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Duration 5 Days 30 CPD hours This course is intended for To fully benefit from this course, you should have three to five years of experience designing and implementing applications that are built on top of Cisco platforms. This course is appropriate for: Network engineers expanding their skill-base to include software and automation Developers expanding expertise in automation and DevOps Solution architects moving to the Cisco ecosystem Infrastructure developers designing hardened production environments The job roles best suited to the material in this course are: Senior network automation engineer Senior software developer Senior system integration programmer Additional job roles that could find this course useful are: Senior infrastructure architect Senior network designer Senior test development engineer Students preparing for Cisco Certified DevNet Professional and Cisco Certified DevNet Specialist - Core certification will also find this material useful. Overview After taking this course, you should be able to: Describe the architectural traits and patterns that improve application maintainability Describe the architectural traits and patterns that improve application serviceability Identify steps to design and build a ChatOps application Implement robust Representational State Transfer (REST) API integrations with network error handling, pagination, and error flow control Describe the necessary steps for securing user and system data in applications Describe the necessary steps for securing applications Identify common tasks in automated application release process Describe best practices for application deployment Describe methodologies for designing distributed systems Describe the concepts of infrastructure configuration management and device automation Utilize Yet Another Next Generation (YANG) data models to describe network configurations and telemetry Compare various relational and nonrelational database types and how to select the appropriate type based on requirements In this course, you will learn how to implement network applications using Cisco© platforms as a base, from initial software design to diverse system integration, as well as testing and deployment automation. The course gives you hands-on experience solving real world problems using Cisco Application Programming Interfaces (APIs) and modern development tools. This course helps you prepare for Cisco DevNet Professional certification and for professional-level network automation engineer roles. COURSE OUTLINE DESIGNING FOR MAINTAINABILITY (SELF-STUDY) DESIGNING FOR SERVICEABILITY (SELF-STUDY) IMPLEMENTING CHATOPS APPLICATION DESCRIBING ADVANCED REST API INTEGRATION SECURING APPLICATION DATA (SELF-STUDY) SECURING WEB AND MOBILE APPLICATIONS (SELF-STUDY) AUTOMATING APPLICATION-RELEASE DEPLOYING APPLICATIONS UNDERSTANDING DISTRIBUTED SYSTEMS ORCHESTRATING NETWORK AND INFRASTRUCTURE MODELING DATA WITH YANG USING RELATIONAL AND NON-RELATIONAL DATABASES (SELF-STUDY) PLEASE NOTE:This class includes lecture sections and self-study sections. In instructor-led classes, lectures are delivered in real-time, either in person or via video conferencing. In e-learning courses, the lectures are on recorded videos. In both versions, you will need to review self-study sections on your own before taking the certification exam. Additional course details: Nexus Humans Cisco Developing Applications Using Cisco Core Platforms and APIs v1.0 (DEVCOR) 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 Cisco Developing Applications Using Cisco Core Platforms and APIs v1.0 (DEVCOR) 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 intended for: Developers Solutions Architects Data Engineers Anyone with little to no experience with ML and wants to learn about the ML pipeline using Amazon SageMaker Overview In this course, you will learn to: Select and justify the appropriate ML approach for a given business problem Use the ML pipeline to solve a specific business problem Train, evaluate, deploy, and tune an ML model using Amazon SageMaker Describe some of the best practices for designing scalable, cost-optimized, and secure ML pipelines in AWS Apply machine learning to a real-life business problem after the course is complete This course explores how to use the machine learning (ML) pipeline to solve a real business problem in a project-based learning environment. Students will learn about each phase of the pipeline from instructor presentations and demonstrations and then apply that knowledge to complete a project solving one of three business problems: fraud detection, recommendation engines, or flight delays. By the end of the course, students will have successfully built, trained, evaluated, tuned, and deployed an ML model using Amazon SageMaker that solves their selected business problem. Module 0: Introduction