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Duration 5 Days 30 CPD hours This course is intended for This course is designed for experienced integration specialists and senior-level developers with experience in application development, messaging middleware applications, and transport protocols such as HTTP and FTP. Overview Describe the features and uses of IBM App Connect Enterprise Develop, deploy, and test message flow applications Generate message flow applications from predefined patterns Use the IBM App Connect Enterprise Toolkit problem determination aids to diagnose and solve development and runtime errors Describe the function and appropriate use of IBM App Connect Enterprise processing nodes Write basic Extended Structured Query Language and Java programs to transform data Use the IBM Graphical Data Mapping editor to transform data Define, use, and test simple XML and Data Format Description Language (DFDL) data models Describe supported transport protocols and how to call them in message flows IBM App Connect Enterprise provides connectivity and universal data transformation in heterogeneous IT environments. It enables businesses of any size to eliminate point-to-point connections and batch processing, regardless of operating system, protocol, and data format. This course teaches you how to use IBM App Connect Enterprise to develop, deploy, and support message flow applications. These applications use various messaging topologies to transport messages between service requesters and service providers, and allow the messages to be routed, transformed, and enriched during processing. In this course, you learn how to construct applications to transport and transform data. The course explores how to control the flow of data by using various processing nodes, and how to use databases and maps to transform and enrich data during processing. You also learn how to construct data models by using the Data Format Description Language (DFDL) Course Outline Introduction to IBM App Connect Enterprise Application development fundamentals Exercise: Importing and testing a message flow Creating message flow applications Exercise: Creating a message flow application Connecting to IBM MQ Exercise: Connecting to IBM MQ Controlling the flow of messages Exercise: Adding flow control to a message flow application Modeling the data Exercise: Creating a DFDL model Processing file data Exercise: Processing file data Using problem determination tools and help resources Exercise: Using problem determination tools Exercise: Implementing explicit error handling Mapping messages with the Graphical Data Mapping editor Referencing a database in a message flow application Exercise: Referencing a database in a map Using Compute nodes to transform messages Exercise: Transforming data by using the Compute and JavaCompute nodes Processing JMS, HTTP, and web service messages Preparing for production Exercise: Creating a runtime-aware message flow Additional course details: Nexus Humans WM668G IBM App Connect Enterprise V11 Application Development 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 WM668G IBM App Connect Enterprise V11 Application Development 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 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.

Duration 2 Days 12 CPD hours This course is intended for Data Protection Officers Data Protection Managers Auditors Legal Compliance Officers Security Manager Information Managers Anyone involved with data protection processes and programs Overview It will show the world that students know privacy laws and regulations and how to apply them, and that students know how to secure your place in the information economy. When students earn a CIPP credential, it means they've gained a foundational understanding of broad global concepts of privacy and data protection law and practice, including: jurisdictional laws, regulations and enforcement models; essential privacy concepts and principals; legal requirements for handling and transferring data and more. The Certified Information Privacy Professional/United States (CIPP/US) program, developed by the International Association of Privacy Professionals (IAPP) - the world?s largest comprehensive global information privacy community and resource, was the first professional certification ever to be offered in information privacy. The CIPP/US credential demonstrates a strong foundation in U.S. privacy laws and regulations and understanding of the legal requirements for the responsible transfer of sensitive personal data to/from the U.S., the EU and other jurisdictions.This course will provide you with a foundational understanding of broad global concepts of privacy and data protection law and practice, including: jurisdictional laws, regulations and enforcement models; essential privacy concepts and principals; legal requirements for handling and transferring