23575 Tea courses delivered Online

Duration 2 Days 12 CPD hours This course is intended for There is no specific prerequisite for the CDRP© course. However, participants who have at least three years' experience in a data centre and/or IT infrastructures will be best suited. Overview After completion of the course, the participant will be able to: 1. Understand the different standards and methodologies for risk management and assessment 2. Establish the required project team for risk management 3. Perform the risk assessment, identifying current threats, vulnerabilities and the potential impact based on customised threat catalogues 4. Report on the current risk level of the data centre both quantitative and qualitative 5. Anticipate and minimise potential financial impacts 6. Understand the options for handling risk 7. Continuously monitor and review the status of risk present in the data centre 8. Reduce the frequency and magnitude of incidents 9. Detect and respond to events when they occur 10. Meet regulatory and compliance requirements 11. Support certification processes such as ISO/IEC 27001 12. Support overall corporate and IT governance Introduction to Risk Management Risk management concepts Senior management and risk Enterprise Risk Management (ERM) Benefits of risk management Data Centre Risk and Impact Risk in facility, power, cooling, fire suppression, infrastructure and IT services Impact of data centre downtime Main causes of downtime Cost factors in downtime Standards, Guidelines and Methodologies ISO/IEC 27001:2013, ISO/IEC 27005:2011, ISO/IEC 27002:2013 NIST SP 800-30 ISO/IEC 31000:2009 SS507:2008 ANSI/TIA-942 Other methodologies (CRAMM, EBIOS, OCTAVE, etc.) Risk Management Definitions Asset Availability/Confidentiality/Integrity Control Information processing facility Information security Policy Risk Risk analysis/Risk assessment/Risk evaluation/ Risk treatment Threat/Vulnerability Types of risk Risk Assessment Software The need for software Automation Considerations Risk Management Process The risk management process Establishing the context Identification Analysis Evaluation Treatment Communication and consultation Monitoring and review Project Approach Project management principles Project management methods Scope Time Cost Cost estimate methods Context Establishment General considerations Risk evaluation, impact and acceptance criteria Severity rating of impact Occurrence rating of probability Scope and boundaries Scope constraints Roles & responsibilities Training, awareness and competence Risk Assessment - Identification The risk assessment process Identification of assets Identification of threats Identification of existing controls Identification of vulnerabilities Identification of consequences Hands-on exercise: Identification of assets, threats, existing controls, vulnerabilities and consequences Risk Assessment - Analysis and Evaluation Risk estimation Risk estimation methodologies Assessment of consequences Assessment of incident likelihood Level of risk estimation Risk evaluation Hands-on exercise: Assessment of consequences, probability and estimating level of risk Risk Treatment The risk treatment process steps Risk Treatment Plan (RTP) Risk modification Risk retention Risk avoidance Risk sharing Constraints in risk modification Control categories Control examples Cost-benefit analysis Control implementation Residual risk Communication Effective communication of risk management activities Benefits and concerns of communication Risk Monitoring and Review Ongoing monitoring and review Criteria for review Risk scenarios Risk assessment approach Data centre site selection Data centre facility Cloud computing UPS scenarios Force majeure Organisational shortcomings Human failure Technical failure Deliberate acts Exam: Certified Data Centre Risk Professional Actual course outline may vary depending on offering center. Contact your sales representative for more information.