Pre-assessment Module 1: Introduction to Machine Learning and the ML Pipeline Overview of machine learning, including use cases, types of machine learning, and key concepts Overview of the ML pipeline Introduction to course projects and approach Module 2: Introduction to Amazon SageMaker Introduction to Amazon SageMaker Demo: Amazon SageMaker and Jupyter notebooks Hands-on: Amazon SageMaker and Jupyter notebooks Module 3: Problem Formulation Overview of problem formulation and deciding if ML is the right solution Converting a business problem into an ML problem Demo: Amazon SageMaker Ground Truth Hands-on: Amazon SageMaker Ground Truth Practice problem formulation Formulate problems for projects Module 4: Preprocessing Overview of data collection and integration, and techniques for data preprocessing and visualization Practice preprocessing Preprocess project data Class discussion about projects Module 5: Model Training Choosing the right algorithm Formatting and splitting your data for training Loss functions and gradient descent for improving your model Demo: Create a training job in Amazon SageMaker Module 6: Model Evaluation How to evaluate classification models How to evaluate regression models Practice model training and evaluation Train and evaluate project models Initial project presentations Module 7: Feature Engineering and Model Tuning Feature extraction, selection, creation, and transformation Hyperparameter tuning Demo: SageMaker hyperparameter optimization Practice feature engineering and model tuning Apply feature engineering and model tuning to projects Final project presentations Module 8: Deployment How to deploy, inference, and monitor your model on Amazon SageMaker Deploying ML at the edge Demo: Creating an Amazon SageMaker endpoint Post-assessment Course wrap-up Additional course details: Nexus Humans The Machine Learning Pipeline on AWS 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 The Machine Learning Pipeline on AWS 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 geared for attendees with Intermediate IT skills who wish to learn Computer Vision with tensor flow 2 Overview This 'skills-centric' course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course. Working in a hands-on learning environment, led by our Computer Vision expert instructor, students will learn about and explore how to Build, train, and serve your own deep neural networks with TensorFlow 2 and Keras Apply modern solutions to a wide range of applications such as object detection and video analysis Run your models on mobile devices and web pages and improve their performance. Create your own neural networks from scratch Classify images with modern architectures including Inception and ResNet Detect and segment objects in images with YOLO, Mask R-CNN, and U-Net Tackle problems faced when developing self-driving cars and facial emotion recognition systems Boost your application's performance with transfer learning, GANs, and domain adaptation Use recurrent neural networks (RNNs) for video analysis Optimize and deploy your networks on mobile devices and in the browser Computer vision solutions are becoming increasingly common, making their way into fields such as health, automobile, social media, and robotics. Hands-On Computervision with TensorFlow 2 is a hands-on course that thoroughly explores TensorFlow 2, the brand-new version of Google's open source framework for machine learning. You will understand how to benefit from using convolutional neural networks (CNNs) for visual tasks. This course begins with the fundamentals of computer vision and deep learning, teaching you how to build a neural network from scratch. You will discover the features that have made TensorFlow the most widely used AI library, along with its intuitive Keras interface. You'll then move on to building, training, and deploying CNNs efficiently. Complete with concrete code examples, the course demonstrates how to classify images with modern solutions, such as Inception and ResNet, and extract specific content using You Only Look Once (YOLO), Mask R-CNN, and U-Net. You will also build generative adversarial networks (GANs) and variational autoencoders (VAEs) to create and edit images, and long short-term memory networks (LSTMs) to analyze videos. In the process, you will acquire advanced insights into transfer learning, data augmentation, domain adaptation, and mobile and web deployment, among other key concepts. Computer Vision and Neural Networks Computer Vision and Neural Networks Technical requirements Computer vision in the wild A brief history of computer vision Getting started with neural networks TensorFlow Basics and Training a Model TensorFlow Basics and Training a Model Technical requirements Getting started with TensorFlow 2 and Keras TensorFlow 2 and Keras in detail The TensorFlow ecosystem Modern Neural Networks Modern