data and more. Introduction to privacy Modern history of privacy Introduction to personal information Overview of data protection roles Summary of modern privacy frameworks Structure of U.S. law Structure and sources of U.S. law and relevant terms Governmental bodies having privacy and information security authority General Data Protection Regulation overview (GDPR) High-level overview of the GDPR Significance of the GDPR to U.S. organizations Roles and responsibilities outlined in the law California Consumer Privacy Act of 2018 (CCPA) High-level overview of the newly passed California Consumer Privacy Act of 2018 Scope Consumer rights Business obligations Enforcement Enforcement of U.S. privacy and security laws Distinguishing between criminal and civil liability Comparing federal and state authority Theories of legal liability Enforcement powers and responsibilities of government bodies, such as the FTC and state attorneys general Information management from a U.S. perspective Developing a privacy program Role of privacy professionals and accountability Employee training User preferences Managing vendors Data classification Federal versus state authority Differences between federal and state authority Preemption Healthcare Privacy laws in healthcare Major components of HIPAA Development of HITECH Privacy protections mandated by other significant healthcare laws Financial privacy Goals of financial privacy laws Key concepts of FCRA, FACTA and GLBA Red Flags Rule, Dodd-Frank and consumer protection laws Education Privacy rights and protections under FERPA Recent amendments provided by PPRA and NCLBA Telecommunications and marketing Rules and regulations of telecommunications entities Laws that govern marketing Addressing privacy in the digital advertising Law enforcement and privacy Privacy laws on intercepting communication Telecommunications industry and law enforcement Laws ensuring rights to financial privacy National security and privacy Rules and regulations on intercepting communication Evolution of the law Collaboration of government agencies and private companies to improve cybersecurity Civil litigation and privacy Privacy issues related to litigation Electronic discovery, redaction and protective orders U.S. discovery rules versus foreign laws Legal overview of workplace privacy Federal and state laws regulating and protecting employee privacy Federal laws prohibiting discrimination Privacy before, during and after employment Lifecycle of employee privacy Background screening Employee monitoring Investigating misconduct and termination Antidiscrimination laws ?Bring your own device? policies State data security laws State laws impacting data security Social Security number use regulation Laws governing data destruction Data breach notification laws Scope of state data breach notification law Nine elements of state data breach notification laws Major differences in state laws

Duration 1 Days 6 CPD hours This course is intended for The audience for this course includes professionals who are new to Looker who are interested in leveraging Looker for data analysis, visualization, and reporting. The course is designed for individuals seeking to gain a comprehensive understanding of Looker's functionalities and apply these skills in their organizations to drive data-driven decision-making. Overview Working in a hands-on learning environment led by our expert facilitator, you'll explore and gain: Solid foundation in Looker's platform: Acquire a comprehensive understanding of Looker's key features, functionality, and interface, enabling you to effectively utilize the platform for your data analysis and visualization needs. Proficiency in LookML and data modeling: Develop essential skills in Looker's unique data modeling language, LookML, to create efficient and customized data models tailored to your organization's specific requirements. Expertise in creating Explores: Learn how to build, customize, and save Explores with dimensions, measures, filters, and calculated fields, empowering you to analyze your data and uncover valuable insights in a short amount of time. Mastery of dashboard design and visualization: Gain the skills to design visually appealing and informative dashboards, create various types of visualizations, and customize them to effectively communicate your data story. Improved content organization with folders and boards: Understand how to effectively use folders and boards in Looker to organize, manage, and discover content, making your data insights easily accessible for you and your team. Looker Basics: Quick Start to Analyzing and Visualizing Data using Looker is a one day, hands-on course designed to equip professionals from a variety of backgrounds with the knowledge and skills needed to harness the full potential of their data using Looker's powerful platform. With the guidance of our expert trainers, you