Duration 2 Days 12 CPD hours This course is intended for This is an intermediate level training course, designed for experienced Java developers and architects who need to identify, design, and implement web services. We will explore and apply the terminology, the specification, the processes and technologies specific to web services. Students should have at least a year of practical development experience with Java and servlets, and should be familiar with XML and JSON. Overview This 'skills-centric' course is about 50% hands-on lab and 50% lecture, designed to train attendees in core RESTful development skills, coupling the most current, effective techniques with the soundest industry practices. Working within in a hands-on learning environment, guided by our expert team, attendees will learn to: Understand and apply the basic concepts of REST Understand and intelligently discuss the similarities and differences between RESTful services and SOAP-based services Appreciate the security concerns associated with RESTful services Design, develop, and deploy real-world RESTful Services Effectively define and design endpoints Work with query parameters Determine the best format to use for exchanging data Understand the principles associated with HTTP methods and how to stay congruent to them Extend the semantics of the RESTful service beyond the HTTP methods Implement RESTful services using JAX-RS to Handle various HTTP methods Use different approaches for parameters Deal with content negotiation Work with different data formats including XML and JSON Handle exceptions Implement RESTful Java clients Develop JAX-RS Java clients using the JAX-RS 2.0 Client API Manage different Web targets Deal with content negotiation on the client Work with different data formats including XML and JSON Properly process server responses Implement JAX-RS Filters and Interceptors Intercept and manipulate service requests and responses Dynamically register interceptor to resources Understand the concept of NameBinding Discover asynchronous JAX-RS processing Implement an asynchronous JAX-RS service endpoint Register response listeners Geared for experienced Java developers, Java REST Essentials is a two day, lab-intensive services training course that introduces developers to the core concepts, principles, and Java implementations for RESTful services. Today's development environments are increasingly dominated by sophisticated tooling that makes the implementation of RESTful services less arduous. The proper design of these services is far more complex and demanding. Java RESTful Service Essentials focuses on providing an understanding of the fundamental principles and technologies that are used in building these services. This understanding is critical to being able to diagnose, troubleshoot, tune, and perform other lifecycle activities. Session: Working with REST Overview of REST Designing RESTful Services JAX-RS Lesson: Introduction to JAX-RS @Path: URI Matching JAX-RS Content Negotiation JAX-RS Request and Response JAX-RS Client API JAX-RS Filters and Interceptors Asynchronous JAX-RS Additional course details: Nexus Humans Java REST Essentials (TT7305) 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 Java REST Essentials (TT7305) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 5 Days 30 CPD hours This course is intended for This course is designed for experienced integration specialists and senior-level developers with experience in application development, messaging middleware applications, and transport protocols such as HTTP and FTP. Overview Describe the features and uses of IBM App Connect Enterprise Develop, deploy, and test message flow applications Generate message flow applications from predefined patterns Use the IBM App Connect Enterprise Toolkit problem determination aids to diagnose and solve development and runtime errors Describe the function and appropriate use of IBM App Connect Enterprise processing nodes Write basic Extended Structured Query Language and Java programs to transform data Use the IBM Graphical Data Mapping editor to transform data Define, use, and test simple XML and Data Format Description Language (DFDL) data models Describe supported transport protocols and how to call them in message flows IBM App Connect Enterprise provides connectivity and universal data transformation in heterogeneous IT environments. It enables businesses of any size to eliminate point-to-point connections and batch processing, regardless of operating system, protocol, and data format. This course teaches you how to use IBM App Connect Enterprise to develop, deploy, and support message flow applications. These applications use various messaging topologies to transport messages between service requesters and service providers, and allow the messages to be routed, transformed, and enriched during processing. In this course, you learn how to construct applications to transport and transform data. The course explores how to control the flow of data by using various processing nodes, and how to use databases and maps to transform and enrich data during processing. You also learn how to construct data models by using the Data Format Description Language (DFDL) Course Outline Introduction to IBM App Connect Enterprise Application development fundamentals Exercise: Importing and testing a message flow Creating message flow applications Exercise: Creating a message flow application Connecting to IBM MQ Exercise: Connecting to IBM MQ Controlling the flow of messages Exercise: Adding flow control to a message flow application Modeling the data Exercise: Creating a DFDL model Processing file data Exercise: Processing file data Using problem determination tools and help resources Exercise: Using problem determination tools Exercise: Implementing explicit error handling Mapping messages with the Graphical Data Mapping editor Referencing a database in a message flow application Exercise: Referencing a database in a map Using Compute nodes to transform messages Exercise: Transforming data by using the Compute and JavaCompute nodes Processing JMS, HTTP, and