111 Python courses in Nottingham delivered Live Online

This course shows you how to apply various approaches and algorithms to solve business problems through AI and ML, follow a methodical workflow to develop sound solutions, use open-source, off-the-shelf tools to develop, test, and deploy those solutions, and ensure that they protect the privacy of users. This course includes hands-on activities for each topic area.

Duration 2 Days 12 CPD hours This course is intended for Experienced security administrators and security analysts who are already familiar with VMware Carbon Black Cloud Overview By the end of the course, you should be able to meet the following objectives: Describe and determine use cases for integrating with VMware Carbon Black Cloud Configure, automate, and troubleshoot the VMware Carbon Black Cloud Syslog Integration Use VMware Carbon Black Cloud APIs to pull data with Postman Install and use the VMware Carbon Black Cloud Python SDK Automate operations using the VMware Carbon Black Cloud SDK and APIs Identify and troubleshoot VMware Carbon Black Cloud sensor installations Gather troubleshooting data within the browser to remediate or escalate problems Identify and resolve sensor usage, networking, and performance problems with the VMware Carbon Black Cloud sensor This two-day, hands-on training course provides you with the advanced knowledge, skills, and tools to achieve competency in performing advanced operations and troubleshooting of VMware Carbon Black Cloud. This course will go into integrating VMware Carbon Black Cloud with other third-party components and utilizing the API and the SDK to automate operations within the product and your security stack. This course will also enable you to troubleshoot common problems during sensor installation, operations, and within the VMware Carbon Black Cloud console with hands-on lab problems. Course Introduction Introductions and course logistics Course objectives VMware Carbon Black Cloud Integrations Describe the integration capabilities with VMware Carbon Black Cloud Determine integration use cases for VMware Carbon Black Cloud Identify required components for integrating VMware Carbon Black Cloud Differentiate VMware Carbon Black Cloud integration vendors VMware Carbon Black Cloud Syslog Integration Describe the function of the Syslog Connector Generate API and SIEM keys from the Cloud console Validate a successful Syslog integration Describe how to automate the Syslog Connector Troubleshoot problems with the Syslog integration Using Postman Explain the concept and purpose of an API Interpret common REST API Status codes Recognize the difference between platform and product APIs Using the Postman Client to initiate API calls Create a custom access level and respective API key Create a valid API request Using the VMware Carbon Black Cloud Python SDK Install the VMware Carbon Black Cloud Python SDK Describe the different authentication methods Evaluate the best authentication method for a given task Automating Operations Automate basic Incident Response tasks using the VMware Carbon Black Cloud SDK and API Automate basic watchlist interactions using the VMware carbon Black Cloud SDK and API Sensor Installation Troubleshooting Describe sensor install log collection process Identify sensor install log parameters Create a detailed sensor install log Locate sensor install logs on an endpoint Interpret sensor install success from an install log Determine likely cause for install failure using sensor logs Propose resolution steps for a given sensor install failure VMware Carbon Black Cloud Console Troubleshooting Identify sensor bypass status reasons Simplify console data exports using search Describe differences in Audit Log detail levels Locate built-in browser tools Gather console diagnostics logs from a browser Review console diagnostics logs Sensor Operations Troubleshooting Identify available types of diagnostic logs Gather appropriate diagnostic logs for a given issue Identify steps for resolving software interoperability problems Identify steps for resolving resource problems Identify steps for resolving network problems Additional course details:Notes Delivery by TDSynex, Exit Certified and New Horizons an VMware Authorised Training Centre (VATC) Nexus Humans VMware Carbon Black Cloud:Advanced Operations and Troubleshooting 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 VMware Carbon Black Cloud:Advanced Operations and Troubleshooting 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 class is intended for experienced developers who are responsible for managing big data transformations including: Extracting, loading, transforming, cleaning, and validating data. Designing pipelines and architectures for data processing. Creating and maintaining machine learning and statistical models. Querying datasets, visualizing query results and creating reports Overview Design and build data processing systems on Google Cloud Platform. Leverage unstructured data using Spark and ML APIs on Cloud Dataproc. Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow. Derive business insights from extremely large datasets using Google BigQuery. Train, evaluate and predict using machine learning models using TensorFlow and Cloud ML. Enable instant insights from streaming data Get hands-on experience with designing and building data processing systems on Google Cloud. This course uses lectures, demos, and hand-on labs to show you how to design data processing systems, build end-to-end data pipelines, analyze data, and implement machine learning. This course covers structured, unstructured, and streaming data. Introduction to Data Engineering Explore the role of a data engineer. Analyze data engineering challenges. Intro to BigQuery. Data Lakes and Data Warehouses. Demo: Federated Queries with BigQuery. Transactional Databases vs Data Warehouses. Website Demo: Finding PII in your dataset with DLP API. Partner effectively with other data teams. Manage data access and governance. Build production-ready pipelines. Review GCP customer case study. Lab: Analyzing Data with BigQuery. Building a Data Lake Introduction to Data Lakes. Data Storage and ETL options on GCP. Building a Data Lake using Cloud Storage. Optional Demo: Optimizing cost with Google Cloud Storage classes and Cloud Functions. Securing Cloud Storage. Storing All Sorts of Data Types. Video Demo: Running federated queries on Parquet and ORC files in BigQuery. Cloud SQL as a relational Data Lake. Lab: Loading Taxi Data into Cloud SQL. Building a Data Warehouse The modern data warehouse. Intro to BigQuery. Demo: Query TB+ of data in seconds. Getting Started. Loading Data. Video Demo: Querying Cloud SQL from BigQuery. Lab: Loading Data into BigQuery. Exploring Schemas. Demo: Exploring BigQuery Public Datasets with SQL using INFORMATION_SCHEMA. Schema Design. Nested and Repeated Fields. Demo: Nested and repeated fields in BigQuery. Lab: Working with JSON and Array data in BigQuery. Optimizing with Partitioning and Clustering. Demo: Partitioned and Clustered Tables in BigQuery. Preview: Transforming Batch and Streaming Data. Introduction to Building Batch Data Pipelines EL, ELT, ETL. Quality considerations. How to carry out operations in BigQuery. Demo: ELT to improve data quality in BigQuery. Shortcomings. ETL to solve data quality issues. Executing Spark on Cloud Dataproc The Hadoop ecosystem. Running Hadoop on Cloud Dataproc. GCS instead of HDFS. Optimizing Dataproc. Lab: Running Apache Spark jobs on Cloud Dataproc. Serverless Data Processing with Cloud Dataflow Cloud Dataflow. Why customers value Dataflow. Dataflow Pipelines. Lab: A Simple Dataflow Pipeline (Python/Java). Lab: MapReduce in Dataflow (Python/Java). Lab: Side Inputs (Python/Java). Dataflow Templates. Dataflow SQL. Manage Data Pipelines with Cloud Data Fusion and Cloud Composer Building Batch Data Pipelines visually with Cloud Data Fusion. Components. UI Overview. Building a Pipeline. Exploring Data using Wrangler. Lab: Building and executing a pipeline graph in Cloud Data Fusion. Orchestrating work between GCP services with Cloud Composer. Apache Airflow Environment. DAGs and Operators. Workflow Scheduling. Optional Long Demo: Event-triggered Loading of data with Cloud Composer, Cloud Functions, Cloud Storage, and BigQuery. Monitoring and Logging. Lab: An Introduction to Cloud Composer. Introduction to Processing Streaming Data Processing Streaming Data. Serverless Messaging with Cloud Pub/Sub Cloud Pub/Sub. Lab: Publish Streaming Data into Pub/Sub. Cloud Dataflow Streaming Features Cloud Dataflow Streaming Features. Lab: Streaming Data Pipelines. High-Throughput BigQuery and Bigtable Streaming Features BigQuery Streaming Features. Lab: Streaming Analytics and Dashboards. Cloud Bigtable. Lab: Streaming Data Pipelines into Bigtable. Advanced BigQuery Functionality and Performance Analytic Window Functions. Using With Clauses. GIS