will gain a basic understanding of Looker's features, enabling you to create visually engaging, interactive, and insightful reports and dashboards to drive informed decision-making. Throughout this interactive workshop, you will explore Looker's key functionalities, including connecting to data sources, mastering LookML, building custom Explores, and designing captivating dashboards. With about 40% of the course dedicated to hands-on labs and a guided project, you will have ample opportunity to apply the skills you've learned in real world scenarios. Don't miss this opportunity to elevate your data analysis and visualization capabilities, enhance your professional skill set, and unlock the power of data-driven decision making. Getting Started with Looker Overview of Looker and its key features Navigating the Looker interface Connecting to Data Sources and LookML Basics Setting up and managing data connections Exploring database schemas Understanding LookML: Looker's data modeling language Creating and Customizing Explores Building and customizing Explores Adding dimensions, measures, and filters Creating calculated fields Data Visualization and Dashboard Design Creating visualizations using Looker's visualization library Customizing chart types, colors, and labels Displaying visualizations in dashboards Organizing Content with Folders and Boards Introduction to folders and boards in Looker Creating and managing folders for organizing content Setting up boards for easy content discovery Hands-on Workshop and Project Participants work on a guided project to apply the skills learned Wrap-up and Q&A Additional course details: Nexus Humans Looker Basics: Quick Start to Analyzing and Visualizing Data using Looker (TTDVLK01) 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 Looker Basics: Quick Start to Analyzing and Visualizing Data using Looker (TTDVLK01) 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 5 Days 30 CPD hours This course is intended for This course is designed for application developers. Overview Describe the benefits of implementing a decision management solution with Operational Decision Manager.Identify the key user roles that are involved in designing and developing a decision management solution, and the tasks that are associated with each role.Describe the development process of building a business rule application and the collaboration between business and development teams.Set up and customize the Business Object Model (BOM) and vocabulary for rule authoring. Implement the Execution Object Model (XOM) that enables rule execution.Orchestrate rule execution through ruleflows. Author rule artifacts to implement business policies.Debug business rule applications to ensure that the implemented business logic is error-free.Set up and customize testing and simulation for business users.Package and deploy decision services to test and production environments.Integrate decision services for managed execution within an enterprise environment.Monitor and audit execution of decision services.Work with Operational Decision Manager features that support decision governance. This course introduces developers to IBM Operational Decision Manager V8.9.2. It teaches participants the concepts and skills required to design, develop, and integrate a business rule solution with Operational Decision Manager. This course begins with an overview of Operational Decision Manager, which is composed of two main environments: Decision Server for technical users and Decision Center for business users. The course outlines the collaboration between development and business teams during project development. Through instructor-led presentations and hands-on lab exercises, participants learn about the core features of Decision Server, which is the primary working environment for developers. Participants design decision services and work with the object models that are required to author and execute rule artifacts. Participants gain experience with deployment and execution, and work extensively with Rule Execution Server. In addition, students become familiar with rule authoring so that you can support business users to set up and customize the rule authoring and validation environments. Participants also learn how to use Operational Decision Manager features to support decision governance. Introducing IBM Operational Decision Manager Exercise: Operational Decision Manager in action Developing decision services Exercise: Setting up decision services Programming with business rules and developing object models Exercise: Working with the BOM Exercise: Refactoring Orchestrating ruleset execution Exercise: Working with ruleflows Authoring rules Exercise: Exploring action rules Exercise: Authoring action rules Exercise: Authoring decision tables Customizing rule vocabulary with categories and domains Exercise: Working with static domains Exercise: Working