web service messages Preparing for production Exercise: Creating a runtime-aware message flow Additional course details: Nexus Humans WM668G IBM App Connect Enterprise V11 Application Development training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the WM668G IBM App Connect Enterprise V11 Application Development course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 1 Days 6 CPD hours This course is intended for This course is intended for the following participants:Cloud professionals interested in taking the Data Engineer certification exam.Data engineering professionals interested in taking the Data Engineer certification exam. Overview This course teaches participants the following skills: Position the Professional Data Engineer Certification Provide information, tips, and advice on taking the exam Review the sample case studies Review each section of the exam covering highest-level concepts sufficient to build confidence in what is known by the candidate and indicate skill gaps/areas of study if not known by the candidate Connect candidates to appropriate target learning This course will help prospective candidates plan their preparation for the Professional Data Engineer exam. The session will cover the structure and format of the examination, as well as its relationship to other Google Cloud certifications. Through lectures, quizzes, and discussions, candidates will familiarize themselves with the domain covered by the examination, to help them devise a preparation strategy. Rehearse useful skills including exam question reasoning and case comprehension. Tips and review of topics from the Data Engineering curriculum. Understanding the Professional Data Engineer Certification Position the Professional Data Engineer certification among the offerings Distinguish between Associate and Professional Provide guidance between Professional Data Engineer and Associate Cloud Engineer Describe how the exam is administered and the exam rules Provide general advice about taking the exam Sample Case Studies for the Professional Data Engineer Exam Flowlogistic MJTelco Designing and Building (Review and preparation tips) Designing data processing systems Designing flexible data representations Designing data pipelines Designing data processing infrastructure Build and maintain data structures and databases Building and maintaining flexible data representations Building and maintaining pipelines Building and maintaining processing infrastructure Analyzing and Modeling (Review and preparation tips) Analyze data and enable machine learning Analyzing data Machine learning Machine learning model deployment Model business processes for analysis and optimization Mapping business requirements to data representations Optimizing data representations, data infrastructure performance and cost Reliability, Policy, and Security (Review and preparation tips) Design for reliability Performing quality control Assessing, troubleshooting, and improving data representation and data processing infrastructure Recovering data Visualize data and advocate policy Building (or selecting) data visualization and reporting tools Advocating policies and publishing data and reports Design for security and compliance Designing secure data infrastructure and processes Designing for legal compliance Resources and next steps Resources for learning more about designing data processing systems, data structures, and databases Resources for learning more about data analysis, machine learning, business process analysis, and optimization Resources for learning more about data visualization and policy Resources for learning more about reliability design Resources for learning more about business process analysis and optimization Resources for learning more about reliability, policies, security, and compliance Additional course details: Nexus Humans Preparing for the Professional Data Engineer Examination 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 Preparing for the Professional Data Engineer Examination 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 From Playgrounds to protocols?discover, explore, and demonstrate how to use the fundamental building blocks of the Swift programming language. This 2-day, hands-on course teaches you the basic concepts of Swift programming, including syntax, logic, structures, functions, and patterns. It also includes detailed explanations of language syntax and coding exercises Introduction to Swift and Playgrounds Learn about the origin of Swift and some of its basic syntax Constants, Variables, and Data Types Learn how to define constants for values that don?t change and variables for values that do. Learn the data types that are included in Swift and how they can help you write better code Operators Learn about some of the operators in the Swift language, including basic math operators. Control Flow Learn how to use logical operators in Swift to check conditions; learn how to use control flow statements. Strings Learn how to create and store text using the string type. You'll learn a variety of string methods that allow you to compare two strings, access specific characters within a string, and insert and remove values Functions Learn how to declare functions with different parameters and return types Structures Learn how to create structures in Swift. Classes Learn what makes classes different from structures and when to use classes instead of structures. Also learn about inheritance, superclasses, and subclasses. Optionals Learn to use ?optionals? to properly handle situations when data may or may not exist Collections Learn about the various collection types available in Swift and how to choose the appropriate one for your program. Loops Learn how to create loops in Swift, control the conditions for looping, and specify when to stop. Type Casting Learn why some data can be expressed using only a broader type, and how you can test for specific kinds of data before using it. Guard Learn to use guard statements to better manage control flow. Scope Learn to write nicely structured code that's easy to read. You'll