Neural Networks Technical requirements Discovering convolutional neural networks Refining the training process Influential Classification Tools Influential Classification Tools Technical requirements Understanding advanced CNN architectures Leveraging transfer learning Object Detection Models Object Detection Models Technical requirements Introducing object detection A fast object detection algorithm ? YOLO Faster R-CNN ? a powerful object detection model Enhancing and Segmenting Images Enhancing and Segmenting Images Technical requirements Transforming images with encoders-decoders Understanding semantic segmentation Training on Complex and Scarce Datasets Training on Complex and Scarce Datasets Technical requirements Efficient data serving How to deal with data scarcity Video and Recurrent Neural Networks Video and Recurrent Neural Networks Technical requirements Introducing RNNs Classifying videos Optimizing Models and Deploying on Mobile Devices Optimizing Models and Deploying on Mobile Devices Technical requirements Optimizing computational and disk footprints On-device machine learning Example app ? recognizing facial expressions

Duration 3 Days 18 CPD hours This course is intended for This course is geared for Python experienced developers, analysts or others who are intending to learn the tools and techniques required in building various kinds of powerful recommendation systems (collaborative, knowledge and content based) and deploying them to the web. Overview Working in a hands-on lab environment led by our expert instructor, attendees will Understand the different kinds of recommender systems Master data-wrangling techniques using the pandas library Building an IMDB Top 250 Clone Build a content-based engine to recommend movies based on real movie metadata Employ data-mining techniques used in building recommenders Build industry-standard collaborative filters using powerful algorithms Building Hybrid Recommenders that incorporate content based and collaborative filtering Recommendation systems are at the heart of almost every internet business today; from Facebook to Net?ix to Amazon. Providing good recommendations, whether its friends, movies, or groceries, goes a long way in defining user experience and enticing your customers to use your platform.This course shows you how to do just that. You will learn about the different kinds of recommenders used in the industry and see how to build them from scratch using Python. No need to wade through tons of machine learning theory?you will get started with building and learning about recommenders as quickly as possible. In this course, you will build an IMDB Top 250 clone, a content-based engine that works on movie metadata. You will also use collaborative filters to make use of customer behavior data, and a Hybrid Recommender that incorporates content based and collaborative filtering techniques.Students will learn to build industry-standard recommender systems, leveraging basic Python syntax skills. This is an applied course, so machine learning theory is only used to highlight how to build recommenders in this course.This skills-focused ccombines engaging lecture, demos, group activities and discussions with machine-based student labs and exercises.. Our engaging instructors and mentors are highly-experienced practitioners who bring years of current, modern 'on-the-job' modern applied datascience, AI and machine learning experience into every classroom and hands-on project. Getting Started with Recommender Systems Technical requirements What is a recommender system? Types of recommender systems Manipulating Data with the Pandas Library Technical requirements Setting up the environment The Pandas library The Pandas DataFrame The Pandas Series Building an IMDB Top 250 Clone with Pandas Technical requirements The simple recommender The knowledge-based recommender Building Content-Based Recommenders Technical requirements Exporting the clean DataFrame Document vectors The cosine similarity score Plot description-based recommender Metadata-based recommender Suggestions for improvements Getting Started with Data Mining Techniques Problem statement Similarity measures Clustering Dimensionality reduction Supervised learning Evaluation metrics Building Collaborative Filters Technical requirements The framework User-based collaborative filtering Item-based collaborative filtering Model-based approaches Hybrid Recommenders Technical requirements Introduction Case study and final project ? Building a hybrid model Additional course details: Nexus Humans Applied AI: Building Recommendation Systems with Python (TTAI2360) 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 Applied AI: Building Recommendation Systems with Python (TTAI2360) 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 3 Days 18 CPD hours This course is intended for This course is geared for Python