with dynamic domains Working with queries Exercise: Working with queries Debugging rules Exercise: Executing rules locally Exercise: Debugging a ruleset Enabling tests and simulations Exercise: Enabling rule validation Managing deployment Exercise: Managing deployment Exercise: Using Build Command to build RuleApps Executing rules with Rule Execution Server Exercise: Exploring the Rule Execution Server console Auditing and monitoring ruleset execution Exercise: Auditing ruleset execution through Decision Warehouse Working with the REST API Exercise: Executing rules as a hosted transparent decision service (HTDS) Additional course details: Nexus Humans WB402 IBM Developing Rule Solutions in IBM Operational Decision Manager V8.9.2 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 WB402 IBM Developing Rule Solutions in IBM Operational Decision Manager V8.9.2 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 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)

Duration 3 Days 18 CPD hours This course is intended for This course is designed primarily for network and software engineers who are interested in learning about automation and programmability and hold the following job roles: Network engineer Systems engineer Wireless engineer Consulting systems engineer Technical solutions architect Network administrator Wireless design engineer Network manager Sales engineer Account manager Overview Upon completing this course, the learner will be able to meet these overall objectives: Get familiar with different API styles (REST, RPC) and synchronous and asynchronous API requests Learn how to use Postman software development tool in order to test the API calls Learn how to automate repetitive tasks using Ansible automation engine Explore a Python programming language, Python libraries and Python virtual environments and learn how can they be used for automation of network configuration tasks Get introduced to GIT version control system and its common operations Learn how to leverage the various models and APIs of the Cisco IOS XE platform to perform day-zero operations, improve troubleshooting methodologies with custom tools, augment the CLI using scripts, and integrate various workflows using Ansible and Python Learn about the paradigm shift of model-driven telemetry and the building blocks of a working solution Learn how to leverage the tools and APIs to automate Cisco DNA infrastructure managed by Cisco DNA Center™ Demonstrate workflows (configuration, verification, health checking, and monitoring) using Python, Ansible, and Postman Understand Cisco SD-WAN solution components, implement a Python library that works with the Cisco SD-WAN APIs to perform configuration, inventory management, and monitoring tasks, and implement reusable Ansible roles to automate provisioning new branch sites on an existing Cisco SD-WAN infrastructure Learn how to leverage the tools and APIs to automate Cisco Meraki managed infrastructure and demonstrate workflows (configuration, verification, health checking, monitoring) using Python, Ansible, and Postman Implementing Automation for Cisco Enterprise Solutions (ENAUI) v.1.2 teaches you how to implement Cisco Enterprise automated solutions, including programming concepts, orchestration, telemetry, and automation tools. This course highlights the tools and the benefits of leveraging programmability and automation in the Cisco-powered Enterprise Campus and WAN. You will also examine platforms including IOS XE software for device-centric automation, Cisco DNA Center for the intent-based enterprise network, Cisco Software-Defined WAN, and Cisco Meraki. Their current ecosystem of APIs, software development toolkits, and relevant workflows are studied in detail together with open industry standards, tools, and APIs, such as Python, Ansible, Git, JSON/YAML, NETCONF/RESTCONF, and YANG. The course qualifies for 24 Cisco Continuing Education credits (CE) towards recertification. This course will help you:Gain high-demand skills using modern programming languages, APIs, and systems such as Python, Ansible, and Git to automate, streamline, and enhance business operationsAcquire the skills and knowledge to customize tools, methods, and processes that improve network performance and agilityPrepare for the 300-435 ENAUTO exam Course Outline Network Programmability Foundation Automating APIs and Protocols Managing Configuration with Python and Ansible Implementing On-Box Programmability and Automation with Cisco IOS XE Software Implementing Model-Driven Telemetry Day 0 Provisioning with Cisco IOS-XE Software Implementing Automation in Enterprise Networks Building Cisco DNA Center Automation with Python Automating Operations using Cisco DNA Center Introducing Cisco SD-WAN Programmability Building Cisco SD-WAN Automation with Python Building Cisco SD-WAN Automation with Ansible Automating Cisco Meraki Implementing Meraki