do this by properly scoping your constants and variables Enumerations Learn when enumerations are commonly used, how to define an enumeration, and how to work with enumerations using switch statements. Protocols Learn what protocols are, when to use them, and how to write your own. Learn how to enable objects to communicate with each other and how to extend protocols to provide shared functionality across multiple types Closures Learn about closures, how to define them, how to use them as function arguments, and how to use some of the common functions that take closures as arguments. Extensions Learn how to define an extension, as well as how and why to use extensions. Additional course details: Nexus Humans Introduction to Swift 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 Introduction to Swift course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 3 Days 18 CPD hours This course is intended for This is an introductory level course for experienced software developers seeking to enhance and extend their core web development skillset leveraging JavaScript. Attendees should have practical experience developing basic software applications. This course provides an excellent foundation for continued learning to gain in-demand skills in in-demand skills and technologies such as NodeJS, Angular, React, Redux and more. This course can also be tailored for less experienced or non-developers as needed. Please inquire for details. Overview Throughout this course, students will explore the practical use of the umbrella of technologies that work in conjunction with JavaScript as well as some of the tools, toolkits, and frameworks that can be used in conjunction with web development and deployment. The course thoroughly explores JavaScript and how it is used within the context of web applications, walking students through the different technologies that are used with JavaScript and exploring core aspects of JavaScript in terms of web applications, security, tools, and frameworks. This skills-focused course is approximately 50% hands-on lab to lecture ratio. Our instructors are highly experienced practitioners who bring years of current 'on-the-job' experience into every classroom. Working within in a hands-on learning environment guided by our expert team, attendees will learn to: Understand what JavaScript is and how it is used within the context of web applications Work with the different technologies that are the foundation for web applications. Understand and work with the fundamental aspects of JavaScript in terms of web applications, security, tools, and frameworks Learn to how to effectively work with the newest advances in JavaScript such as ES6 and TypeScript Develop code using conventions and optimal constructs for performance Introduction to JavaScript | Modern JavaScript Essentials is a hands-on geared for web developers who need to learn basic JavaScript to use with today's systems and architectures to build sophisticated web interfaces. The training will guide students through a balanced mixture of theory and practical labs to gain core JavaScript development skills and have them explore its related technologies through to the use of tools and libraries to ease the development of advanced web applications. Course attendees will be able to hit the ground running right after class, applying essential JavaScript to projects at both an architectural as well as a line by line coding level. HTML Refresher (optional) HTMLÿ HTML5 CSS Refresher (optional) CSSÿ CSS3 Overview Introduction to JavaScript JavaScript Basics Debugging Tools JavaScript Functions JavaScript Arrays, Math and Date JavaScript Event Handling and the DOM Object-Oriented JavaScript Advanced JavaScript Topics The Next Step TypeScript Introduction to JSON and Ajax JavaScript Best Practices JavaScript Scheduling, Execution, and Security HTML5 JavaScript API Working with XML (Optional) XML DOM Mechanics XSLT Applied Additional course details: Nexus Humans Introduction to JavaScript | Modern JavaScript Essentials (TT4110) 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 Introduction to JavaScript | Modern JavaScript Essentials (TT4110) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 3 Days 18 CPD hours This course is intended for This course is geared for attendees with Intermediate IT skills who wish to learn Computer Vision with tensor flow 2 Overview This 'skills-centric' course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course. Working in a hands-on learning environment, led by our Computer Vision expert instructor, students will learn about and explore how to Build, train, and serve your own deep neural networks with TensorFlow 2 and Keras Apply modern solutions to a wide range of applications such as object detection and video analysis Run your models on mobile devices and web pages and improve their performance. Create your own neural networks from scratch Classify images with modern architectures including Inception and ResNet Detect and segment objects in images with YOLO, Mask R-CNN, and U-Net Tackle problems faced when developing self-driving cars and facial emotion recognition systems Boost your application's performance with transfer learning, GANs, and domain adaptation Use recurrent neural networks (RNNs) for video analysis Optimize and deploy your networks on mobile devices and in the browser Computer vision solutions are becoming increasingly common, making their way into fields such as health, automobile, social media, and robotics. Hands-On Computervision with TensorFlow 2 is a hands-on course that thoroughly explores TensorFlow 2, the brandnew version of Google's open source framework for machine learning. You will understand how to benefit from using convolutional neural networks (CNNs) for visual tasks. This course begins with the fundamentals of computer vision and deep learning, teaching you how to build a neural network from scratch. You will discover the features that have made TensorFlow the most widely used AI library, along with its intuitive Keras interface. You'll then move on to building, training, and deploying