105 Courses in London delivered Live Online

Duration 5 Days 30 CPD hours This course is intended for Students in this course are interested in Azure development or in passing the Microsoft Azure Developer Associate certification exam. This course teaches developers how to create end-to-end solutions in Microsoft Azure. Students will learn how to implement Azure compute solutions, create Azure Functions, implement and manage web apps, develop solutions utilizing Azure storage, implement authentication and authorization, and secure their solutions by using KeyVault and Managed Identities. Students will also learn how to connect to and consume Azure services and third-party services, and include event- and message-based models in their solutions. The course also covers monitoring, troubleshooting, and optimizing Azure solutions. Prerequisites To be successful in this course, learners should have the following: Hands-on experience with Azure IaaS and PaaS solutions, and the Azure Portal. Experience writing in an Azure supported language at the intermediate level. (C#, JavaScript, Python, or Java) Ability to write code to connect and perform operations on, a SQL or NoSQL database product. (SQL Server, Oracle, MongoDB, Cassandra or similar) Experience writing code to handle authentication, authorization, and other security principles at the intermediate level. A general understanding of HTML, the HTTP protocol and REST API interfaces. 1 - Explore Azure App Service Examine Azure App Service Examine Azure App Service plans Deploy to App Service Explore authentication and authorization in App Service Discover App Service networking features 2 - Configure web app settings Configure application settings Configure general settings Configure path mappings Enable diagnostic logging Configure security certificates 3 - Scale apps in Azure App Service Examine autoscale factors Identify autoscale factors Enable autoscale in App Service Explore autoscale best practices 4 - Explore Azure App Service deployment slots Explore staging environments Examine slot swapping Swap deployment slots Route traffic in App Service 5 - Explore Azure Functions Discover Azure Functions Compare Azure Functions hosting options Scale Azure Functions 6 - Develop Azure Functions Explore Azure Functions development Create triggers and bindings Connect functions to Azure services 7 - Explore Azure Blob storage Explore Azure Blob storage Discover Azure Blob storage resource types Explore Azure Storage security features Discover static website hosting in Azure Storage 8 - Manage the Azure Blob storage lifecycle Explore the Azure Blob storage lifecycle Discover Blob storage lifecycle policies Implement Blob storage lifecycle policies Rehydrate blob data from the archive tier 9 - Work with Azure Blob storage Explore Azure Blob storage client library Create a client object Manage container properties and metadata by using .NET Set and retrieve properties and metadata for blob resources by using REST 10 - Explore Azure Cosmos DB Identify key benefits of Azure Cosmos DB Explore the resource hierarchy Explore consistency levels Choose the right consistency level Explore supported APIs Discover request units 11 - Work with Azure Cosmos DB Explore Microsoft .NET SDK v3 for Azure Cosmos DB Create stored procedures Create triggers and user-defined functions Explore change feed in Azure Cosmos DB 12 - Manage container images in Azure Container Registry Discover the Azure Container Registry Explore storage capabilities Build and manage containers with tasks Explore elements of a Dockerfile 13 - Run container images in Azure Container Instances Explore Azure Container Instances Run containerized tasks with restart policies Set environment variables in container instances Mount an Azure file share in Azure Container Instances 14 - Implement Azure Container Apps Explore Azure Container Apps Explore containers in Azure Container Apps Implement authentication and authorization in Azure Container Apps Manage revisions and secrets in Azure Container Apps Explore Dapr integration with Azure Container Apps 15 - Explore the Microsoft identity platform Explore the Microsoft identity platform Explore service principals Discover permissions and consent Discover conditional access 16 - Implement authentication by using the Microsoft Authentication Library Explore the Microsoft Authentication Library Initialize client applications 17 - Implement shared access signatures Discover shared access signatures Choose when to use shared access signatures Explore stored access policies 18 - Explore Microsoft Graph Discover Microsoft Graph Query Microsoft Graph by using REST Query Microsoft Graph by using SDKs Apply best practices to Microsoft Graph 19 - Implement Azure Key Vault Explore Azure Key Vault Discover Azure Key Vault best practices Authenticate to Azure Key Vault 20 - Implement managed identities Explore managed identities Discover the managed identities authentication flow Configure managed identities Acquire an access token 21 - Implement Azure App Configuration Explore the Azure App Configuration service Create paired keys and values Manage application features Secure app configuration data 22 - Explore API Management Discover the API Management service Explore API gateways Explore API Management policies Create advanced policies Secure APIs by using subscriptions Secure APIs by using certificates 23 - Explore Azure Event Grid Explore Azure Event Grid Discover event schemas Explore event delivery durability Control access to events Receive events by using webhooks Filter events 24 - Explore Azure Event Hubs Discover Azure Event Hubs Explore Event Hubs Capture Scale your processing application Control access to events Perform common operations with the Event Hubs client library 25 - Discover Azure message queues Choose a message queue solution Explore Azure Service Bus Discover Service Bus queues, topics, and subscriptions Explore Service Bus message payloads and serialization Explore Azure Queue Storage Create and manage Azure Queue Storage and messages by using .NET 26 - Monitor app performance Explore Application Insights Discover log-based metrics Instrument an app for monitoring Select an availability test Troubleshoot app performance by using Application Map 27 - Develop for Azure Cache for Redis Explore Azure Cache for Redis Configure Azure Cache for Redis Interact with Azure Cache for Redis by using .NET 28 - Develop for storage on CDNs Explore Azure Content Delivery Networks Control cache behavior on Azure Content Delivery Networks Interact with Azure Content Delivery Networks by using .NET