Functions. Demo: Mapping Fastest Growing Zip Codes with BigQuery GeoViz. Performance Considerations. Lab: Optimizing your BigQuery Queries for Performance. Optional Lab: Creating Date-Partitioned Tables in BigQuery. Introduction to Analytics and AI What is AI?. From Ad-hoc Data Analysis to Data Driven Decisions. Options for ML models on GCP. Prebuilt ML model APIs for Unstructured Data Unstructured Data is Hard. ML APIs for Enriching Data. Lab: Using the Natural Language API to Classify Unstructured Text. Big Data Analytics with Cloud AI Platform Notebooks What's a Notebook. BigQuery Magic and Ties to Pandas. Lab: BigQuery in Jupyter Labs on AI Platform. Production ML Pipelines with Kubeflow Ways to do ML on GCP. Kubeflow. AI Hub. Lab: Running AI models on Kubeflow. Custom Model building with SQL in BigQuery ML BigQuery ML for Quick Model Building. Demo: Train a model with BigQuery ML to predict NYC taxi fares. Supported Models. Lab Option 1: Predict Bike Trip Duration with a Regression Model in BQML. Lab Option 2: Movie Recommendations in BigQuery ML. Custom Model building with Cloud AutoML Why Auto ML? Auto ML Vision. Auto ML NLP. Auto ML Tables.

Duration 3 Days 18 CPD hours This course is intended for Experienced system administrators or network administrators, software and DevOps engineers Overview By the end of the course, you should be able to meet the following objectives: Describe VMware NSX Advanced Load Balancer architecture Describe VMware NSX Advanced Load Balancer components and main functions Explain VMware NSX Advanced Load Balancer key features and benefits Describe and leverage VMware NSX Advanced Load Balancer REST API Describe and leverage VMware NSX Advanced Load Balancer SDKs with extended focus on Python SDK Leverage REST API and SDK features and functions to provision application delivery components Describe and leverage VMware NSX Advanced Load Balancer Ansible and Terraform integrations Describe and leverage VMware NSX Advanced Load Balancer Github, Docker (avinetworks/avitools), Ansible Galaxy and other open source resources to accelerate the automation planning and implementation Leverage VMware NSX Advanced Load Balancer Ansible and Terraform integrations to provision infrastructure components Leverage VMware NSX Advanced Load Balancer Ansible and Terraform integrations to automate and streamline application delivery services provisioning This three-day, fast-paced course provides comprehensive training on how to automate infrastructure and application components of VMware NSX Advanced Load Balancer (Avi Networks) solution. This course covers key application delivery features of NSX Advanced Load Balancer (Avi Networks) features and functionality offered in VMware NSX Advanced Load Balancer 18.2 release and focuses on how to plan and implement automation of infrastructure and application components leveraging REST API, SDK or automation solutions such as Ansible, Terraform or similar. Access to a software-defined data center environment is provided through hands-on labs to reinforce the skills and concepts presented in the course. Course Introduction Introductions and course logistics Course objectives Introduction to NSX Advanced Load Balancer Introduce NSX Advanced Load Balancer Discuss NSX Advanced Load Balancer use cases, and benefits Explain NSX Advanced Load Balancer architecture and components Explain the management, control, data, and consumption planes and functions Virtual Services Configuration Concepts Explain Virtual Service components Explain Virtual Service types Explain and configure basic virtual services components such as Application Profiles, Network Profiles,Pools and Health Monitors Pools Configuration Concepts Explain and deep dive on Pool configuration options Describe multiple load balancing algorithms Explain multiple Health Monitor types Explain multiple Persistent profiles Explain and configure Pool Groups Leveraging NSX Advanced Load Balancer REST API Explain NSX Advanced Load Balancer automation vision Explain and introduce NSX Advanced Load Balancer REST API Describe NSX Advanced Load Balancer REST API methods and capabilities Describe NSX Advanced Load Balancer REST API session handling properties such authentication, API versioning and tenancy model Deep dive on NSX Advanced Load Balancer REST API Object Model Explain and investigate NSX Advanced Load Balancer REST API leveraging browser and command line utilities Explain and interact with NSX Advanced