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Duration 3 Days 18 CPD hours This course is intended for This course is intended for: DevOps engineers DevOps architects Operations engineers System administrators Developers Overview In this course, you will learn to: Use DevOps best practices to develop, deliver, and maintain applications and services at high velocity on AWS List the advantages, roles and responsibilities of small autonomous DevOps teams Design and implement an infrastructure on AWS that supports DevOps development projects Leverage AWS Cloud9 to write, run and debug your code Deploy various environments with AWS CloudFormation Host secure, highly scalable, and private Git repositories with AWS CodeCommit Integrate Git repositories into CI/CD pipelines Automate build, test, and packaging code with AWS CodeBuild Securely store and leverage Docker images and integrate them into your CI/CD pipelines Build CI/CD pipelines to deploy applications on Amazon EC2, serverless applications, and container-based applications Implement common deployment strategies such as 'all at once,' 'rolling,' and 'blue/green' Integrate testing and security into CI/CD pipelines Monitor applications and environments using AWS tools and technologies DevOps Engineering on AWS teaches you how to use the combination of DevOps cultural philosophies, practices, and tools to increase your organization?s ability to develop, deliver, and maintain applications and services at high velocity on AWS. This course covers Continuous Integration (CI), Continuous Delivery (CD), infrastructure as code, microservices, monitoring and logging, and communication and collaboration. Hands-on labs give you experience building and deploying AWS CloudFormation templates and CI/CD pipelines that build and deploy applications on Amazon Elastic Compute Cloud (Amazon EC2), serverless applications, and container-based applications. Labs for multi-pipeline workflows and pipelines that deploy to multiple environments are also included. Module 0: Course overview Course objective Suggested prerequisites Course overview breakdown Module 1: Introduction to DevOps What is DevOps? The Amazon journey to DevOps Foundations for DevOps Module 2: Infrastructure automation Introduction to Infrastructure Automation Diving into the AWS CloudFormation template Modifying an AWS CloudFormation template Demonstration: AWS CloudFormation template structure, parameters, stacks, updates, importing resources, and drift detection Module 3: AWS toolkits Configuring the AWS CLI AWS Software Development Kits (AWS SDKs) AWS SAM CLI AWS Cloud Development Kit (AWS CDK) AWS Cloud9 Demonstration: AWS CLI and AWS CDK Hands-on lab: Using AWS CloudFormation to provision and manage a basic infrastructure Module 4: Continuous integration and continuous delivery (CI/CD) with development tools CI/CD Pipeline and Dev Tools Demonstration: CI/CD pipeline displaying some actions from AWS CodeCommit, AWS CodeBuild, AWS CodeDeploy and AWS CodePipeline Hands-on lab: Deploying an application to an EC2 fleet using AWS CodeDeploy AWS CodePipeline Demonstration: AWS integration with Jenkins Hands-on lab: Automating code deployments using AWS CodePipeline Module 5: Introduction to Microservices Introduction to Microservices Module 6: DevOps and containers Deploying applications with Docker Amazon Elastic Container Service and AWS Fargate Amazon Elastic Container Registry and Amazon Elastic Kubernetes service Demonstration: CI/CD pipeline deployment in a containerized application Module 7: DevOps and serverless computing AWS Lambda and AWS Fargate AWS Serverless Application Repository and AWS SAM AWS Step Functions Demonstration: AWS Lambda and characteristics Demonstration: AWS SAM quick start in AWS Cloud9 Hands-on lab: Deploying a serverless application using AWS Serverless Application Model (AWS SAM) and a CI/CD Pipeline Module 8: Deployment strategies Continuous Deployment Deployments with AWS Services Module 9: Automated testing Introduction to testing Tests: Unit, integration, fault tolerance, load, and synthetic Product and service integrations Module 10: Security automation Introduction to DevSecOps Security of the Pipeline Security in the Pipeline Threat Detection Tools Demonstration: AWS Security Hub, Amazon GuardDuty, AWS Config, and Amazon Inspector Module 11: Configuration management Introduction to the configuration management process AWS services and tooling for configuration management Hands-on lab: Performing blue/green deployments with CI/CD pipelines and Amazon Elastic Container Service (Amazon ECS) Module 12: Observability Introduction to observability AWS tools to assist with observability Hands-on lab: Using AWS DevOps tools for CI/CD pipeline automations Module 13: Reference architecture (Optional module) Reference architectures Module 14: Course summary Components of DevOps practice CI/CD pipeline review AWS Certification

