730 Computing & Software Development Tools courses

Duration 5 Days 30 CPD hours This course is intended for This course is designed for IT professionals who wish to develop cloud computing skills to enable them to move IT workloads to the cloud and integrate products and services from different providers and industries. Their focus is to ensure that cloud deployments are secure, that automation and orchestration are used effectively to bring business value from the cloud, and that costs are controlled through effective management of cloud vendors. This course is also designed for students who are preparing to take the CompTIA Cloud+ certification exam CV0-003, or who plan to use Cloud+ as the foundation for more advanced cloud certifications or career roles. Overview In this course, you will deploy, test, secure, manage, optimize, and troubleshoot a cloud solution. You will: - Prepare to deploy cloud solutions - Deploy a pilot project - Test a pilot project deployment - Design a secure network for cloud deployment - Determine CPU and memory sizing for cloud deployments - Determine storage requirements for cloud deployments - Plan Identity and Access Management for cloud deployments - Analyze workload characteristics to ensure successful migration to the cloud - Secure systems to meet access requirements - Maintain cloud systems - Implement backup, restore, and business continuity measures - Analyze cloud systems for required performance - Analyze cloud systems for anomalies and growth forecasting - Troubleshoot deployment, capacity, automation, and orchestration issues - Troubleshoot connectivity issues - Troubleshoot security issues In this course, you will learn how to implement, maintain, and deliver cloud technologies including network, storage, and virtualization technologies to create cloud solutions. 1 - PREPARING TO DEPLOY CLOUD SOLUTIONS Describe Interaction of Cloud Components and Services Describe Interaction of Non-cloud Components and Services Evaluate Existing Components and Services for Cloud Deployment Evaluate Automation and Orchestration Options Prepare for Cloud Deployment 2 - DEPLOYING A PILOT PROJECT Manage Change in a Pilot Project Execute Cloud Deployment Workflow Complete Post-Deployment Configuration 3 - TESTING PILOT PROJECT DEPLOYMENTS Identify Cloud Service Components for Testing Test for High Availability and Accessibility Perform Deployment Load Testing Analyze Test Results 4 - DESIGNING A SECURE AND COMPLIANT CLOUD INFRASTRUCTURE Design Cloud Infrastructure for Security Determine Organizational Compliance Needs 5 - DESIGNING AND IMPLEMENTING A SECURE CLOUD ENVIRONMENT Design Virtual Network for Cloud Deployment Determine Network Access Requirements Secure Networks for Cloud Interaction Manage Cloud Component Security Implement Security Technologies 6 - PLANNING IDENTITY AND ACCESS MANAGEMENT FOR CLOUD DEPLOYMENTS Determine Identity Management and Authentication Technologies Plan Account Management Policies for the Network and Systems Control Access to Cloud Objects Provision Accounts 7 - DETERMINING CPU AND MEMORY SIZING FOR CLOUD DEPLOYMENTS Determine CPU Size for Cloud Deployment Determine Memory Size for Cloud Deployment 8 - DETERMINING STORAGE REQUIREMENTS FOR CLOUD DEPLOYMENTS Determine Storage Technology Requirements Select Storage Options for Deployment Determine Storage Access and Provisioning Requirements Determine Storage Security Options 9 - ANALYZING WORKLOAD CHARACTERISTICS TO ENSURE SUCCESSFUL MIGRATION Determine the Type of Cloud Deployment to Perform Manage Virtual Machine and Container Migration Manage Network, Storage, and Data Migration 10 - MAINTAINING CLOUD SYSTEMS Patch Cloud Systems Design and Implement Automation and Orchestration for Maintenance 11 - IMPLEMENTING BACKUP, RESTORE, DISASTER RECOVERY, AND BUSINESS CONTINUITY MEASURES Back Up and Restore Cloud Data Implement Disaster Recovery Plans Implement Business Continuity Plans 12 - ANALYZING CLOUD SYSTEMS FOR PERFORMANCE Monitor Cloud Systems to Measure Performance Optimize Cloud Systems to Meet Performance Criteria 13 - ANALYZING CLOUD SYSTEMS FOR ANOMALIES AND GROWTH FORECASTING Monitor for Anomalies and Resource Needs Plan for Capacity Create Reports on Cloud System Metrics 14 - TROUBLESHOOTING DEPLOYMENT, CAPACITY, AUTOMATION, AND ORCHESTRATION ISSUES Troubleshoot Deployment Issues Troubleshoot Capacity Issues Troubleshoot Automation and Orchestration Issues 15 - TROUBLESHOOTING CONNECTIVITY ISSUES Identify Connectivity Issues Troubleshoot Connectivity Issues 16 - TROUBLESHOOTING SECURITY ISSUES Troubleshoot Identity and Access Issues Troubleshoot Attacks Troubleshoot Other Security Issues Additional course details: Nexus Humans CompTIA Cloud Plus Certification (Exam CV0-003) 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 CompTIA Cloud Plus Certification (Exam CV0-003) 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.