Load Balancer REST API leveraging browser, Postman and Curl Explain Swagger-based API Documentation Explain and leverage NSX Advanced Load Balancer Inventory API Explain and leverage NSX Advanced Load Balancer methods such as GET, PUT, POST and PATCH and associated queries, filters and parameters Deep dive on NSX Advanced Load Balancer PATCH method Explain and leverage NSX Advanced Load Balancer Analytics API Explain and leverage NSX Advanced Load Balancer MACRO API NSX Advanced Load Balancer Software-Defined Kits (SDKs) and ControlScripts Introduce NSX Advanced Load Balancer SDKs Describe, install and leverage NSX Advanced Load Balancer Python SDK Deep dive on NSX Advanced Load Balancer Python SDK Describe and leverage Golang SDK Leverage NSX Advanced Load Balancer open source resources such as Github, etc to accelerate SDKs adoption Describe NSX Advanced Load Balancer Events and Alerts framework Introduce ControlScripts foundations Leverage ControlScripts to automate configuration changes and alerts remediation Automating NSX Advanced Load Balancer Application Delivery Services with Ansible and Terraform Introduce NSX Advanced Load Balancer Configuration Orchestration and Management vision Introduce and explain Ansible foundations Describe Ansible and NSX Advanced Load Balancer Ansible capabilities Deep dive and implement NSX Advanced Load Balancer Ansible Core configuration modules (avinetworks/avisdk) Deep dive and implement Ansible NSX Advanced Load Balancer Declarative configuration role (avinetworks/aviconfig) Leverage Swagger NSX Advanced Load Balancer REST API models to develop and implement Ansible playbooks Explain application delivery configuration automation approach and models Apply configuration automation models with Ansible Introduce and explain Terraform foundations Describe Terraform and NSX Advanced Load Balancer Terraform capabilities Deep dive and implement NSX Advanced Load Balancer Terraform Provider Leverage Swagger NSX Advanced Load Balancer REST API models to develop and implement Terraform plans Apply configuration automation models with Terraform Automating NSX Advanced Load Balancer Infrastructure with Ansible and Terraform Introduce NSX Advanced Load Balancer infrastructure Automation vision Describe infrastructure deployment approach and capabilities Describe Ansible and NSX Advanced Load Balancer Ansible Infrastructure deployment approach and capabilities Describe Terraform and NSX Advanced Load Balancer Terraform deployment approach and capabilities Leverage Terraform to deploy Controllers and perform system configuration, including control plane cluster setup Leverage Terraform to provision Cloud, Service Engine Groups and Service Engine components Describe and leverage Ansible roles to deploy Controllers and perform initial system configuration, including control plane cluster setup Leverage Ansible declarative and core roles to provision Cloud, Service Engine Groups and Service Engine components Describe and implement combined Terraform + Ansible model to streamline NSX Advanced Load Balancer solution deployment

Duration 3 Days 18 CPD hours This course is intended for This course is designed for Service Provider networking professionals in job roles such as: Network administrators Network architects Network designers Network engineers Network managers Network Operations Center (NOC) personnel Network supervisors Overview After taking this course, you should be able to: Use NETCONF and RESTCONF programmability protocols on Cisco devices Describe and use tools to validate YANG data models on Cisco devices Describe and configure model-driven telemetry on Cisco devices Describe and configure network traffic automation with Cisco XTC Describe and use network automation tools that utilize SSH Automate service provider network configuration with Cisco NSO Describe how to automate virtualized resources with Cisco ESC Describe how to automate service provider WAN with Cisco WAE This course prepares you to implement and support automation solutions in a Service Provider network infrastructure, using network programmability principles, protocols, tools, and mechanisms. Through a combination of lessons and hands-on labs, you will learn to deploy, configure, monitor, and operate Service Provider network environments using modern data models. These models allow you to represent operational data and new network management protocols in order to administer hundreds or thousands of