experienced developers, analysts or others who are intending to learn the tools and techniques required in building various kinds of powerful recommendation systems (collaborative, knowledge and content based) and deploying them to the web. Overview This skills-focused combines engaging lecture, demos, group activities and discussions with machine-based student labs and exercises.. Our engaging instructors and mentors are highly-experienced practitioners who bring years of current, modern 'on-the-job' modern applied datascience, AI and machine learning experience into every classroom and hands-on project. Working in a hands-on lab environment led by our expert instructor, attendees will Understand the different kinds of recommender systems Master data-wrangling techniques using the pandas library Building an IMDB Top 250 Clone Build a content-based engine to recommend movies based on real movie metadata Employ data-mining techniques used in building recommenders Build industry-standard collaborative filters using powerful algorithms Building Hybrid Recommenders that incorporate content based and collaborative filtering Recommendation systems are at the heart of almost every internet business today; from Facebook to Net?ix to Amazon. Providing good recommendations, whether its friends, movies, or groceries, goes a long way in defining user experience and enticing your customers to use your platform.This course shows you how to do just that. You will learn about the different kinds of recommenders used in the industry and see how to build them from scratch using Python. No need to wade through tons of machine learning theory?you will get started with building and learning about recommenders as quickly as possible. In this course, you will build an IMDB Top 250 clone, a content-based engine that works on movie metadata. You will also use collaborative filters to make use of customer behavior data, and a Hybrid Recommender that incorporates content based and collaborative filtering techniques. Students will learn to build industry-standard recommender systems, leveraging basic Python syntax skills. This is an applied course, so machine learning theory is only used to highlight how to build recommenders in this course. Getting Started with Recommender Systems Technical requirements What is a recommender system? Types of recommender systems Manipulating Data with the Pandas Library Technical requirements Setting up the environment The Pandas library The Pandas DataFrame The Pandas Series Building an IMDB Top 250 Clone with Pandas Technical requirements The simple recommender The knowledge-based recommender Building Content-Based Recommenders Technical requirements Exporting the clean DataFrame Document vectors The cosine similarity score Plot description-based recommender Metadata-based recommender Suggestions for improvements Getting Started with Data Mining Techniques Problem statement Similarity measures Clustering Dimensionality reduction Supervised learning Evaluation metrics Building Collaborative Filters Technical requirements The framework User-based collaborative filtering Item-based collaborative filtering Model-based approaches Hybrid Recommenders Technical requirements Introduction Case study and final project ? Building a hybrid model Additional course details: Nexus Humans Building Recommendation Systems with Python (TTAI2360) 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 Building Recommendation Systems with Python (TTAI2360) 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.

This half-day workshop delivered face-to-face or online is designed for anyone in your organisation that wants to become a Neurodiversity Champion - someone who wants to educate and change the way that Neurodiversity is viewed in the workplace.

Duration 3 Days 18 CPD hours This course is intended for The primary audience for this course is as follows: Cisco Partners and customers interested in the Catalyst 9800 wireless controller Overview Upon completing this course, the learner will be able to meet these overall objectives: Describe Cisco Catalyst 9800 Understand the Benefits for Catalyst 9800 Configure Catalyst 9800 Migrate to the Catalyst 9800 Troubleshoot the Catalyst 9800 Understand and Discuss WIFI6 Configuring Cisco Catalyst 9800 and Intro to WIFI6 v1.0 is a three-day course designed to help students understand how the Catalyst 9800 Series wireless controllers combine the best of RF excellence with IOS XE benefits. This course begins with a description of the Cisco Catalyst 9800 and its benefits while introducing the learner to WIFI6. The configuration, migration, and troubleshooting will also be covered in this instructor-led course. Introduction Cisco Catalyst 9800 Overview Intent Based Networking (IBN) Cisco Catalyst Next Gen Wireless Architecture Cisco Catalyst 9800 Wireless ? Platform Support Cisco Catalyst 9800 