Integration APIs Additional course details: Nexus Humans Cisco Implementing Automation for Cisco Enterprise Solutions v1.2 (ENAUI) 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 Implementing Automation for Cisco Enterprise Solutions v1.2 (ENAUI) 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 5 Days 30 CPD hours This course is intended for Channel Partner / Reseller Customer Employee Overview At course completion students will be able to: - Explain the need for QoS, describe the fundamentals of QoS policy, and identify and describe the different models that are used for ensuring QoS in a network - Explain the use of MQC and AutoQoS to implement QoS on the network and describe some of the mechanisms used to monitor QoS implementations - Given a converged network and a policy defining QoS requirements, classify and mark network traffic to implement the policy - Use Cisco QoS queuing mechanisms to manage network congestion - Use Cisco QoS congestion avoidance mechanisms to reduce the effects of congestion on the network - Use Cisco QoS traffic policing and traffic shaping mechanisms to effectively limit the rate of network traffic - Given a low speed WAN link, use Cisco link efficiency mechanisms to improve the badwidth efficiency of the link - Describe the recommended best practices and methods used for end-to-end QoS deployment in the enterprise This course provides students with knowledge of IP QoS requirements, conceptual models such as best effort, IntServ, and DiffServ, and the implementation of QoS on Cisco platforms. Introduction to QoS Review Converged Networks Understand QoS Describe Best-Effort and Integrated Services Models Describe the Differentiated Services Model Module Summary Module Self-Check Implement and Monitor QoS MQC Introduction Monitor QoS Define Campus AutoQoS Define WAN AutoQoS Module Summary Module Self-Check Lab 2-1: IP SLA Setup and QoS Baseline Measurement Lab 2-2: Configuring QoS with Cisco AutoQoS Classification and Marking Classification and Marking Overview Case Study 3-1: Classification and Marking MQC for Classification and Marking NBAR for Classification Use of QoS Preclassify Campus Classification and Marking Module Summary Module Self-Check Lab 3-1: Classification and Marking Using MQC Lab 3-2: Using NBAR for Classification Lab 3-3: Configuring QoS Preclassify Lab 3-4: Campus Classification and Marking Using MQC Congestion Management Queuing Introduction Configure WFQ Configure CBWFQ and LLQ Configure Campus Congestion Management Module Summary Module Self-Check Lab 4-1: Configuring Fair Queuing Lab 4-2: Configuring LLQ-CBWFQ Lab 4-3: Configuring Campus-Based Queuing Mechanisms Congestion Avoidance Congestion Avoidance Introduction Configure Class-Based WRED Case Study 5-1: WRED Traffic Profiles Configure ECN Describe Campus-Based Congestion Avoidance Module Summary Module Self-Check Lab 5-1: Configuring DSCP-Based WRED Lab 5-2: Configuring WTD Thresholds Traffic Policing and Shaping Traffic Policing and Shaping Overview Configure Class-Based Policing Campus Policing Configure Class-Based Shaping Configure Class-Based Shaping on Frame Relay Interfaces Configure Frame Relay Voice-Adaptive Traffic Shaping and Fragmentation Module Summary Module Self-Check Lab 6-1: Configuring Class-Based Policing Lab 6-2: Configuring Class-Based Shaping Link Efficiency Mechanisms Link Efficiency Mechanisms Overview Configure Class-Based Header Compression Configure LFI Module Summary Module Self-Check Lab 7-1: Configuring Class-Based Header Compression Lab 7-2: Configuring LFI Deploying End-to-End QoS Apply Best Practices for QoS Policy Design End-to-End QoS Deployments Module Summary Module Self-Check Lab 8-1: Mapping Enterprise QoS Policy to the Service Provider Policy Additional course details: Nexus Humans Cisco Implementing Cisco Quality of Service v2.5 (QOS) 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 Implementing Cisco Quality of Service v2.5 (QOS) 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 1 Days 6 CPD hours This course is intended for To gain the most from attending this course you should possess the following incoming skills: Basic knowledge of programming concepts and syntax in Python. Familiarity with common data formats such as CSV, JSON, and XML. Experience using command-line interfaces and basic text editing tools. Understanding of basic machine learning concepts and algorithms. Overview Working in an interactive learning environment, led by our engaging expert, you will: Gain a solid understanding of prompt engineering concepts and their applications in software development and AI-driven solutions. Master the techniques for preprocessing and cleaning text data to ensure high-quality inputs for AI models like GPT-4. Develop expertise in GPT-4 tokenization, input formatting, and controlling model behavior for various tasks and requirements. Acquire the ability to