity Culture, Wellbeing & Leadership Specialist Aimed at those working in fundraising and marcomms roles in UK hospices - - - Change is inevitable - but that doesn’t mean it’s easy. Whether it’s shifting targets, new team structures, or sector-wide challenges, hospice fundraisers are constantly navigating uncertainty. This interactive workshop will help you: ✅ Understand why change feels hard—and how to make it easier ✅ Identify what’s in your control (and let go of what’s not) ✅ Build resilience and confidence in uncertain times With practical strategies, group discussions, and real-world tools, you’ll leave feeling more in control, no matter what’s ahead. - - - Claire Warner (she/her) is a Charity Culture, Wellbeing & Leadership Specialist. Before developing this specialism, Claire had 19 years working in the charity sector, including 10 years in Director / Senior Leader roles. But it was a period of significant unwellbeing (breast cancer) that led Claire to the career change. And it was in conducting a large piece of research into sector wellbeing, that Claire recognised this significant gap in provision and went on to create Lift. In 2020, Claire won the Best Digital Leader Award at the Social CEO Awards for her wellbeing work during the pandemic. In 2021, she curated the first Charity Workplace Wellbeing Summit and was named as one of Charity Times Magazine’s 20 Pandemic Pioneers. Claire lives in rural Lancashire with her husband, their two daughters, Rowan the dog and horses Maddie and Bernie.

The two-day Youth MHFAider® course is tailored for people who teach, work, live with, support and care for young people aged 8 to 18. This mental health first aid course can be attended by anyone from age 16 upwards.

Duration 2 Days 12 CPD hours This course is intended for Audience: Data Scientists, Software Developers, IT Architects, and Technical Managers. Participants should have the general knowledge of statistics and programming Also familiar with Python Overview ? NumPy, pandas, Matplotlib, scikit-learn ? Python REPLs ? Jupyter Notebooks ? Data analytics life-cycle phases ? Data repairing and normalizing ? Data aggregation and grouping ? Data visualization ? Data science algorithms for supervised and unsupervised machine learning Covers theoretical and technical aspects of using Python in Applied Data Science projects and Data Logistics use cases. Python for Data Science ? Using Modules ? Listing Methods in a Module ? Creating Your Own Modules ? List Comprehension ? Dictionary Comprehension ? String Comprehension ? Python 2 vs Python 3 ? Sets (Python 3+) ? Python Idioms ? Python Data Science ?Ecosystem? ? NumPy ? NumPy Arrays ? NumPy Idioms ? pandas ? Data Wrangling with pandas' DataFrame ? SciPy ? Scikit-learn ? SciPy or scikit-learn? ? Matplotlib ? Python vs R ? Python on Apache Spark ? Python Dev Tools and REPLs ? Anaconda ? IPython ? Visual Studio Code ? Jupyter ? Jupyter Basic Commands ? Summary Applied Data Science ? What is Data Science? ? Data Science Ecosystem ? Data Mining vs. Data Science ? Business Analytics vs. Data Science ? Data Science, Machine Learning, AI? ? Who is a Data Scientist? ? Data Science Skill Sets Venn Diagram ? Data Scientists at Work ? Examples of Data Science Projects ? An Example of a Data Product ? Applied Data Science at Google ? Data Science Gotchas ? Summary Data Analytics Life-cycle Phases ? Big Data Analytics Pipeline ? Data Discovery Phase ? Data Harvesting Phase ? Data Priming Phase ? Data Logistics and Data Governance ? Exploratory Data Analysis ? Model Planning Phase ? Model Building Phase ? Communicating the Results ? Production Roll-out ? Summary Repairing and Normalizing Data ? Repairing and Normalizing Data ? Dealing with the Missing Data ? Sample Data Set ? Getting Info on Null Data ? Dropping a Column ? Interpolating Missing Data in pandas ? Replacing the Missing Values with the Mean Value ? Scaling (Normalizing) the Data ? Data Preprocessing with scikit-learn ? Scaling with the scale() Function ? The MinMaxScaler Object ? Summary Descriptive Statistics Computing Features in Python ? Descriptive Statistics ? Non-uniformity of a Probability Distribution ? Using NumPy for Calculating Descriptive Statistics Measures ? Finding Min and Max in NumPy ? Using pandas for Calculating Descriptive Statistics Measures ? Correlation ? Regression and Correlation ? Covariance ? Getting Pairwise Correlation and Covariance Measures ? Finding Min and Max in pandas DataFrame ? Summary Data Aggregation and Grouping ? Data Aggregation and Grouping ? Sample Data Set ? The pandas.core.groupby.SeriesGroupBy Object ? Grouping by Two or More Columns ? Emulating the SQL's WHERE Clause ? The Pivot Tables ? Cross-Tabulation ? Summary Data Visualization with matplotlib ? Data Visualization ? What is matplotlib? ? Getting Started with matplotlib ? The Plotting Window ? The Figure Options ? The matplotlib.pyplot.plot() Function ? The matplotlib.pyplot.bar() Function ? The matplotlib.pyplot.pie () Function ? Subplots ? Using the matplotlib.gridspec.GridSpec Object ? The matplotlib.pyplot.subplot() Function ? Hands-on Exercise ? Figures ? Saving Figures to File ? Visualization with pandas ? Working with matplotlib in Jupyter Notebooks ? Summary Data Science and ML Algorithms in scikit-learn ? Data Science, Machine Learning, AI? ? Types of Machine Learning ? Terminology: Features and Observations ? Continuous and Categorical Features (Variables) ? Terminology: Axis ? The scikit-learn Package ? scikit-learn Estimators ? Models, Estimators, and Predictors ? Common Distance Metrics ? The Euclidean Metric ? The LIBSVM format ? Scaling of the Features ? The Curse of Dimensionality ? Supervised vs Unsupervised Machine Learning ? Supervised Machine Learning Algorithms ? Unsupervised Machine Learning Algorithms ? Choose the Right Algorithm ? Life-cycles of Machine Learning Development ? Data Split for Training and Test Data Sets ? Data Splitting in scikit-learn ? Hands-on Exercise ? Classification Examples ? Classifying with k-Nearest Neighbors (SL) ? k-Nearest Neighbors Algorithm ? k-Nearest Neighbors Algorithm ? The Error Rate ? Hands-on Exercise ? Dimensionality Reduction ? The Advantages of Dimensionality Reduction ? Principal component analysis (PCA) ? Hands-on Exercise ? Data Blending ? Decision Trees (SL) ? Decision Tree Terminology ? Decision Tree Classification in Context of Information Theory ? Information Entropy Defined ? The Shannon Entropy Formula ? The Simplified Decision Tree Algorithm ? Using Decision Trees ? Random Forests ? SVM ? Naive Bayes Classifier (SL) ? Naive Bayesian Probabilistic Model in a Nutshell ? Bayes Formula ? Classification of Documents with Naive Bayes ? Unsupervised Learning Type: Clustering ? Clustering Examples ? k-Means Clustering (UL) ? k-Means Clustering in a Nutshell ? k-Means Characteristics ? Regression Analysis ? Simple Linear Regression Model ? Linear vs Non-Linear Regression ? Linear Regression Illustration ? Major Underlying Assumptions for Regression Analysis ? Least-Squares Method (LSM) ? Locally Weighted Linear Regression ? Regression Models in Excel ? Multiple Regression Analysis ? Logistic Regression ? Regression vs Classification ? Time-Series Analysis ? Decomposing Time-Series ? Summary Lab Exercises Lab 1 - Learning the Lab Environment Lab 2 - Using Jupyter Notebook Lab 3 - Repairing and Normalizing Data Lab 4 - Computing Descriptive Statistics Lab 5 - Data Grouping and Aggregation Lab 6 - Data Visualization with matplotlib Lab 7 - Data Splitting Lab 8 - k-Nearest Neighbors Algorithm Lab 9 - The k-means Algorithm Lab 10 - The Random Forest Algorithm