Wireless Controller Appliances Cisco Catalyst 9800 Wireless Controller Series: C9800-80-K9 Cisco Catalyst 9800 Wireless Controller Series: C9800-40-K9 Cisco Catalyst 9800 Wireless Controller Cloud Series: C9800-CL-K9 Cisco Catalyst 9800 use in Private and Public Cloud Environments Private Public Hybrid What is WiFi 6 and Why do we need it? Use Cases ? how WiFi 6 will change Buisness and Industry WIFI6 technical ? a leap from previous WiFi technologies Design Considerations Cisco WiFi6 Portfolio and Interoperability Configure WiFi6 on Cat 9800 Cisco Catalyst 9800 Series Embedded Controller for SDA SD-Access Everywhere Wireless Assurance with DNA Center Catalyst 9800 SD-Access Wireless Catalyst 9800 SD-Access Embedded Wireless Controllers High Availability Reducing downtime for Upgrades and Unplanned Events High Availability (Client SSO) High Availability (AP & Client SSO) Software Updates Software Updates SSO Patching Rolling Upgrades Wireless Controller SMU Rolling AP Update Image Upgrade Programmability and Telemetry Flexible management options with Cisco Catalyst 9800 Wireless Controllers Wireless Programmability ?Stack? Config vs Operational YANG data models Model Driven Telemetry Security and Threat Detection Intent-based wireless networks Security Security and Threat Mitigation Catalyst 9800 Wireless Controller Configuration Model New Configuration Model AireOS vs. Catalyst 9800 Config Model Catalyst 9800 Config Model Wireless Basic Setup Wireless Basic Configuration Model Adding Local Site Adding Remote Site Provisioning APs to Site Day 0 AP PnP Wireless Advanced Guided UI Configuration Workflow WLAN Profile Policy Profile AP Join profile RF Profile Static and Rule-Based AP Tagging Migration AireOS Config Translator Using the Tool Migration using Prime AireOS Config Translator on PI 3.5 Troubleshooting IOS-XE logging architecture Packet tracing and packet captures Embedded Packet Capture web interface Useful commands and tools Additional course details: Nexus Humans Cisco Configuring Cisco Catalyst 9800 and Intro to WIFI6 (C98WF6) 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 Cisco Configuring Cisco Catalyst 9800 and Intro to WIFI6 (C98WF6) 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 2 Days 12 CPD hours Overview This skills-focused course combines expert instructor-led discussions with practical hands-on labs that emphasize useful, current techniques, best practices and standards. Working in this hands-on lab environment, guided by our expert practitioner, you'll learn about and explore: Review of the File System Introduction to Shells: sh, bash, and ksh Shell Programming Advanced Shell Features Text Manipulation Utilities File Processing Utilities Multitasking and Batch Processing Regular Expressions Intermediate Linux: Shell, Bash, Text Manipulation, Multitasking & More is a two-day course designed to provide you with hands on experience using standard Linux commands and utilities used for day-to-day tasks including file manipulation, program execution and control, and effective use of the shell and desktop environments. Throughout the course you?ll explore key concepts to Linux core functionality, while learning the system's most commonly used commands. You?ll also learn the Bourne shell, Bash shell and Korn shell programming techniques you?ll need to read and modify existing shell scripts, and create your own. Data manipulation utilities and shell syntax for synthesizing command pipelines are also emphasized throughout the course. Review of the File System File System Organization File Types File and Directory Naming Rules and Conventions Commands for Navigating the File System Introduction to Inodes Ownership, Permissions, and Dates Manipulating Files and Links Manipulating Directories Determining Disk Usage Other File System Utilities Introduction to Shells: sh, bash, and ksh Shell Functions I/O Redirection and Pipes Command Separation and Grouping Background Execution Filename Expansion Shell Variables Command Substitution Quoting and Escaping Metacharacters Bash Shell Features Korn Shell Features Command Execution Startup Files Customizing the User Environment Shell Programming Shell Script Features and Capabilities Creating and Running a Script Working With Variables Environment Variables Working With Data Types Formatting Base Conversion Setting Special Attributes Input/Output Techniques Conditional Constructs if/then else/elif Looping Constructs for, while, until Math Operators Advanced Shell Features Manipulating Strings Writing and Calling Functions Controlling Process Priorities Interpreting Command Line Arguments Making Scripts Interactive Special Shell Variables Advanced I/O with Streams Improving Performance of Scripts Text Manipulation Utilities Editing a File from a Script Scripting with ed or sed UNIX and Linux Utilities to Manipulate Files Regular Expressions grep and egrep