design, optimize, and test prompts effectively, catering to diverse business applications and use cases. Learn advanced prompt engineering techniques, such as conditional text generation and multi-turn conversations, to create more sophisticated AI solutions. Practice creating prompts to generate, run, and test code in a chosen programming language using GPT-4 and OpenAI Codex. Understand the ethical implications and best practices in responsible AI deployment, ensuring fair and unbiased AI applications in software development. Prompt Engineering offers coders and software developers a competitive edge by empowering them to develop more effective and efficient AI-driven solutions in their projects. By harnessing the capabilities of cutting-edge AI models like GPT-4, coders can automate repetitive tasks, enhance natural language understanding, and even generate code suggestions, boosting productivity and creativity. In addition, mastering prompt engineering can contribute to improved job security, as professionals with these in-demand skills are highly sought after in the rapidly evolving tech landscape. Quick Start to Prompt Engineering for Coders and Software Developers is a one day course designed to get you quickly up and running with the prompting skills required to out AI to work for you in your development efforts. Guided by our AI expert, you?ll explore key topics such as text preprocessing, data cleansing, GPT-4 tokenization, input formatting, prompt design, and optimization, as well as ethical considerations in prompt engineering. In the hands-on labs you?ll explore tasks such as formatting inputs for GPT-4, designing and optimizing prompts for business applications, and implementing multi-turn conversations with AI. You?ll work with innovative tools like the OpenAI API, OpenAI Codex, and OpenAI Playground, enhancing your learning experience while preparing you for integrating prompt engineering into your professional toolkit. By the end of this immersive course, you?ll have the skills necessary to effectively use prompt engineering in your software development projects. You'll be able to design, optimize, and test prompts for various business tasks, integrate GPT-4 with other software platforms, and address ethical concerns in AI deployment. Introduction to Prompt Engineering Overview of prompt engineering and its importance in AI applications Major applications of prompt engineering in business Common challenges faced in prompt engineering Overview of GPT-4 and its role in prompt engineering Key terminology and concepts in prompt engineering Getting Things Ready: Text Preprocessing and Data Cleansing Importance of data preprocessing in prompt engineering Techniques for text cleaning and normalization Tokenization and n-grams Stop word removal and stemming Regular expressions and pattern matching GPT-4 Tokenization and Input Formatting GPT-4 tokenization and its role in prompt engineering Understanding and formatting GPT-4 inputs Context windows and token limits Controlling response length and quality Techniques for handling out-of-vocabulary tokens Prompt Design and Optimization Master the skills to design, optimize, and test prompts for various business tasks. Designing effective prompts for different tasks Techniques for prompt optimization GPT-4 system and user parameters for controlling behavior Importance of prompt testing and iteration Best practices for prompt engineering in business applications Advanced Techniques and Tools in Prompt Engineering Learn advanced techniques and tools for prompt engineering and their integration in business applications. Conditional text generation with GPT-4 Techniques for handling multi-turn conversations Overview of tools for prompt engineering: OpenAI API, OpenAI Codex, and OpenAI Playground Integration of GPT-4 with other software platforms and tools Monitoring and maintaining prompt performance Code Generation and Testing with Prompt Engineering Develop the skills to generate, integrate, and test AI-generated code effectively, enhancing productivity and creativity in software development projects. Introduction to code generation with AI models like GPT-4 Designing prompts for code generation across programming languages Techniques for specifying requirements and constraints in prompts Generating and interpreting code snippets using AI-driven solutions Integrating generated code into existing projects and codebases Best practices for testing and validating AI-generated code Ethics and Responsible AI Understand the ethical implications of prompt engineering and the importance of responsible AI deployment in business. Ethical considerations in prompt engineering Bias in AI systems and its impact on prompt engineering Techniques to minimize bias and ensure fairness Best practices for responsible AI deployment in business applications Monitoring and addressing ethical concerns in prompt engineering