9405 Mode courses

NFV training course description Network Functions Virtualization (NFV) brings many benefits, this training course cuts through the hype and looks at the technology, architecture and products available for NFV. What will you learn Explain how NFV works. Describe the architecture of NFV. Explain the relationship between NFV and SDN. Recognise the impact NFV will have on existing networks. NFV training course details Who will benefit: Anyone wishing to know more about NFV. Prerequisites: Introduction to Virtualization. Duration 2 days NFV training course content Introduction What is NfV? What are network Functions? NfV benefits, NfV market drivers. ETSI NfV framework. Virtualization review Server, storage and network virtualization and NfV. Virtual machines, containers and docker. Data centres, clouds, SaaS, IaaS, PaaS. Virtualization of Network Functions Network virtualization versus Network Function virtualization. ETSI NfV architecture ETSI documents, Architecture overview, compute domain, hypervisor domain, infrastructure network domain. IETF and NfV Creating services, Service Functions, Service Function Chaining. SPRING and source packet routing. YANG and NetConf. RESTCONF. VLANs, VPNs, VXLAN. MANO Management and Orchestration. OpenStack, OpenDaylight PaaS and NfV. The VNF domain. Service graphs, MANO descriptors, Open orchestration. The virtualization layer VM centric model, containers versus hypervisors, FD.io. Summary Deploying NfV, performance, testing. Futures.

Duration 4 Days 24 CPD hours This course is intended for This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud. Overview Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure. Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Azure Machine Learning and MLflow. Prerequisites Creating cloud resources in Microsoft Azure. Using Python to explore and visualize data. Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow. Working with containers AI-900T00: Microsoft Azure AI Fundamentals is recommended, or the equivalent experience. 1 - Design a data ingestion strategy for machine learning projects Identify your data source and format Choose how to serve data to machine learning workflows Design a data ingestion solution 2 - Design a machine learning model training solution Identify machine learning tasks Choose a service to train a machine learning model Decide between compute options 3 - Design a model deployment solution Understand how model will be consumed Decide on real-time or batch deployment 4 - Design a machine learning operations solution Explore an MLOps architecture Design for monitoring Design for retraining 5 - Explore Azure Machine Learning workspace resources and assets Create an Azure Machine Learning workspace Identify Azure Machine Learning resources Identify Azure Machine Learning assets Train models in the workspace 6 - Explore developer tools for workspace interaction Explore the studio Explore the Python SDK Explore the CLI 7 - Make data available in Azure Machine Learning Understand URIs Create a datastore Create a data asset 8 - Work with compute targets in Azure Machine Learning Choose the appropriate compute target Create and use a compute instance Create and use a compute cluster 9 - Work with environments in Azure Machine Learning Understand environments Explore and use curated environments Create and use custom environments 10 - Find the best classification model with Automated Machine Learning Preprocess data and configure featurization Run an Automated Machine Learning experiment Evaluate and compare models 11 - Track model training in Jupyter notebooks with MLflow Configure MLflow for model tracking in notebooks Train and track models in notebooks 12 - Run a training script as a command job in Azure Machine Learning Convert a notebook to a script Run a script as a command job Use parameters in a command job 13 - Track model training with MLflow in jobs Track metrics with MLflow View metrics and evaluate models 14 - Perform hyperparameter tuning with Azure Machine Learning Define a search space Configure a sampling method Configure early termination Use a sweep job for hyperparameter tuning 15 - Run pipelines in Azure Machine Learning Create components Create a pipeline Run a pipeline job 16 - Register an MLflow model in Azure Machine Learning Log models with MLflow Understand the MLflow model format Register an MLflow model 17 - Create and explore the Responsible AI dashboard for a model in Azure Machine Learning Understand Responsible AI Create the Responsible AI dashboard Evaluate the Responsible AI dashboard 18 - Deploy a model to a managed online endpoint Explore managed online endpoints Deploy your MLflow model to a managed online endpoint Deploy a model to a managed online endpoint Test managed online endpoints 19 - Deploy a model to a batch endpoint Understand and create batch endpoints Deploy your MLflow model to a batch endpoint Deploy a custom model to a batch endpoint Invoke and troubleshoot batch endpoints