The Stream Editor sed Sorting in Scripts Generating Reports with awk Splitting Large Files Counting Words, Lines, and Characters Transforming File Contents File Processing Utilities Examining and Comparing Files Reporting Differences Between Files Comparing Files of Any Format Displaying Data in Octal and Hex Compressing Data Converting File Formats Extracting Text Strings Multitasking and Batch Processing Multitasking Scheduled Execution Using cron The at and batch Commands Regular Expressions Regular Expression Overview Regular Expression Implementations Regular Expressions RE Character Classes Regex Quantifiers RE Parenthesis Additional course details: Nexus Humans Intermediate Linux (TTLX2104) 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 Intermediate Linux (TTLX2104) 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 The course content is aimed at operators of devices running the Junos OS in a NOC environment. These operators include network engineers, administrators, support personnel, and reseller support personnel. Overview After successfully completing this course, you should be able to: Reduce the time it takes to identify and isolate the root cause of an issue impacting your network. Gain familiarity with Junos products as they pertain to troubleshooting. Become familiar with online resources valuable to Junos troubleshooting. Gain familiarity with Junos tools used in troubleshooting. Identify and isolate hardware issues. Troubleshoot problems with the control plane. Troubleshoot problems with interfaces and other data plane components. Describe the staging and acceptance methodology. Troubleshoot routing protocols. Describe how to monitor your network with SNMP, RMON, Junos Traffic Vision (formerly known as JFlow), and port mirroring. Become familiar with JTAC procedures. This instructor-led four-day course is designed to provide introductory troubleshooting skills for engineers in a network operations center (NOC) environment. Chapter 1: Course Introduction Course Introduction Chapter 2: Troubleshooting as a Process Before You Begin The Troubleshooting Process Challenging Network Issues The Troubleshooting Process Lab Chapter 3: Junos Product Families The Junos OS Control Plane and Data Plane Field-Replaceable Units Junos Product Families Lab: Identifying Hardware Components Chapter 4: Troubleshooting Toolkit Troubleshooting Tools Best Practices Lab: Using Monitoring Tools and Establishing a Baseline Chapter 5: Hardware and Environmental Conditions Hardware Troubleshooting Overview Memory and Storage Boot Monitoring Hardware-Related System Logs Chassis and Environmental Monitoring Lab: Monitoring Hardware and Environmental Conditions Chapter 6: Control Plane Control Plane Review System and User Processes Monitoring Routing Tables and Protocols Monitoring Bridging Monitoring the Address Resolution Protocol Lab: Control Plane Monitoring and Troubleshooting Chapter 7: Control Plane Protection Protection Overview DDOS Protection Loopback Filter Lab: Control Plane Monitoring and Troubleshooting Chapter 8: Data Plane ? Interfaces Interface Properties General Interface Troubleshooting Ethernet Interface Troubleshooting Lab: Monitoring and Troubleshooting Ethernet Interfaces Chapter 9: Data Plane ? Other Components Definition of a Data Plane Problem Data Plane Components Data Plane Forwarding Load-Balancing Behavior Firewall Filters and Policers Data Plane Troubleshooting Case Study Lab: Isolate and Troubleshoot PFE Issues Chapter 10: Staging and Acceptance Testing Initial Inspection and Power-on General System Checks Interface Testing Chapter 11: Troubleshooting Routing Protocols Troubleshooting OSPF Troubleshooting BGP Troubleshooting Routing Loops and Route Oscillation Lab: Troubleshooting Routing Protocols Chapter 12: High Availability High Availability Overview Graceful Routing Engine Switchover Graceful Restart Nonstop Active Routing and Bridging Unified In-Service Software Upgrade Chapter 13: Network Monitoring SNMP RMON Telemetry Flow Monitoring Lab: Monitoring the Network Chapter 14: vMX Troubleshooting vMX Overview Troubleshooting Lab: Monitoring vMX Chapter 15: JTAC Procedures Opening a Support Case Customer Support Tools The Content of a PR Transferring Files to JTAC Chapter 16: Automated Support and Prevention Overview Service Now Service Insight Lab: Automated Support and Prevention Additional course details: Nexus Humans JTNOC - Junos Troubleshooting in the NOC 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 JTNOC - Junos Troubleshooting in the NOC 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.

Our intensive singing courses are greatly enjoyed by adults who are eager to boost their vocal abilities within a short period. You can either attend to work on a specific aspect of your voice or your overall vocal technique. Expect tremendous progress in a few days of focused studies.