devices in a single operation, replacing traditional, time-consuming, error prone, device-by-device Command Line Interface (CLI) management. The course also introduces powerful automation solutions that can streamline network operations. This course also prepares you for the CCNP-Service Provider concentration exam 300-535. Implementing Network Device Programmability Interfaces with NETCONF and RESTCONF Implement NETCONF Protocol Implement RESTCONF Protocol Implementing Model-Driven Programmability with YANG YANG Data Models YANG Tools YANG Development Kit Implementing Model-Driven Telemetry Implementing Model-Driven Telemetry with gRPC Implementing Model-Driven Telemetry with gNMI Automating Service Provider Network Traffic with Cisco XTC Cisco XTC Fundamentals Configure Cisco XTC Automating Networks with Tools That Utilize SSH Implement Device Configurations with Python Netmiko Library Implement Device Configurations with Ansible Playbooks Orchestrating Network Services with Cisco NSO Cisco NSO Fundamentals Cisco NSO Device Manager Cisco NSO Services Implement Device Configurations with Python Automating Virtualized Resources with Cisco Elastic Services Controller Cisco ESC Architecture Cisco ESC Resource Management Automating the WAN with Cisco WAE Describe the Cisco WAE Components Additional course details: Nexus Humans Cisco Implementing Automation for Cisco Service Provider Solutions v1.0 (SPAUI) 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 Service Provider Solutions v1.0 (SPAUI) 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 Overview In this course you?ll learn how to: Containerize and deploy a new Python script Configure the deployment with ConfigMaps, Secrets and SecurityContexts Understand multi-container pod design Configure probes for pod health Update and roll back an application Implement services and NetworkPolicies Use PersistentVolumeClaims for state persistence And more In this vendor agnostic course, you will use Python to build, monitor and troubleshoot scalable applications in Kubernetes. Introduction Objectives Who You Are The Linux Foundation Linux Foundation Training Preparing Your System Course Registration Labs Kubernetes Architecture What Is Kubernetes? Components of Kubernetes Challenges The Borg Heritage Kubernetes Architecture Terminology Master Node Minion (Worker) Nodes Pods Services Controllers Single IP per Pod Networking Setup CNI Network Configuration File Pod-to-Pod Communication Cloud Native Computing Foundation Resource Recommendations Labs Build Container Options Containerizing an Application Hosting a Local Repository Creating a Deployment Running Commands in a Container Multi-Container Pod readinessProbe livenessProbe Testing Labs Design Traditional Applications: Considerations Decoupled Resources Transience Flexible Framework Managing Resource Usage Multi-Container Pods Sidecar Container Adapter Container Ambassador Points to Ponder Labs Deployment Configuration Volumes Overview Introducing Volumes Volume Spec Volume Types Shared Volume Example Persistent Volumes and Claims Persistent Volume Persistent Volume Claim Dynamic Provisioning Secrets Using Secrets via Environment Variables Mounting Secrets as Volumes Portable Data with ConfigMaps Using ConfigMaps Deployment Configuration Status Scaling and Rolling Updates Deployment Rollbacks Jobs Labs Security Security Overview Accessing the API Authentication Authorization ABAC RBAC RBAC Process Overview Admission Controller Security Contexts Pod Security Policies Network Security Policies Network Security Policy Example Default Policy Example Labs Exposing Applications Service Types Services Diagram Service Update Pattern Accessing an Application with a Service Service without a Selector ClusterIP NodePort LoadBalancer ExternalName Ingress Resource Ingress Controller Labs Troubleshooting Troubleshotting Overview Basic Troubleshooting Steps Ongoing (Constant) Change Basic Troubleshooting Flow: Pods Basic Troubleshooting Flow: Node and Security Basic Troubleshooting Flow: Agents Monitoring Logging Tools Monitoring Applications System and Agent Logs Conformance Testing More Resource Labs Additional course details: Nexus Humans Kubernetes for App Developers 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 Kubernetes for App Developers 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 System installersSystem integratorsSystem administratorsNetwork administratorsSolutions designers Overview After completing