Server Load Balancing course description This two-day Server Load Balancing course introduces the concepts of SLB from the reasons to implement, through the basics and then onto details studies of load distribution, health checks, layer 7 switching and Global SLB. What will you learn Explain packet paths when implementing SLB. Recognise the impact of different topologies. Evaluate SLB load distribution methods. Describe how load balancers can improve security. Explain how GSLB works. Server Load Balancing course details Who will benefit: Anyone working with SLB. Prerequisites: None. Duration 2 days Server Load Balancing course contents Introduction Concept, reasons, benefits, alternatives. Other features: Security, Caching. SLB concepts Architectures, Virtual servers, real servers, Virtual IP address, health checks. DNS load balancing. Packet walk using SLB. Load balancing 6 modes of bonding and load balancing without SLB. ISP load balancing. Health. Distribution policies: Round Robin, least connections, weighted distributions, response time, other variations. Persistent versus concurrent. Layer 4 switching L2 SLB, L3 SLB, single arm SLB, DSR, more packet walking, TCP versus UDP, Port numbers. Layer 7 switching Persistence. Cookie switching, Cookie hashing, Cookie insertion, URL switching, URL Hashing, SSL. Health checks Layer 3: ARP, ping. Layer 4: SYN, UDP. Layer 7: HTTP GET, Status codes, HTTP keepalives, content verification, SSL. Other application keepalives. What to do after failure and recovery. Security DOS attack protection, SYN attack protection, Rate limiting: connections, transactions. SSL offload. Redundancy Hot standby, Active standby, Active active. Stateful, stateless. VRRP, STP. GSLB Anycasting. DNS, TTL, DNS load balancing, problems with DNS load balancing,. HTTP redirect, health, thresholds, round trip times, location.

Network virtualization training course description This course covers network virtualization. It has been designed to enable network engineers to recognise and handle the requirements of networking Virtual Machines. Both internal and external network virtualization is covered along with the technologies used to map overlay networks on to the physical infrastructure. Hands on sessions are used to reinforce the theory rather than teach specific manufacturer implementations. What will you learn Evaluate network virtualization implementations and technologies. Connect Virtual Machines with virtual switches. Explain how overlay networks operate. Describe the technologies in overlay networks. Network virtualization training course details Who will benefit: Engineers networking virtual machines. Prerequisites: Introduction to virtualization. Duration 2 days Network virtualization training course contents Virtualization review Hypervisors, VMs, containers, migration issues, Data Centre network design. TOR and spine switches. VM IP addressing and MAC addresses. Hands on VM network configuration Network virtualization What is network virtualization, internal virtual networks, external virtual networks. Wireless network virtualization: spectrum, infrastructure, air interface. Implementations: Open vSwitch, NSX, Cisco, others. Hands on VM communication over the network. Single host network virtualization NICs, vNICs, resource allocation, vSwitches, tables, packet walks. vRouters. Hands on vSwitch configuration, MAC and ARP tables. Container networks Single host, network modes: Bridge, host, container, none. Hands on Docker networking. Multi host network virtualization Access control, path isolation, controllers, overlay networks. L2 extensions. NSX manager. OpenStack neutron. Packet walks. Distributed logical firewalls. Load balancing. Hands on Creating, configuring and using a distributed vSwitch. Mapping virtual to physical networks VXLAN, VTEP, VXLAN encapsulation, controllers, multicasts and VXLAN. VRF lite, GRE, MPLS VPN, 802.1x. Hands on VXLAN configuration. Orchestration vCenter, vagrant, OpenStack, Kubernetes, scheduling, service discovery, load balancing, plugins, CNI, Kubernetes architecture. Hands on Kubernetes networking. Summary Performance, NFV, automation. Monitoring in virtual networks.