Duration 1 Days 6 CPD hours This course is intended for This course is intended for: Developers System Administrators Solutions Architects Overview This course is designed to teach you how to: Design a microservices-based architecture that uses containers Use Amazon ECS to run and scale a microservices-based application Integrate Amazon ECS with other AWS services Running Container-Enabled Microservices on AWS is designed to teach you how to manage and scale container-enabled applications by using Amazon Elastic Container Service (ECS). This course highlights the challenges of running containerized applications at scale and provides guidance on creating and using Amazon ECS to develop and deploy containerized microservices-based applications. In the hands-on lab exercises you will use Amazon ECS to handle long-running services, build and deploy container images, link services together, and scale capacity to meet demand. You will also learn how to run container workers for asynchronous application processes. Module 1a: Overview of Microservices on AWS Welcome to Simple Mustache Service! The monolith What are microservices? How to implement a microservices infrastructure The six principles of microservices Module 1b: Containers and Docker Introduction to containers Comparing virtual machines with containers Docker Running containers Storing container images Hands-on lab: Building and running your first container Module 2: Continuous delivery for container-based microservices Compare and contrast different software development cycles Use AWS CodePipeline to code, build, and deploy a microservice Use AWS CodeCommit as a source control service Use Jenkins to perform a Docker build Use Postman to run and test microservices Use AWS CloudFormation to provision and deploy microservices Hands-on lab: Using the Amazon ECS Service Scheduler Module 3: High availability and scaling with Amazon Elastic Container Service High availability Cluster management and scheduling Monitoring Scaling a cluster Scaling services Hands-on lab: Continuous delivery pipelines for container-based microservices Module 4: Security for container-based microservices Implement security Apply best practices Automate security Evaluate compliance requirements Embed security into the CI/CD Hands-on lab: Extending Amazon ECS with Service Discovery and Config Management Additional course details: Nexus Humans Running Container Enabled Microservices on AWS 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 Running Container Enabled Microservices on AWS 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: Individuals planning to deploy applications and create application environments on Google Cloud Developers, systems operations professionals, and solution architects getting started with Google Cloud. Executives and business decision makers evaluating the potential of Google Cloud to address their business needs. Overview This course teaches participants the following skills: Identify Google Cloud counterparts for AWS IaaS, AWS PaaS, AWS SQL, AWS Blob Storage, AWS Application Insights, and AWS Data Lake Configure accounts, billing, projects, networks, subnets, firewalls, VMs, disks, auto scaling, load balancing, storage, databases, IAM, and more Manage and monitor applications Explain feature and pricing model differences This course with labs introduces AWS professionals to the core capabilities of Google Cloud Platform (GCP) in the four technology pillars: networking, compute, storage, and database. It is designed for AWS Solution Architects and SysOps Administrators familiar with AWS features and setup and want to gain experience configuring GCP products immediately. With presentations, demos, and hands-on labs, participants will get details of similarities, differences, and initial how-tos quickly. Introducing Google Cloud Explain the advantages of Google Cloud. Define the components of Google's network infrastructure,including: Points of presence, data centers, regions, and zones. Understand the difference between Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS) Getting Started with Google Cloud Identify the purpose of projects on Google Cloud Platform. Understand how AWS?s resource hierarchy differs from Google Cloud?s. Understand the purpose of and use cases for Identity and Access Management. Understand how AWS IAM differs from Google Cloud IAM. List the methods of interacting with Google Cloud Platform. Launch a solution using Cloud Marketplace. Virtual Machines in the Cloud Identify the purpose and use cases for Google Compute Engine. Understand the basics of networking in Google Cloud Platform. Understand how Amazon VPC differs from Google VPC. Understand the similarities and differences between Amazon EC2 and Google Compute Engine. Understand how typical approaches to load-balancing in Google Cloud differ from those in AWS. Deploy applications using Google Compute Engine. Storage in the Cloud Understand the purpose of and use cases for: Cloud Storage,Cloud SQL, Cloud Bigtable and Cloud Datastore. Understand how Amazon S3 and Amazon Glacier compare to Cloud Storage. Compare Google Cloud?s managed database services with Amazon RDS and Amazon Aurora. Learn how to choose among the various storage options on Google Cloud Platform. Load data from Cloud Storage into BigQuery. Perform a query on the data in BigQuery. Containers in the Cloud Define the concept