Duration 4 Days 24 CPD hours This course is intended for The primary audience for this course is system installers, system integrators, system administrators, network administrators, and solutions designers. Overview At the end of this course, you will be able to: Describe the NSO's transactional application framework and mapping model options Describe the Reactive Fastmap design pattern and the NSO Configuration Database (CDB) subscriber in the NSO Transaction model Simplify packages to remove the need for subscriber applications, scale orchestration solutions, and integrate NSO with external systems (east-west integration)Describe the Cisco ESC architecture and integration with NSO, and how the NSO VNF Orchestration (VNFO) Release 2 bundle interacts with ESC for orchestration This course explores how to create advanced services using the NSO application framework and Python scripting with both new and existing Layer 3 Multiprotocol Label Switching (MPLS) VPN services. Students will also learn how to manage and scale these services, and how to use NSO Network Functions Virtualization (NFV) orchestration features and Cisco Elastic Services Controller (ESC) to manage Virtualized Network Functions (VNFs). Cisco NSO Programmability NSO Application Framework NSO Python Scripting NSO Python and Template-Based Services Resources Augmenting Cisco NSO Service Service Lifecycle and Integration Options Overview Greenfield Layer 3 MPLS VPN Service Brownfield Layer 3 MPLS VPN Service Managed Services Managed Services Overview Stacked Service Design Overview Design-Managed Network Services Scaling Service Orchestration Cisco NSO Network Functions Virtualization (NFV) Orchestration ETSI MANO Cisco ESC Cisco NSO Orchestration Additional course details: Nexus Humans Cisco Network Services Orchestrator Advanced Design (NSO300) 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 Network Services Orchestrator Advanced Design (NSO300) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

This 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.

Learn how to work with data using Python (the coding language) as a tool. Learn how data is structured and how to manipulate it into a usable, clean form ready for analysis. Work on a small real-life project from conception to solution, in a team or on your own.