Advanced Ethernet switching training course description An advanced hands on switching course for those already familiar with the basics such as STP and VLANs. The course starts with advanced diagnostics and network monitoring moving onto switch protocols. A large part of the second day is spent implementing QoS and security such as 802.1x. What will you learn Troubleshoot switching. Explain how a number of switch protocols work, including: UDLD LLDP LACP DTP VTP/MVRP Design redundancy into switched networks. Implement QoS on switches. Harden switches. Advanced Ethernet switching training course details Who will benefit: Technical staff working with Ethernet switches. Prerequisites: None Duration 2 days Advanced Ethernet switching training course contents Switches Switch review, troubleshooting, diagnostics, L2 traceroute, UDLD, message logging, Wireshark, port mirroring, Hands on: Troubleshooting. Network management SNMP, SNMPv3, RMON, Netflow, Sflow. System logging. Hands on: Managing switches with SNMP. Syslogd. Switches and automatic configuration Auto-MDIX, LLDP, CDP, Link aggregation, LACP, Link state tracking, VLANS and tags, DTP. Hands on: Discovery, dynamic configuration. RSTP 802.1w, new port roles and states, new BPDUs, rapid convergence, topology changes, compatibility issues. L3 redundancy, VRRP, GLBP. Design issues. Hands on: RSTP, VRRP. VLANS: Registration protocols Why VTP? VTP modes, how VTP works, VTP pruning. GVRP, MVRP. Hands on: Dynamic VLANs STP variations and alternatives 802.1s (multiple spanning tree), regions, rings, L2MP, TRILL. Hands on: 802.1s Multicasting What is multicasting, Static configuration, IGMP snooping, CGMP, MVR. IPv6 MLD snooping. Hands on: Multicast through switches QoS Storm control, DSCP, 802.1Q, 802.1p, mapping, classification, policy, Ingress queues, Egress queues. Dropping frames, limiting bandwidth. Hands on: Voice through switches. More VLANS Native VLANs, Voice VLANs Security Static MAC addresses, AAA, RADIUS, Port based authentication, 802.1x, Guest VLANs, L2 attacks, SSH, HTTPS. Hands on: Hardening switches. Miscellaneous NTP, managing the MAC address table, managing system resources, SDN

HTTP streaming training course description This course looks at the delivery of video streams using HTTP adaptive streaming. Both MPEG DASH and HLS are investigated. Hands on sessions primarily involve using Wireshark to analyse streams. What will you learn Use Wireshark to analyse and troubleshoot HTTP video streams. Explain HTTP adaptive streaming works. Evaluate and compare MPEG DASH and HLS. Use tools to create HTTP adaptive streams. HTTP streaming training course details Who will benefit: Anyone working in the broadcast industry. Prerequisites: TCP/IP foundation for engineers Duration 2 days HTTP streaming training course contents What is HTTP streaming? The old way. Progressive downloads versus streaming. Why not UDP and RTP for delivery? Adaptive bit rate streaming. Standards. Hands on Base network setup. Using WireShark for HTTP streams. HTTP protocol stack IP, TCP, IPv6. HTTP. HTTP 1.0, HTTP 1.1, HTTP 2.0, HTTP header fields. HTML 5. Hands on Analysing HTTP. Adaptive bitrate streaming principles Chunks, fragments, segments. Manifest files. Encoding, resolution, bitrates. Addressing, relative and absolute URLs, redirection. When does the client switch streams? Switch points. Hands on Walk through of client behaviours on a stream. HTTP streaming architecture Server components, distribution components, client software. CDN, caching, multiple servers. Hands on Analysing CDN and Internet delivery. TCP and HTTP streaming interactions TCP ACK, TCP connections, unicast only. TCP flow control, TCP and performance. Hands on TCP window sizes. MPEG DASH Stakeholders, DASH architecture and model, codec agnostic, XML, Media Presentation Description, Media Presentation, segment formats. Hands on MPEG DASH analysis. HTTP Live Streaming and others Stakeholders. Media segments, media playlists, master playlists. Adobe HTTP dynamic streaming, Microsoft smooth streaming. Hands on Analysing HLS. Tools mp4dash, mp4fragment, libdash. Apple developer tools for HLS. Hands on Creating segmented content. Security HTTPS, encryption, content protection. Hands on Encryption analysis. Summary Choosing a streaming method. Impact of live versus VoD. Web sockets.