CNNs efficiently. Complete with concrete code examples, the course demonstrates how to classify images with modern solutions, such as Inception and ResNet, and extract specific content using You Only Look Once (YOLO), Mask R-CNN, and U-Net. You will also build generative dversarial networks (GANs) and variational autoencoders (VAEs) to create and edit images, and long short-term memory networks (LSTMs) to analyze videos. In the process, you will acquire advanced insights into transfer learning, data augmentation, domain adaptation, and mobile and web deployment, among other key concepts Computer Vision and Neural Networks Computer Vision and Neural Networks Technical requirements Computer vision in the wild A brief history of computer vision Getting started with neural networks TensorFlow Basics and Training a Model TensorFlow Basics and Training a Model Technical requirements Getting started with TensorFlow 2 and Keras TensorFlow 2 and Keras in detail The TensorFlow ecosystem Modern Neural Networks Modern Neural Networks Technical requirements Discovering convolutional neural networks Refining the training process Influential Classification Tools Influential Classification Tools Technical requirements Understanding advanced CNN architectures Leveraging transfer learning Object Detection Models Object Detection Models Technical requirements Introducing object detection A fast object detection algorithm YOLO Faster R-CNN ? a powerful object detection model Enhancing and Segmenting Images Enhancing and Segmenting Images Technical requirements Transforming images with encoders-decoders Understanding semantic segmentation Training on Complex and Scarce Datasets Training on Complex and Scarce Datasets Technical requirements Efficient data serving How to deal with data scarcity Video and Recurrent Neural Networks Video and Recurrent Neural Networks Technical requirements Introducing RNNs Classifying videos Optimizing Models and Deploying on Mobile Devices Optimizing Models and Deploying on Mobile Devices Technical requirements Optimizing computational and disk footprints On-device machine learning Example app ? recognizing facial expressions

Duration 3 Days 18 CPD hours This course is intended for This course is aimed at anyone who wants to harness the power of data analytics in their organization including: Business Analysts, Data Analysts, Reporting and BI professionals Analytics professionals and Data Scientists who would like to learn Python Overview This course teaches delegates with no prior programming or data analytics experience how to perform data manipulation, data analysis and data visualization in Python. Mastery of these techniques and how to apply them to business problems will allow delegates to immediately add value in their workplace by extracting valuable insight from company data to allow better, data-driven decisions. Outcome: After attending this course, delegates will: Be able to write effective Python code Know how to access their data from a variety of sources using Python Know how to identify and fix data quality using Python Know how to manipulate data to create analysis ready data Know how to analyze and visualize data to drive data driven decisioning across your organization Becoming a world class data analytics practitioner requires mastery of the most sophisticated data analytics tools. These programming languages are some of the most powerful and flexible tools in the data analytics toolkit. From business questions to data analytics, and beyond For data analytics tasks to affect business decisions they must be driven by a business question. This section will formally outline how to move an analytics project through key phases of development from business question to business solution. Delegates will be able: to describe and understand the general analytics process. to describe and understand the different types of analytics can be used to derive data driven solutions to business to apply that knowledge to their business context Basic Python Programming Conventions This section will cover the basics of writing R programs. Topics covered will include: What is Python? Using Anaconda Writing Python programs Expressions and objects Functions and arguments Basic Python programming conventions Data Structures in Python This section will look at the basic data structures that Python uses and accessing data in Python. Topics covered will include: Vectors Arrays and matrices Factors Lists Data frames Loading .csv files into Python Connecting to External Data This section will look at loading data from other sources into Python. Topics covered will include: Loading .csv files into a pandas data frame Connecting to and loading data from a database into a panda data frame Data Manipulation in Python This section will look at how Python can be used to perform data manipulation operations to prepare datasets for analytics projects. Topics covered will include: Filtering data Deriving new fields Aggregating data Joining data sources Connecting to external data sources Descriptive Analytics and Basic Reporting in Python This section will explain how Python can be used to perform basic descriptive. Topics covered will include: Summary statistics Grouped summary statistics Using descriptive analytics to assess data quality Using descriptive analytics to created business report Using descriptive analytics to conduct exploratory analysis Statistical Analysis in Python This section will explain how Python can be used to created more interesting statistical analysis. Topics covered will include: Significance tests Correlation Linear regressions Using statistical output to create better business decisions. Data Visualisation in Python This section will explain how Python can be used to create effective charts and visualizations. Topics covered will include: Creating different chart types such as bar charts, box plots, histograms and line plots Formatting charts Best Practices Hints and Tips This section will go through some best practice considerations that should be adopted of you are applying Python in a business context.