Duration 3 Days 18 CPD hours This course is intended for Data Science for Marketing Analytics is designed for developers and marketing analysts looking to use new, more sophisticated tools in their marketing analytics efforts. It'll help if you have prior experience of coding in Python and knowledge of high school level mathematics. Some experience with databases, Excel, statistics, or Tableau is useful but not necessary. Overview By the end of this course, you will be able to build your own marketing reporting and interactive dashboard solutions. The course starts by teaching you how to use Python libraries, such as pandas and Matplotlib, to read data from Python, manipulate it, and create plots, using both categorical and continuous variables. Then, you'll learn how to segment a population into groups and use different clustering techniques to evaluate customer segmentation.As you make your way through the course, you'll explore ways to evaluate and select the best segmentation approach, and go on to create a linear regression model on customer value data to predict lifetime value. In the concluding sections, you'll gain an understanding of regression techniques and tools for evaluating regression models, and explore ways to predict customer choice using classification algorithms. Finally, you'll apply these techniques to create a churn model for modeling customer product choices. Data Preparation and Cleaning Data Models and Structured Data pandas Data Manipulation Data Exploration and Visualization Identifying the Right Attributes Generating Targeted Insights Visualizing Data Unsupervised Learning: Customer Segmentation Customer Segmentation Methods Similarity and Data Standardization k-means Clustering Choosing the Best Segmentation Approach Choosing the Number of Clusters Different Methods of Clustering Evaluating Clustering Predicting Customer Revenue Using Linear Regression Understanding Regression Feature Engineering for Regression Performing and Interpreting Linear Regression Other Regression Techniques and Tools for Evaluation Evaluating the Accuracy of a Regression Model Using Regularization for Feature Selection Tree-Based Regression Models Supervised Learning: Predicting Customer Churn Classification Problems Understanding Logistic Regression Creating a Data Science Pipeline Fine-Tuning Classification Algorithms Support Vector Machine Decision Trees Random Forest Preprocessing Data for Machine Learning Models Model Evaluation Performance Metrics Modeling Customer Choice Understanding Multiclass Classification Class Imbalanced Data Additional course details: Nexus Humans Data Science for Marketing Analytics 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 Data Science for Marketing Analytics 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 DevOps Engineers Software Developers Telecommunications Professionals Architects Quality Assurance & Site Reliability Professionals Overview Automate basic freestyle projects Jenkins Pipelines and Groovy Programming Software lifecycle management with Jenkins Popular plugins Scaling options Integrating Jenkins with Git and GitHub (as well as other Software Control Management platforms) Triggering Jenkins with Webhooks Deploying into Docker and Kubernetes CI / CD with Jenkins This course covers the fundamentals necessary to deploy and utilize the Jenkins automation server. Jenkins enables users to immediately begin automating both their individual and collaborative workflows. Jenkins is a proven solution for a wide variety of tasks ranging from the helpful automation of scripts (such as Python and Ansible) to creating complex pipelines that govern the technical parts of not only Continuous Integration, but Continuous Delivery (CI/CD) as well. Jenkins is free, open source, and easily controlled with a simple web- based UI- it can be expanded by third party plugins and is deployable on nearly any on-site (Linux, Windows and Mac) or cloud platform. Overview of Jenkins Overview of Continuous Integration and Continuous Deployment (CI/CD) Understanding Git and GitHub Git Branching Methods for Installing Jenkins Jenkins Dashboard Jenkins Jobs Getting Started with Freestyle Jobs Triggering builds HTTP Web Hooks Augmenting Jenkins with Plugins Overview of Docker and Dockerfile for Building and Launching Images Pipeline Jobs for Continuous Integration and Continuous Deployment Pipeline Build Stage Pipeline Testing Stage Post Build actions SMTP and Other Notifications Programming Pipelines with Groovy More Groovy Programming Essentials Extracting Jenkins Data Analytics to Support Project Management Troubleshooting Failures Auditing stdout and stderr with Jenkins Jenkins REST API Controlling Jenkins API with Python Jenkins Security Scaling Jenkins Jenkins CLI Building a Kubernetes Cluster and Deploying Jenkins How to start successfully using Jenkins to automate aspects of your job the moment this course ends.

Duration 4 Days 24 CPD hours This course is intended for The workshop is designed for data scientists who currently use Python or R to work with smaller datasets on a single machine and who need to scale up their analyses and machine learning models to large datasets on distributed clusters. Data engineers and developers with some knowledge of data science and machine learning may also find this workshop useful. Overview Overview of data science and machine learning at scale Overview of the Hadoop ecosystem Working with HDFS data and Hive tables using Hue Introduction to Cloudera Data Science Workbench Overview of Apache Spark 2 Reading and writing data Inspecting data quality Cleansing and transforming data Summarizing and grouping data Combining, splitting, and reshaping data Exploring data Configuring, monitoring, and troubleshooting Spark applications Overview of machine learning in Spark MLlib Extracting, transforming, and selecting features Building and evaluating regression models Building and evaluating classification models Building and evaluating clustering models Cross-validating models and tuning hyperparameters Building machine learning pipelines Deploying machine learning models Spark, Spark SQL, and Spark MLlib PySpark and sparklyr Cloudera Data Science Workbench (CDSW) Hue This workshop covers data science and machine learning workflows at scale using Apache Spark 2 and other key components of the Hadoop ecosystem. The workshop emphasizes the use of data science and machine learning methods to address real-world business challenges. Using scenarios and datasets from a fictional technology company, students discover insights to support critical business decisions and develop data products to transform the business. The material is presented through a sequence of brief lectures, interactive demonstrations, extensive hands-on exercises, and discussions. The Apache Spark demonstrations and exercises are conducted in Python (with PySpark) and R (with sparklyr) using the Cloudera Data Science Workbench (CDSW) environment. The workshop is designed for data scientists who currently use Python or R to work with smaller datasets on a single machine and who need to scale up their analyses and machine learning models to large datasets on distributed clusters. Data engineers and developers with some knowledge of data science and machine learning may also find this workshop useful. Overview of data science and machine learning at scaleOverview of the Hadoop ecosystemWorking with HDFS data and Hive tables using HueIntroduction to Cloudera Data Science WorkbenchOverview of Apache Spark 2Reading and writing dataInspecting data qualityCleansing and transforming dataSummarizing and grouping dataCombining, splitting, and reshaping dataExploring dataConfiguring, monitoring, and troubleshooting Spark applicationsOverview of machine learning in Spark MLlibExtracting, transforming, and selecting featuresBuilding and evauating regression modelsBuilding and evaluating classification modelsBuilding and evaluating clustering modelsCross-validating models and tuning hyperparametersBuilding machine learning pipelinesDeploying machine learning models Additional course details: Nexus Humans Cloudera Data Scientist Training 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 Cloudera Data Scientist Training 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 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