Total SIPp course description SIPp is a robust performance testing tool designed for evaluating the SIP protocol. This comprehensive course takes you on a journey from the initial installation of SIPp to mastering fundamental scenarios, exploring diverse architectures, delving into statistics analysis, and crafting XML scenario files. What will you learn Monitor SIP traffic with SIPp. Use SIPp for performance testing. Use the standard SIPp scenarios. Create custom scenarios in XML for SIPp. Total SIPp course details Who will benefit: Those working with SIP. Prerequisites: Definitive SIP for engineers Duration 2 days Total SIPp course contents Introduction What is SIPp? SIP review: UAC, UAS, INVITE, BYE. Sample SIP call flows. Hands on Wireshark, SIP call flow. Installing SIPp Getting SIPp, installing SIPp. Using SIPp Running sipp. sipp with uas scenario, sipp with uac scenario. The integrated scenarios. Online help. Hands on uac, uas. Controlling SIPp Hot keys, commands, UDP socket. Running SIPp in the background. Traffic control. SIPp performance testing. Hands on Changing call rates, remote control, pausing traffic. Monitoring SIP traffic Scenario screen, statistics. Response times, counters. Hands on Monitoring SIP traffic. More integrated scenarios SIPp and media and RTP. 3PCC. 3PCC extended. Transport modes: UDP, TCP, TLS, SCTP, IPv6 mono and multi socket. Hands on Third Party Call Control. XML What is XML? Content, markup, elements, attributes. Start tags, end tags. Hands on Displaying embedded scenarios, looking at the XML files of the integrated scenarios. Creating your own XML scenarios scenario, message commands, send, recv, nop, pause, sendCmd, recvCmd, common sipp scenario attributes, command specific sipp scenario attributes. XML DTD, jEdit. Hands on uac and uas scenario XML files. Recv actions Log and warning, exec, variables, variable types, variable scope. External variables. Hands on RTP streaming, Change a calls network destination, injection files. Regular expressions What is an RE. POSIX 1003.2. Re injection. Validation. Hands on regex example.

Definitive Puppet training course description Puppet is a framework and toolset for configuration management. This course covers Puppet to enable delegates to manage configurations. Hands on sessions follow all the major sections. What will you learn Deploy Puppet. Manage configurations with Puppet. Build hosts with Puppet. Produce reports with Puppet. Definitive Puppet training course details Who will benefit: Anyone working with Puppet. Prerequisites: Linux fundamentals. Duration 2 days Definitive Puppet training course contents Getting started with Puppet What is Puppet, Selecting the right version of Puppet, Installing Puppet, Configuring Puppet. Developing and deploying Puppet The puppet apply command and modes of operation, Foreground Puppet master, Developing Puppet with Vagrant, Environments, Making changes to the development environment, Testing the new environments with the Puppet agent, Environment branching and merging, Dynamic Puppet environments with Git branches, Summary, Resources. Scaling Puppet Identifying the challenges, Running the Puppet master with Apache and Passenger, Testing the Puppet master in Apache, Load balancing multiple Puppet masters, Scaling further, Load balancing alternatives. Measuring performance, Splay time, Summary, Going further, Resources. Externalizing Puppet configuration External node classification, Storing node configuration in LDAP, Summary, Resources. Exporting and storing configuration Virtual resources, Getting started with exported and stored configurations, Using exported resources, Expiring state resources, Summary, Resources. Puppet consoles The foreman, Puppet enterprise console, Puppetboard, Summary, Resources. Tools and integration Puppet forge and the module tool, Searching and installing a module from the forge, Generating a module, Managing module dependencies, Testing the modules, Developing Puppet modules with Geppetto, Summary, Resources. Reporting with Puppet Getting started, Configuring reporting, Report processors, Custom reporting, Other Puppet reporters, Summary, Resources. Extending Facter and Puppet Writing and distributing custom facts, Developing custom types, providers and functions, Summary, Resources, Complex data structures, Additional backends, Hiera functions in depth, Module data bindings, Hiera examples. Jiera-2, Summary, Resources. Mcollective Installing and configuring Mcollective, testing, Mcollective plugins, accessing hosts with Metadata. Hiera Lists, initial Hiera configuration, Hiera command line utility, complex data structures, additional backends, Hiera functions in depth, module data bindings. Hiera-2.