this course, you should be able to:Explain transactional service activation and how it relates to business requirementsExplain the benefits and uses of Cisco NSOExplain how Cisco NSO communicates with network devicesUnderstand the NETCONF protocol and be able to read and write simple YANG modelsInstall NSO and describe how NSO uses NETCONF and the Device Manager componentUnderstand the difference between devices that are fully NETCONF capable and those that are less or not NETCONF capableExplain the YANG service model structureDescribe how YANG is used with NSO, create and deploy a service, and explain NSO FASTMAPDesign and manage services with YANG modelsPerform NSO configuration and basic troubleshooting, and describe the following NSO features: integration options, alarms and reporting, scalability and performance options, and available function packsUse logs to troubleshoot the Cisco NSO deployment and check NSO communication with network devicesExplain the mapping logic of service parameters to device models and consequently to device configurationsDescribe the use of different integration options and APIsExplain the use of Reactive FASTMAP for manipulating and implementing advanced Network Functions Virtualization (NFV) componentsDescribe the use of feature components and function packsDefine and explain the European Telecommunications Standards Institute (ETSI) Open Source NFV Management and Orchestration (MANO) principles and solutionWork with the alarm console, and understand the NSO alarm structure and how it conforms to modern network operations procedures The Cisco NSO Essentials for Programmers and Network Architects (NSO201) course introduces you to Cisco© Network Services Orchestrator (NSO). You will learn to install Cisco NSO and use it to manage devices and create services based on YANG templates with XPath. This course provides a brief overview of NSO as a network automation solution, as well as an introduction to NETCONF, YANG, and XPath. You will learn about service packages, network element drivers, and Application Programming Interfaces (APIs). The course also covers service creation, device and configuration management, NSO maintenance, NSO options and integrations, and basic NSO troubleshooting. Introduction to Cisco NSO Meeting Challenges with Orchestration Challenges of Network Management Challenges of Network Orchestration NSO Features and Benefits That Meet Challenges Standardized Approach What Is NSO? Logical Architecture Components What Does NSO Do? Orchestration Use Cases How Does NSO Work? Introduction to NETCONF and YANG Packages Mapping Logic Network Element Drivers (NEDs) Resources and Training Resources Training Get Started with Cisco NSO Installing Cisco NSO Setup Overview Cisco NSO Local Installation Installing NEDs Using NetSim NETCONF Overview Challenges of Network Management Introduction to NETCONF NETCONF Operation Device Manager Device Manager Overview Device Configuration Management Device Connection Management Templates and Groups Other Device Management Tools Service Manager Essentials YANG Overview Introduction to YANG Other Representations of YANG Data Types XPath Overview Basic YANG Statements Can You Spot the Error? Using Services Package Architecture Creating a Service Package Sample Service Configuration Service Template YANG Service Model Deploying a Service Model-to-Model Mapping Mapping Introduction Mapping Logic FASTMAP Template Processing Service Design and Cisco NSO Programmability Service Design Service Design Overview Top-Down Approach Bottom-Up Approach Device Configuration Service Model Service Management Service Management Tasks Service Lifecycle Management Guidelines NSO Programmability Introduction NSO Programmability Overview Python Service Skeleton Creating a Service YANG Model Creating a Service Template Template Processing with Python Cisco NSO Flexibility System Configuration and Troubleshooting System Configuration System Troubleshooting Integration Integration Options NETCONF Server Web Integration SNMP Agent Alarm Management and Reporting Alarm Management Reporting Scalability and Performance High Availability High-Availability Cluster Communications Clustering Layered Service Architecture Addressing Performance Limitations Components and Function Packs Function Packs NFV Orchestration Reactive FASTMAP