Duration 3 Days 18 CPD hours This course is intended for This class is intended for the following participants: Cloud architects, administrators, and SysOps/DevOps personnel Individuals using Google Cloud Platform to create new solutions or to integrate existing systems, application environments, and infrastructure with the Google Cloud Platform. Overview This course teaches participants the following skills: Understand how software containers work Understand the architecture of Kubernetes Understand the architecture of Google Cloud Platform Understand how pod networking works in Kubernetes Engine Create and manage Kubernetes Engine clusters using the GCP Console and gcloud/ kubectl commands Launch, roll back and expose jobs in Kubernetes Manage access control using Kubernetes RBAC and Google Cloud IAM Managing pod security policies and network policies Using Secrets and ConfigMaps to isolate security credentials and configuration artifacts Understand GCP choices for managed storage services Monitor applications running in Kubernetes Engine This class introduces participants to deploying and managing containerized applications on Google Kubernetes Engine (GKE) and the other services provided by Google Cloud Platform. Through a combination of presentations, demos, and hands-on labs, participants explore and deploy solution elements, including infrastructure components such as pods, containers, deployments, and services; as well as networks and application services. This course also covers deploying practical solutions including security and access management, resource management, and resource monitoring. Introduction to Google Cloud Platform Use the Google Cloud Platform Console Use Cloud Shell Define cloud computing Identify GCPs compute services Understand regions and zones Understand the cloud resource hierarchy Administer your GCP resources Containers and Kubernetes in GCP Create a container using Cloud Build Store a container in Container Registry Understand the relationship between Kubernetes and Google Kubernetes Engine (GKE) Understand how to choose among GCP compute platforms Kubernetes Architecture Understand the architecture of Kubernetes: pods, namespaces Understand the control-plane components of Kubernetes Create container images using Google Cloud Build Store container images in Google Container Registry Create a Kubernetes Engine cluster Kubernetes Operations Work with the kubectl command Inspect the cluster and Pods View a Pods console output Sign in to a Pod interactively Deployments, Jobs, and Scaling Create and use Deployments Create and run Jobs and CronJobs Scale clusters manually and automatically Configure Node and Pod affinity Get software into your cluster with Helm charts and Kubernetes Marketplace GKE Networking Create Services to expose applications that are running within Pods Use load balancers to expose Services to external clients Create Ingress resources for HTTP(S) load balancing Leverage container-native load balancing to improve Pod load balancing Define Kubernetes network policies to allow and block traffic to pods Persistent Data and Storage Use Secrets to isolate security credentials Use ConfigMaps to isolate configuration artifacts Push out and roll back updates to Secrets and ConfigMaps Configure Persistent Storage Volumes for Kubernetes Pods Use StatefulSets to ensure that claims on persistent storage volumes persist across restarts Access Control and Security in Kubernetes and Kubernetes Engine Understand Kubernetes authentication and authorization Define Kubernetes RBAC roles and role bindings for accessing resources in namespaces Define Kubernetes RBAC cluster roles and cluster role bindings for accessing cluster-scoped resources Define Kubernetes pod security policies Understand the structure of GCP IAM Define IAM roles and policies for Kubernetes Engine cluster administration Logging and Monitoring Use Stackdriver to monitor and manage availability and performance Locate and inspect Kubernetes logs Create probes for wellness checks on live applications Using GCP Managed Storage Services from Kubernetes Applications Understand pros and cons for using a managed storage service versus self-managed containerized storage Enable applications running in GKE to access GCP storage services Understand use cases for Cloud Storage, Cloud SQL, Cloud Spanner, Cloud Bigtable, Cloud Firestore, and Bigquery from within a Kubernetes application