OTT TV for engineers course description This course covers OTT TV by primarily looking at the delivery of video streams using HTTP adaptive streaming. Both MPEG DASH and HLS are investigated. Hands on sessions involve using Wireshark to analyse streams as well as crafting segmented content. What will you learn Explain what OTT TV is, and how it works. Describe the OTT TV architecture. Use Wireshark to analyse and troubleshoot OTT video streams. Explain how HTTP adaptive streaming works. Evaluate and compare MPEG DASH and HLS. Use tools to create OTT TV adaptive streams. OTT TV for engineers course details Who will benefit: Anyone working in the broadcast industry. Prerequisites: TCP/IP foundation for engineers. Duration 2 days OTT TV for engineers course contents What is OTT TV? Brodeo providers vs ISPs. Progressive downloads versus streaming. Why not UDP and RTP for delivery? Adaptive bit rate streaming. Standards. Hands on: Base network setup. Using WireShark for HTTP streams. HTTP protocol stack IP, TCP, IPv6. HTTP. HTTP 1.0, HTTP 1.1, HTTP 2.0, HTTP header fields. HTML 5. Hands on: Analysing HTTP. Adaptive bitrate streaming principles Chunks, fragments, segments. Manifest files. Encoding, resolution, bitrates. Addressing, relative and absolute URLs, redirection. When does the client switch streams? Switch points. Hands on: Walk through of client behaviours on a stream. OTT TV streaming architecture Server components, distribution components, client software. CDN, caching, multiple servers. Hands on: Analysing CDN and Internet delivery. TCP and HTTP streaming interactions TCP ACK, TCP connections, unicast only. TCP flow control, TCP and performance. Hands on: TCP window sizes. MPEG DASH Stakeholders, DASH architecture and model, codec agnostic, XML, Media Presentation Description, Media Presentation, segment formats. Hands on: MPEG DASH analysis. HTTP Live Streaming and others Stakeholders. Media segments, media playlists, master playlists. Adobe HTTP dynamic streaming, Microsoft smooth streaming. Hands on: Analysing HLS. Tools mp4dash, mp4fragment, libdash. Apple developer tools for HLS. Hands on: Creating segmented content. Security HTTPS, encryption, content protection. Hands on: Encryption analysis. Summary Choosing a streaming method. Impact of live versus VoD. Web sockets.

Duration 4 Days 24 CPD hours This course is intended for Software engineers concerned with building, managing and deploying AI solutions that leverage Azure AI Services, Azure AI Search, and Azure OpenAI. They are familiar with C# or Python and have knowledge on using REST-based APIs to build computer vision, language analysis, knowledge mining, intelligent search, and generative AI solutions on Azure. AI-102 Designing and Implementing an Azure AI Solution is intended for software developers wanting to build AI infused applications that leverage?Azure AI Services,?Azure AI Search, and?Azure OpenAI. The course will use C# or Python as the programming language. Prerequisites Before attending this course, students must have: Knowledge of Microsoft Azure and ability to navigate the Azure portal Knowledge of either C# or Python Familiarity with JSON and REST programming semantics Recommended course prerequisites AI-900T00: Microsoft Azure AI Fundamentals course 1 - Prepare to develop AI solutions on Azure Define artificial intelligence Understand AI-related terms Understand considerations for AI Engineers Understand considerations for responsible AI Understand capabilities of Azure Machine Learning Understand capabilities of Azure AI Services Understand capabilities of the Azure Bot Service Understand capabilities of Azure Cognitive Search 2 - Create and consume Azure AI services Provision an Azure AI services resource Identify endpoints and keys Use a REST API Use an SDK 3 - Secure Azure AI services Consider authentication Implement network security 4 - Monitor Azure AI services Monitor cost Create alerts View metrics Manage diagnostic logging 5 - Deploy Azure AI services in containers Understand containers Use Azure AI services containers 6 - Analyze images Provision an Azure AI Vision resource Analyze an image Generate a smart-cropped thumbnail 7 - Classify images Provision Azure resources for Azure AI Custom Vision Understand image classification Train an image classifier 8 - Detect, analyze, and recognize faces