Duration 4 Days 24 CPD hours This course is intended for System installers System integrators System administrators Network administrators Solutions designers Overview After completing this course, you should be able to: Explain transactional service activation and how it relates to business requirements Explain the benefits and uses of Cisco NSO Explain how Cisco NSO communicates with network devices Understand the NETCONF protocol and be able to read and write simple YANG models Install NSO and describe how NSO uses NETCONF and the Device Manager component Understand the difference between devices that are fully NETCONF capable and those that are less or not NETCONF capable Explain the YANG service model structure Describe how YANG is used with NSO, create and deploy a service, and explain NSO FASTMAP Design and manage services with YANG models Perform NSO configuration and basic troubleshooting, and describe the following NSO features: integration options, alarms and reporting, scalability and performance options, and available function packs Use logs to troubleshoot the Cisco NSO deployment and check NSO communication with network devices Explain the mapping logic of service parameters to device models and consequently to device configurations Describe the use of different integration options and APIs Explain the use of Reactive FASTMAP for manipulating and implementing advanced Network Functions Virtualization (NFV) components Describe the use of feature components and function packs Define and explain the European Telecommunications Standards Institute (ETSI) Open Source NFV Management and Orchestration (MANO) principles and solution Work with the alarm console, and understand the NSO alarm structure and how it conforms to modern network operations procedures The Cisco NSO Essentials for Programmers and Network Architects (NSO201) v. 4.0 course introduces you to Cisco© Network Services Orchestrator (NSO). You will learn to install Cisco NSO and use it to manage devices and create services based on YANG templates with XPath. This course provides an overview of NSO as a network automation solution, as well as introductions to NETCONF, YANG, and XPath. You will learn about managing devices and creating device templates, service management and service package creation, network element drivers, interfacing with other systems using APIs, configuring and troubleshooting system settings, managing alarms and reporting, configuring NSO for scalability and performance, and capabilities that can be added to Cisco NSO. Introduction to Cisco NSO Meeting Challenges with Orchestration Challenges of Network Management Challenges of Network Orchestration NSO Features and Benefits That Meet Challenges Standardized Approach What Is NSO? Logical Architecture Components What Does NSO Do? Orchestration Use Cases How Does NSO Work? Introduction to NETCONF and YANG Packages Mapping Logic Network Element Drivers (NEDs) Resources and Training Resources Training Get Started with Cisco NSO Installing Cisco NSO Setup Overview Cisco NSO Local Installation Installing NEDs Using NetSim NETCONF Overview Challenges of Network Management Introduction to NETCONF NETCONF Operation Device Manager Device Manager Overview Device Configuration Management Device Connection Management Templates and Groups Other Device Management Tools Service Manager Essentials YANG Overview Introduction to YANG Other Representations of YANG Data Types XPath Overview Basic YANG Statements Can You Spot the Error? Using Services Package Architecture Creating a Service Package Sample Service Configuration Service Template YANG Service Model Deploying a Service Model-to-Model Mapping Mapping Introduction Mapping Logic FASTMAP Template Processing Service Design and Cisco NSO Programmability Service Design Service Design Overview Top-Down Approach Bottom-Up Approach Device Configuration Service Model Service Management Service Management Tasks Service Lifecycle Management Guidelines NSO Programmability Introduction NSO Programmability Overview Python Service Skeleton Creating a Service YANG Model Creating a Service Template Template Processing with Python Cisco NSO Flexibility System Configuration and Troubleshooting System Configuration System Troubleshooting Integration Integration Options NETCONF Server Web Integration SNMP Agent Alarm Management and Reporting Alarm Management Reporting Scalability and Performance High Availability High-Availability Cluster Communications Clustering Layered Service Architecture Addressing Performance Limitations Components and Function Packs Function Packs NFV Orchestration Reactive FASTMAP