of a container and identify uses for containers. Identify the purpose of and use cases for Google Container Engine and Kubernetes. Understand how Amazon Elastic Container Service (ECS) and Amazon Elastic Kubernetes Service (EKS) differ from GKE. Provision a Kubernetes cluster using Kubernetes Engine. Deploy and manage Docker containers using kubectl Applications in the Cloud Understand the purpose of and use cases for Google App Engine. Contrast the App Engine Standard environment with the App Engine Flexible environment. Understand how App Engine differs from Amazon Elastic Beanstalk. Understand the purpose of and use cases for Google Cloud Endpoints. Developing, Deploying and Monitoring in the Cloud Understand options for software developers to host their source code. Understand the purpose of template-based creation and management of resources. Understand how Cloud Deployment Manager differs from AWS CloudFormation. Understand the purpose of integrated monitoring, alerting, and debugging. Understand how Google Monitoring differs from Amazon CloudWatch and AWS CloudTrail. Create a Deployment Manager deployment. Update a Deployment Manager deployment. View the load on a VM instance using Google Monitoring. Big Data and Machine Learning in the Cloud Understand the purpose of and use cases for the products and services in the Google Cloud big data and machine learning platforms. Understand how Google Cloud BigQuery differs from AWS Data Lake. Understand how Google Cloud Pub/Sub differs from AWS Event Hubs and Service Bus. Understand how Google Cloud?s machine-learning APIs differ from AWS's. Load data into BigQuery from Cloud Storage. Perform queries using BigQuery to gain insight into data. Summary and Review Review the products that make up Google Cloud and remember how to choose among them Understand next steps for training and certification Understand, at a high level, the process of migrating from AWS to Google Cloud. Additional course details: Nexus Humans Google Cloud Fundamentals for AWS Professionals 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 Google Cloud Fundamentals for AWS Professionals 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 course is intended for: Developers Solutions Architects Data Engineers Anyone with little to no experience with ML and wants to learn about the ML pipeline using Amazon SageMaker Overview In this course, you will learn to: Select and justify the appropriate ML approach for a given business problem Use the ML pipeline to solve a specific business problem Train, evaluate, deploy, and tune an ML model using Amazon SageMaker Describe some of the best practices for designing scalable, cost-optimized, and secure ML pipelines in AWS Apply machine learning to a real-life business problem after the course is complete This course explores how to use the machine learning (ML) pipeline to solve a real business problem in a project-based learning environment. Students will learn about each phase of the pipeline from instructor presentations and demonstrations and then apply that knowledge to complete a project solving one of three business problems: fraud detection, recommendation engines, or flight delays. By the end of the course, students will have successfully built, trained, evaluated, tuned, and deployed an ML model using Amazon SageMaker that solves their selected business problem. Module 0: Introduction Pre-assessment Module 1: Introduction to Machine Learning and the ML Pipeline Overview of machine learning, including use cases, types of machine learning, and key concepts Overview of the ML pipeline Introduction to course projects and approach Module 2: Introduction to Amazon SageMaker Introduction to Amazon SageMaker Demo: Amazon SageMaker and Jupyter notebooks Hands-on: Amazon SageMaker and Jupyter notebooks Module 3: Problem Formulation Overview of problem formulation and deciding if ML is the right solution Converting a business problem into an ML problem Demo: Amazon SageMaker Ground Truth Hands-on: Amazon SageMaker Ground Truth Practice problem formulation Formulate problems for projects Module 4: Preprocessing Overview of data collection and integration, and techniques for data preprocessing and visualization Practice preprocessing Preprocess project data Class discussion about projects Module 5: Model Training Choosing the right algorithm Formatting and splitting your data for training Loss functions and gradient descent for improving your model Demo: Create a training job in Amazon SageMaker Module 6: Model Evaluation How to evaluate classification models How to evaluate regression models Practice model training and evaluation Train and evaluate project models Initial project presentations Module 7: Feature Engineering and Model Tuning Feature extraction, selection, creation, and transformation Hyperparameter tuning Demo: SageMaker hyperparameter optimization Practice feature engineering and model tuning Apply feature engineering and model tuning to projects Final project presentations Module 8: Deployment How to deploy, inference, and monitor your model on Amazon SageMaker Deploying ML at the edge Demo: Creating an Amazon SageMaker