Identify options for face detection analysis and identification Understand considerations for face analysis Detect faces with the Azure AI Vision service Understand capabilities of the face service Compare and match detected faces Implement facial recognition 9 - Read Text in images and documents with the Azure AI Vision Service Explore Azure AI Vision options for reading text Use the Read API 10 - Analyze video Understand Azure Video Indexer capabilities Extract custom insights Use Video Analyzer widgets and APIs 11 - Analyze text with Azure AI Language Provision an Azure AI Language resource Detect language Extract key phrases Analyze sentiment Extract entities Extract linked entities 12 - Build a question answering solution Understand question answering Compare question answering to Azure AI Language understanding Create a knowledge base Implement multi-turn conversation Test and publish a knowledge base Use a knowledge base Improve question answering performance 13 - Build a conversational language understanding model Understand prebuilt capabilities of the Azure AI Language service Understand resources for building a conversational language understanding model Define intents, utterances, and entities Use patterns to differentiate similar utterances Use pre-built entity components Train, test, publish, and review a conversational language understanding model 14 - Create a custom text classification solution Understand types of classification projects Understand how to build text classification projects 15 - Create a custom named entity extraction solution Understand custom named entity recognition Label your data Train and evaluate your model 16 - Translate text with Azure AI Translator service Provision an Azure AI Translator resource Specify translation options Define custom translations 17 - Create speech-enabled apps with Azure AI services Provision an Azure resource for speech Use the Azure AI Speech to Text API Use the text to speech API Configure audio format and voices Use Speech Synthesis Markup Language 18 - Translate speech with the Azure AI Speech service Provision an Azure resource for speech translation Translate speech to text Synthesize translations 19 - Create an Azure AI Search solution Manage capacity Understand search components Understand the indexing process Search an index Apply filtering and sorting Enhance the index 20 - Create a custom skill for Azure AI Search Create a custom skill Add a custom skill to a skillset 21 - Create a knowledge store with Azure AI Search Define projections Define a knowledge store 22 - Plan an Azure AI Document Intelligence solution Understand AI Document Intelligence Plan Azure AI Document Intelligence resources Choose a model type 23 - Use prebuilt Azure AI Document Intelligence models Understand prebuilt models Use the General Document, Read, and Layout models Use financial, ID, and tax models 24 - Extract data from forms with Azure Document Intelligence What is Azure Document Intelligence? Get started with Azure Document Intelligence Train custom models Use Azure Document Intelligence models Use the Azure Document Intelligence Studio 25 - Get started with Azure OpenAI Service Access Azure OpenAI Service Use Azure OpenAI Studio Explore types of generative AI models Deploy generative AI models Use prompts to get completions from models Test models in Azure OpenAI Studio's playgrounds 26 - Build natural language solutions with Azure OpenAI Service Integrate Azure OpenAI into your app Use Azure OpenAI REST API Use Azure OpenAI SDK 27 - Apply prompt engineering with Azure OpenAI Service Understand prompt engineering Write more effective prompts Provide context to improve accuracy 28 - Generate code with Azure OpenAI Service Construct code from natural language Complete code and assist the development process Fix bugs and improve your code 29 - Generate images with Azure OpenAI Service What is DALL-E? Explore DALL-E in Azure OpenAI Studio Use the Azure OpenAI REST API to consume DALL-E models 30 - Use your own data with Azure OpenAI Service Understand how to use your own data Add your own data source Chat with your model using your own data 31 - Fundamentals of Responsible Generative AI Plan a responsible generative AI solution Identify potential harms Measure potential harms Mitigate potential harms Operate a responsible generative AI solution