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 3 Days 18 CPD hours This course is intended for Senior Consultants (both functional and technical) that aspire to be Solution Architects, or current Solution Architects that are new to the role. The Solution Architect is responsible for the successful design, implementation, deployment and adoption of an overall solution. The Solution Architect ensures that the solution meets the customer?s needs now and in the future. In this course, students will learn about decisions a Solution Architect makes during an implementation, covering security, integrations, Power Apps architecture, Power Automate architecture, and more. This course is designed to give you an introduction to the Solution Architect role. Becoming a Solution Architect/Getting to know your customer Define a Solution Architect Role of a Solution Architect on projects Project Methodology Getting to know your customer Group exercise - Getting to know your customer Conceptualizing the design from requirements How to lead the requirement collection effort Using fit gap analysis Pillars of good architecture Blueprinting the solution architecture Group exercise - Design from requirements Project governance and working as a team Solution Architect's role in project governance Techniques for keeping a project on track Scenarios that could cause a project to fail Group exercise - Project governance and working as a team Power Platform Architecture Key Power Platform architecture components Understand how platform design and limits influence solution architectures Updates and feature releases Understand how to communicate how the platform meets customer needs Data Modeling Data model influences Data model strategy Data types Data relationships Group exercise - Data modeling Analytics and artificial intelligence Planning and evaluating requirements Operational reporting Power BI Enterprise BI Pre-built insights and custom AI Power Apps Architecture Discuss options for apps and how to choose where to start Discuss app composition options Using components as part of your app architecture Considerations for including Portals as an app in your architecture Group exercise - Power Apps Architecture topics Application Lifecycle Management (ALM) Microsoft vision and Solution Architect's role in ALM Environment strategies Defning a solution structure for your deliverable Power Automate Architecture Discuss options for automation and custom logic Review considerations for using triggers and common actions Explore using Business Process Flows (BPF) to guide users through business processes Group Exercise - Evaluate scenarios for Power Automate usage Security Modeling Solution Architect's role in security modeling Discovery and learning your client's environment Controlling access to environments and resources Controlling access to CDS Data Group Exercise - Security Modeling Integration Solution Architects role in Integrations What is an integration and why do we need it Platform features that enable integration CDS Event Publishing Scenarios for group discussion Dynamics 365 Applications Architecture Solution Architect's role when deploying Dynamics 365 apps Architecture Considerations for primary apps Group Exercise - App specific working groups evaluate requirements Power Virtual Agents architecture Introduction Chatbot options Chatbot concepts Best practices Integrate chatbots Power Virtual Agents in Microsoft Teams Robotic Process Automation Introduction Power Automate Desktop Recording and editing tasks Running desktop flows Process advisor Testing and Go Live Solution Architect's role with testing and go live Planning for testing Planning for go live

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