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Duration 2 Days 12 CPD hours This course is intended for This course is intended for Business Leaders, including managers/supervisors in the following roles: Developer Architect Video Operator Overview In this course, you will learn to: Articulate the essential terms and concepts fundamental to video compression and distribution Describe the four fundamental stages of video streaming workflows: ingest, process, store and deliver Explain the importance of security in the AWS Cloud and how it is applied in video streaming workflows Analyze video streaming workflow diagrams using AWS services, based on simple to complex use cases Describe some of the key variables that influence workflow decisions Recognize how other AWS services for compliance, storage, and compute, interact with AWS Media Services in video streaming workflows and the functions they perform Describe strategies to test or prototype workflows to mitigate risk and cost impacts and optimize video streaming workflows Use the AWS Management Console to build and run simple video streaming workflows for live and video-on-demand content Recognize the automation and data analytics available for Media Services when used with AWS AI and explore media-specific use cases for these services Identify the next steps in exploring migration to the cloud for one or more Media Services This course covers the media and cloud fundamentals that will empower you to develop a cloud migration strategy for media workflows in support of business goals. The course covers important concepts related to video processing and delivery, the variables that can impact migration decisions, and real-world examples of hybrid and cloud use cases for AWS Media Services. It also introduces security, artificial intelligence, and analytics concepts to help you consider how AWS Media Services fit into your overall cloud strategy. Module 1: Important video concepts Video Metrics Video Compression Video Distribution Major Protocols Used in Video Streaming Module 2: Anatomy of streaming workflows Ingest Process Store Deliver Module 3: Using AWS services in media workflows video-on-demand (VOD) Introduction to AWS Media Services Security Variables Impacting Workflow Design VOD Simple Use Cases VOD Advanced Use Cases Lab 1: Build and run a simple video streaming workflow for VOD content Module 4: Using AWS services in media workflows live streaming Challenges of Live Streaming Live Streaming Simple Use Cases Live Streaming Advanced Use Cases Lab 2: Build and run a simple video streaming workflow for live content Module 5: Optimizing Workflows Cost Considerations Mitigating Risk Monitoring and Automation Exploring Migration Options Additional course details: Nexus Humans AWS Media Essentials for IT Business Decision Makers 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 Media Essentials for IT Business Decision Makers 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: 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 3.5 Days 21 CPD hours This course is intended for This course is for AWS Cloud Architects with expertise in designing and implementing solutions running on AWS who now want to design for Microsoft Azure. Overview After completing this course, students will be able to: Secure identities with Azure Active Directory and users and groups. Implement identity solutions spanning on-premises and cloud-based capabilities Apply monitoring solutions for collecting, combining, and analyzing data from different sources. Manage subscriptions, accounts, Azure policies, and Role-Based Access Control. Administer Azure using the Resource Manager, Azure portal, Cloud Shell, and CLI. Configure intersite connectivity solutions like VNet Peering, and virtual network gateways. Administer Azure App Service, Azure Container Instances, and Kubernetes. This course teaches Solutions Architects who have previously designed for Amazon Web Services how to translate business requirements into secure, scalable, and reliable solutions for Azure. Introduction to Azure Subscriptions and accounts Resource groups and templates in Azure Resource Manager Azure global infrastructure Azure regions Azure Availability Zones Comparison with AWS Implement Azure Active Directory Introduction to Azure Active Directory Domains and custom domains Safety features Guest users in Azure Active Directory Manage multiple directories Comparison with AWS Implement and manage hybrid identities Introduction to Azure AD Connect Comparison with AWS Implement virtual networking Azure Virtual Network and VNet peering VPN and ExpressRoute connections Comparison with AWS Implement VMs for Windows and Linux Configure high availability Comparison with AWS Implement load balancing and network security Implement Azure Load Balancer Implement an Azure Application Gateway Implement Azure Firewall Implement network security groups and application security groups Comparison with AWS Implement container-based applications Configure Azure Kubernetes Service Publish a solution on an Azure Container Instance Comparison with AWS Implement an application infrastructure Create an App Service plan Create and configure Azure App Service Configure networking for an App Service Introduction to Logic Apps and Azure Functions Comparison with AWS Implement storage accounts Azure Storage core concepts Managing the Azure Blob storage lifecycle Working with Azure Blob storage Comparison with AWS Implement NoSQL databases Introduction to Azure Cosmos DB Consistency Select appropriate CosmosDB APIs Set up replicas in CosmosDB Comparison with AWS DynamoDB Implement Azure SQL databases Configure Azure SQL database settings Implement Azure SQL Database managed instances Configure high availability for an Azure SQL database Comparison with AWS Implement cloud infrastructure monitoring Monitor security Monitor cost Configure a Log Analytics workspace Comparison with AWS Implement and manage Azure governance solutions Assign RBAC roles Configure management access to Azure Implement and configure an Azure Policy Comparison with AWS Manage security for applications Implement Azure Key Vault Implement and configure Azure AD Managed Identities Register and manage applications in Azure AD Comparison with AWS Migration, backup, and disaster recovery management Migrate workloads Implement Azure Backup for VMs Implement disaster recovery Comparison with AWS