endpoint Post-assessment Course wrap-up Additional course details: Nexus Humans The Machine Learning Pipeline on AWS 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 The Machine Learning Pipeline on AWS 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: Solutions architects, cloud engineers, including security engineers, delivery and implementation engineers, professional services, and Cloud Center of Excellence (CCOE) Overview In this course, you will learn to: Design and implement a secure network infrastructure Design and implement compute security Design and implement a logging solution Currently, the average cost of a security breach can be upwards of $4 million. AWS Security Best Practices provides an overview of some of the industry best practices for using AWS security and control types. This course helps you understand your responsibilities while providing valuable guidelines for how to keep your workload safe and secure. You will learn how to secure your network infrastructure using sound design options. You will also learn how you can harden your compute resources and manage them securely. Finally, by understanding AWS monitoring and alerting, you can detect and alert on suspicious events to help you quickly begin the response process in the event of a potential compromise. Module 1: AWS Security Overview Shared responsibility model Customer challenges Frameworks and standards Establishing best practices Compliance in AWS Module 2: Securing the Network Flexible and secure Security inside the Amazon Virtual Private Cloud (Amazon VPC) Security services Third-party security solutions Module 3: Amazon EC2 Security Compute hardening Amazon Elastic Block Store (EBS) encryption Secure management and maintenance Detecting vulnerabilities Using AWS Marketplace Module 4: Monitoring and Alerting Logging network traffic Logging user and Application Programming Interface (API) traffic Visibility with Amazon CloudWatch Enhancing monitoring and alerting Verifying your AWS environment Additional course details: Nexus Humans AWS Security Best Practices 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 AWS Security Best Practices 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: Data platform engineers Solutions architects IT professionals Overview In this course, you will learn to: Apply data lake methodologies in planning and designing a data lake Articulate the components and services required for building an AWS data lake Secure a data lake with appropriate permission Ingest, store, and transform data in a data lake Query, analyze, and visualize data within a data lake In this course, you will learn how to build an operational data lake that supports analysis of both structured and unstructured data. You will learn the components and functionality of the services involved in creating a data lake. You will use AWS Lake Formation to build a data lake, AWS Glue to build a data catalog, and Amazon Athena to analyze data. The course lectures and labs further your learning with the exploration of several common data lake Introduction to data lakes Describe the value of data lakes Compare data lakes and data warehouses Describe the components of a data lake Recognize common architectures built on data lakes Data ingestion, cataloging, and preparation Describe the relationship between data lake storage and data ingestion Describe AWS Glue crawlers and how they are used to create a data catalog Identify data formatting, partitioning, and compression for efficient storage and query Lab 1: Set up a simple data lake Data processing and analytics Recognize how data processing applies to a data lake Use AWS Glue to process data within a data lake Describe how to use Amazon Athena to analyze data in a data lake Building a data lake with AWS Lake Formation Describe the features and benefits of AWS Lake Formation Use AWS Lake Formation to create a data lake Understand the AWS Lake Formation security model Lab 2: Build a data lake using AWS Lake Formation Additional Lake Formation configurations Automate AWS Lake Formation using blueprints and workflows Apply security and access controls to AWS Lake Formation Match records with AWS Lake Formation FindMatches Visualize data with Amazon QuickSight Lab 3: Automate data lake creation using AWS Lake Formation blueprints Lab 4: Data visualization using Amazon QuickSight Architecture and course review Post course knowledge check Architecture review Course review