Duration 4 Days 24 CPD hours This course is intended for This course is geared for attendees with Intermediate IT skills who wish to learn Computer Vision with tensor flow 2 Overview This 'skills-centric' course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course. Working in a hands-on learning environment, led by our Computer Vision expert instructor, students will learn about and explore how to Build, train, and serve your own deep neural networks with TensorFlow 2 and Keras Apply modern solutions to a wide range of applications such as object detection and video analysis Run your models on mobile devices and web pages and improve their performance. Create your own neural networks from scratch Classify images with modern architectures including Inception and ResNet Detect and segment objects in images with YOLO, Mask R-CNN, and U-Net Tackle problems faced when developing self-driving cars and facial emotion recognition systems Boost your application's performance with transfer learning, GANs, and domain adaptation Use recurrent neural networks (RNNs) for video analysis Optimize and deploy your networks on mobile devices and in the browser Computer vision solutions are becoming increasingly common, making their way into fields such as health, automobile, social media, and robotics. Hands-On Computervision with TensorFlow 2 is a hands-on course that thoroughly explores TensorFlow 2, the brand-new version of Google's open source framework for machine learning. You will understand how to benefit from using convolutional neural networks (CNNs) for visual tasks. This course begins with the fundamentals of computer vision and deep learning, teaching you how to build a neural network from scratch. You will discover the features that have made TensorFlow the most widely used AI library, along with its intuitive Keras interface. You'll then move on to building, training, and deploying CNNs efficiently. Complete with concrete code examples, the course demonstrates how to classify images with modern solutions, such as Inception and ResNet, and extract specific content using You Only Look Once (YOLO), Mask R-CNN, and U-Net. You will also build generative adversarial networks (GANs) and variational autoencoders (VAEs) to create and edit images, and long short-term memory networks (LSTMs) to analyze videos. In the process, you will acquire advanced insights into transfer learning, data augmentation, domain adaptation, and mobile and web deployment, among other key concepts. Computer Vision and Neural Networks Computer Vision and Neural Networks Technical requirements Computer vision in the wild A brief history of computer vision Getting started with neural networks TensorFlow Basics and Training a Model TensorFlow Basics and Training a Model Technical requirements Getting started with TensorFlow 2 and Keras TensorFlow 2 and Keras in detail The TensorFlow ecosystem Modern Neural Networks Modern Neural Networks Technical requirements Discovering convolutional neural networks Refining the training process Influential Classification Tools Influential Classification Tools Technical requirements Understanding advanced CNN architectures Leveraging transfer learning Object Detection Models Object Detection Models Technical requirements Introducing object detection A fast object detection algorithm ? YOLO Faster R-CNN ? a powerful object detection model Enhancing and Segmenting Images Enhancing and Segmenting Images Technical requirements Transforming images with encoders-decoders Understanding semantic segmentation Training on Complex and Scarce Datasets Training on Complex and Scarce Datasets Technical requirements Efficient data serving How to deal with data scarcity Video and Recurrent Neural Networks Video and Recurrent Neural Networks Technical requirements Introducing RNNs Classifying videos Optimizing Models and Deploying on Mobile Devices Optimizing Models and Deploying on Mobile Devices Technical requirements Optimizing computational and disk footprints On-device machine learning Example app ? recognizing facial expressions