Duration 1 Days 6 CPD hours This course is intended for This course is designed for data scientists with experience of Python who need to learn how to apply their data science and machine learning skills on Azure Databricks. Overview After completing this course, you will be able to: Provision an Azure Databricks workspace and cluster Use Azure Databricks to train a machine learning model Use MLflow to track experiments and manage machine learning models Integrate Azure Databricks with Azure Machine Learning Azure Databricks is a cloud-scale platform for data analytics and machine learning. In this course, students will learn how to use Azure Databricks to explore, prepare, and model data; and integrate Databricks machine learning processes with Azure Machine Learning. Introduction to Azure Databricks Getting Started with Azure Databricks Working with Data in Azure Databricks Training and Evaluating Machine Learning Models Preparing Data for Machine Learning Training a Machine Learning Model Managing Experiments and Models Using MLflow to Track Experiments Managing Models Managing Experiments and Models Using MLflow to Track Experiments Managing Models Integrating Azure Databricks and Azure Machine Learning Tracking Experiments with Azure Machine Learning Deploying Models

Computer Science GCSE Syllabus The GCSE Computer Science Tutor Syllabus is designed to provide tutors in England with a comprehensive framework for teaching the GCSE Computer Science curriculum effectively. This syllabus aims to equip tutors with the necessary knowledge and skills to support students in their understanding and application of core computer science concepts.  Module 1: Introduction to Computer Science - Overview of computer science and its relevance in today's world - Understanding the components of a computer system - Introduction to algorithms and problem-solving techniques - Exploration of programming languages and their uses Module 2: Computer Hardware - Understanding the main components of a computer system, including CPU, memory, and storage devices - Exploring input and output devices and their functionalities - Understanding the role of operating systems and software in computer systems Module 3: Software Development - Introduction to programming concepts and languages (e.g., Python or Java) - Understanding variables, data types, and operators - Building algorithms and logical reasoning skills - Introduction to flowcharts and pseudocode - Implementation of simple programs and debugging techniques Module 4: Data Representation - Understanding binary, hexadecimal, and denary number systems - Representation of text, images, and sound using binary - Introduction to data compression and encryption techniques Module 5: Computer Networks - Understanding the basics of computer networks, including LAN, WAN, and the Internet - Introduction to network topologies, protocols, and security - Exploring the impact of digital communication on society Module 6: Cybersecurity and Ethical Issues - Understanding the importance of cybersecurity and data protection - Introduction to common threats and vulnerabilities - Exploring ethical issues related to computer science, such as privacy and intellectual property rights Module 7: Algorithms and Programming Techniques - Advanced programming concepts, including conditionals, loops, and functions - Introduction to sorting and searching algorithms - Exploring data structures, such as arrays and lists Module 8: System Architecture - Understanding the structure and function of a CPU - Introduction to memory hierarchy and cache - Exploring the Von Neumann architecture and its limitations Module 9: Computational Thinking and Problem Solving - Advanced problem-solving techniques using computational thinking - Introduction to algorithms for complex problems - Exploring algorithmic efficiency and optimization techniques Module 10: Exam Preparation and Revision - Reviewing key concepts covered throughout the syllabus - Practicing past exam questions and providing guidance on exam techniques - Supporting students with exam preparation strategies Please note that the duration and depth of each module can vary depending on the level of expertise required and the specific needs of the learners. Additionally, it's important to adapt the curriculum to the learners' proficiency levels, whether they are A Level/GCSE students or adult learners with different experience levels.