Duration 3 Days 18 CPD hours This course is intended for Experienced software developers who are already familiar with AWS services Overview In this course, you will learn how to: Analyze a monolithic application architecture to determine logical or programmatic break points where the application can be broken up across different AWS services Apply Twelve-Factor Application manifesto concepts and steps while migrating from a monolithic architecture Recommend the appropriate AWS services to develop a microservices based cloud-native application Use the AWS API, CLI, and SDKs to monitor and manage AWS services Migrate a monolithic application to a microservices application using the 6 Rs of migration Explain the SysOps and DevOps interdependencies necessary to deploy a microservices application in AWS The Advanced Developing on AWS course uses the real-world scenario of taking a legacy, on-premises monolithic application and refactoring it into a serverless microservices architecture. This three-day advanced course covers advanced development topics such as architecting for a cloud-native environment; deconstructing on-premises, legacy applications and repackaging them into cloud-based, cloud-native architectures; and applying the tenets of the Twelve-Factor Application methodology. Module 1: The cloud journey Common off-cloud architecture Introduction to Cloud Air Monolithic architecture Migration to the cloud Guardrails The six R?s of migration The Twelve-Factor Application Methodology Architectural styles and patterns Overview of AWS Services Interfacing with AWS Services Authentication Infrastructure as code and Elastic Beanstalk Demonstration: Walk through creating base infrastructure with AWS CloudFormation in the AWS console Hands-on lab 1: Deploy your monolith application using AWS Elastic Beanstalk Module 2: Gaining Agility DevOps CI/CD Application configuration Secrets management CI/CD Services in AWS Demonstration: Demo AWS Secrets Manager Module 3: Monolith to MicroServices Microservices Serverless A look at Cloud Air Microservices using Lambda and API Gateway SAM Strangling the Monolith Hands-on lab: Using AWS Lambda to develop microservices Module 4: Polyglot Persistence & Distributed Complexity Polyglot persistence DynamoDB best practices Distributed complexity Steps functions Module 5: Resilience and Scale Decentralized data stores Amazon SQS Amazon SNS Amazon Kinesis Streams AWS IoT Message Broker Serverless event bus Event sourcing and CQRS Designing for resilience in the cloud Hands-on lab: Exploring the AWS messaging options Module 6: Security and Observability Serverless Compute with AWS Lambda Authentication with Amazon Cognito Debugging and traceability Hands-on lab: Developing microservices on AWS Additional course details: Nexus Humans Advanced Developing on AWS 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 Advanced Developing on AWS 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: Data platform engineers Solutions architects IT professionals Overview In this course, you will learn to: Apply data lake methodologies in planning and designing a data lake Articulate the components and services required for building an AWS data lake Secure a data lake with appropriate permission Ingest, store, and transform data in a data lake Query, analyze, and visualize data within a data lake In this course, you will learn how to build an operational data lake that supports analysis of both structured and unstructured data. You will learn the components and functionality of the services involved in creating a data lake. You will use AWS Lake Formation to build a data lake, AWS Glue to build a data catalog, and Amazon Athena to analyze data. The course lectures and labs further your learning with the exploration of several common data lake architectures. Module 1: Introduction to data lakes Describe the value of data lakes Compare data lakes and data warehouses Describe the components of a data lake Recognize common architectures built on data lakes Module 2: Data ingestion, cataloging, and preparation Describe the relationship between data lake storage and data ingestion Describe AWS Glue crawlers and how they are used to create a data catalog Identify data formatting, partitioning, and compression for efficient storage and query Lab 1: Set up a simple data lake Module 3: Data processing and analytics Recognize how data processing applies to a data lake Use AWS Glue to process data within a data lake Describe how to use Amazon Athena to analyze data in a data lake Module 4: Building a data lake with AWS Lake Formation Describe the features and benefits of AWS Lake Formation Use AWS Lake Formation to create a data lake Understand the AWS Lake Formation security model Lab 2: Build a data lake using AWS Lake Formation Module 5: Additional Lake Formation configurations Automate AWS Lake Formation using blueprints and workflows Apply security and access controls to AWS Lake Formation Match records with AWS Lake Formation FindMatches Visualize data with Amazon QuickSight Lab 3: Automate data lake creation using AWS Lake Formation blueprints Lab 4: Data visualization using Amazon QuickSight Module 6: Architecture and course review Post course knowledge check Architecture review Course review Additional course details: Nexus Humans Building Data Lakes on AWS training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Building Data Lakes on AWS 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 intended for: Solutions architects and cloud architects seeking their AWS Certified Solutions Architect - Associate certification Customers and APN Partners who have 6 to 12 months of experience with AWS including a strong architecture background and experience Individuals who prefer an instructor led course for training and exam readiness Individuals who have not taken the Architecting on AWS course in the last ~6 months Overview In this course, you will learn to: Make architectural decisions based on AWS architectural principles and best practices Leverage AWS services to make your infrastructure scalable, reliable, and highly available Leverage AWS Managed Services to enable greater flexibility and resiliency in an infrastructure Make an AWS-based infrastructure more efficient to increase performance and reduce costs Use the Well-Architected Framework to improve architectures with AWS solutions Navigate the logistics of the examination process, exam structure, and question types Identify how questions relate to AWS architectural concepts Interpret the concepts being tested by an exam question This five-day, instructor-led course helps busy architects get away from the day-to-day to get focused and ready for their AWS Certified Solutions Architect ? Associate exam. Attendees learn the fundamentals of building IT infrastructure on AWS, so they can build scalable and resilient solutions in the cloud, by spending the first 3 days on the Architecting on AWS course. They?ll start getting in the exam readiness mindset with bonus end of module quizzes. Next, they?ll learn strategies to answer exam questions and avoid common mistakes with the Exam Readiness: AWS Certified Solutions Architect ? Associate half-day course. The course broadens attendees? knowledge of AWS services with deep dives into Amazon Redshift, Amazon Kinesis, and AWS Key Management Service, and then concludes with two quizzes and an instructor guided review of the AWS Certified Solutions Architect ? Associate practice exam. Architecting on AWS Module 1: Introduction Module 2: The Simplest Architectures Hands-On Lab 1: Hosting a Static Website Module 3: Adding a Compute Layer Module 4: Adding a Database Layer Hands-On Lab 2: Deploying a Web Application on AWS Module 5: Networking in AWS Part 1 Hands-On Lab 3: Creating a Virtual Private Cloud Architecting on AWS (continued) Module 6: Networking in AWS Part 2 Module 7: AWS Identity and Access Management (IAM) Module 8: Elasticity, High Availability, and Monitoring Hands-On Lab 4: Creating a Highly Available Environment Module 9: Automation Hands-On Lab 5: Automating Infrastructure Deployment with AWS CloudFormation Module 10: Caching Architecting on AWS (continued) Module 11: Building Decoupled Architectures Module 12: Microservices and Serverless Architectures Hands-On Lab 6: Implementing a Serverless Architecture with AWS Managed Services Module 13: RTP/RPO and Backup Recovery Setup Module 14: Optimizations and Review Exam Readiness: AWS Certified Solutions Architect -- Associate Module 0: The Exam Module 1: Design Resilient Architectures Module 2: Design Performant Architectures Module 3: Specify Secure Applications and Architectures Module 4: Design Cost-Optimized Architectures Module 5: Define Operationally Excellent Architectures Exam Readiness Additional deep dive of AWS services Quiz #1 Practice exam: AWS Certified Solutions Architect ? Associate Quiz #2 Wrap-up

Duration 1 Days 6 CPD hours This course is intended for This course is intended for: Developers responsible for developing Deep Learning applications Developers who want to understand concepts behind Deep Learning and how to implement a Deep Learning solution on AWS Overview This course is designed to teach you how to: Define machine learning (ML) and deep learning Identify the concepts in a deep learning ecosystem Use Amazon SageMaker and the MXNet programming framework for deep learning workloads Fit AWS solutions for deep learning deployments In this course, you?ll learn about AWS?s deep learning solutions, including scenarios where deep learning makes sense and how deep learning works. You?ll learn how to run deep learning models on the cloud using Amazon SageMaker and the MXNet framework. You?ll also learn to deploy your deep learning models using services like AWS Lambda while designing intelligent systems on AWS. Module 1: Machine learning overview A brief history of AI, ML, and DL The business importance of ML Common challenges in ML Different types of ML problems and tasks AI on AWS Module 2: Introduction to deep learning Introduction to DL The DL concepts A summary of how to train DL models on AWS Introduction to Amazon SageMaker Hands-on lab: Spinning up an Amazon SageMaker notebook instance and running a multi-layer perceptron neural network model Module 3: Introduction to Apache MXNet The motivation for and benefits of using MXNet and Gluon Important terms and APIs used in MXNet Convolutional neural networks (CNN) architecture Hands-on lab: Training a CNN on a CIFAR-10 dataset Module 4: ML and DL architectures on AWS AWS services for deploying DL models (AWS Lambda, AWS IoT Greengrass, Amazon ECS, AWS Elastic Beanstalk) Introduction to AWS AI services that are based on DL (Amazon Polly, Amazon Lex, Amazon Rekognition) Hands-on lab: Deploying a trained model for prediction on AWS Lambda Additional course details: Nexus Humans Deep Learning on AWS 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 Deep Learning on AWS 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.