Duration 3 Days 18 CPD hours This course is intended for Data platform engineers Database administrators Solutions architects IT professionals Overview Apply database concepts, database management, and data modeling techniques Evaluate hosting databases on Amazon EC2 instances Evaluate relational database services (Amazon RDS, Amazon Aurora, and Amazon Redshift) and their features Evaluate nonrelational database services (Amazon DocumentDB, Amazon DynamoDB, Amazon ElastiCache, Amazon Neptune, and Amazon QLDB) and their features Examine how the design criteria apply to each service Apply management principles based on the unique features of each service This course will teach you the process of planning and designing both relational and nonrelational database and the planning and design requirements of all 8 of the AWS databases services, their pros and cons, and how to know which AWS databases service is right for your workloads. Day 1 Module 0: Planning and Designing Databases on AWS Module 1: Database Concepts and General Guidelines Module 2: Database Planning and Design Module 3: Databases on Amazon EC2 Module 4: Purpose-Built Databases Module 5: Databases on Amazon RDS Databases in Amazon Aurora Day 2 Module 6: Databases in Amazon Aurora (continued) Module 7: Databases in Amazon DocumentDB (with MongoDB compatibility) Module 8: Amazon DynamoDB Tables Day 3 Module 9: Databases in Amazon Neptune Module 10: Databases in Amazon Quantum Ledger Database (Amazon QLDB) Module 11: Databases in Amazon ElastiCache Module 12: Data Warehousing in Amazon Redshift Module 13: Course Review Additional course details: Nexus Humans Planning and Designing Databases 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 Planning and Designing Databases 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 Architects and operators who build and manage data analytics pipelines Overview In this course, you will learn to: Compare the features and benefits of data warehouses, data lakes, and modern data architectures Design and implement a batch data analytics solution Identify and apply appropriate techniques, including compression, to optimize data storage Select and deploy appropriate options to ingest, transform, and store data Choose the appropriate instance and node types, clusters, auto scaling, and network topology for a particular business use case Understand how data storage and processing affect the analysis and visualization mechanisms needed to gain actionable business insights Secure data at rest and in transit Monitor analytics workloads to identify and remediate problems Apply cost management best practices In this course, you will learn to build batch data analytics solutions using Amazon EMR, an enterprise-grade Apache Spark and Apache Hadoop managed service. You will learn how Amazon EMR integrates with open-source projects such as Apache Hive, Hue, and HBase, and with AWS services such as AWS Glue and AWS Lake Formation. The course addresses data collection, ingestion, cataloging, storage, and processing components in the context of Spark and Hadoop. You will learn to use EMR Notebooks to support both analytics and machine learning workloads. You will also learn to apply security, performance, and cost management best practices to the operation of Amazon EMR. Module A: Overview of Data Analytics and the Data Pipeline Data analytics use cases Using the data pipeline for analytics Module 1: Introduction to Amazon EMR Using Amazon EMR in analytics solutions Amazon EMR cluster architecture Interactive Demo 1: Launching an Amazon EMR cluster Cost management strategies Module 2: Data Analytics Pipeline Using Amazon EMR: Ingestion and Storage Storage optimization with Amazon EMR Data ingestion techniques Module 3: High-Performance Batch Data Analytics Using Apache Spark on Amazon EMR Apache Spark on Amazon EMR use cases Why Apache Spark on Amazon EMR Spark concepts Interactive Demo 2: Connect to an EMR cluster and perform Scala commands using the Spark shell Transformation, processing, and analytics Using notebooks with Amazon EMR Practice Lab 1: Low-latency data analytics using Apache Spark on Amazon EMR Module 4: Processing and Analyzing Batch Data with Amazon EMR and Apache Hive Using Amazon EMR with Hive to process batch data Transformation, processing, and analytics Practice Lab 2: Batch data processing using Amazon EMR with Hive Introduction to Apache HBase on Amazon EMR Module 5: Serverless Data Processing Serverless data processing, transformation, and analytics Using AWS Glue with Amazon EMR workloads Practice Lab 3: Orchestrate data processing in Spark using AWS Step Functions Module 6: Security and Monitoring of Amazon EMR Clusters Securing EMR clusters Interactive Demo 3: Client-side encryption with EMRFS Monitoring and troubleshooting Amazon EMR clusters Demo: Reviewing Apache Spark cluster history Module 7: Designing Batch Data Analytics Solutions Batch data analytics use cases Activity: Designing a batch data analytics workflow Module B: Developing Modern Data Architectures on AWS Modern data architectures

Duration 0.5 Days 3 CPD hours This course is intended for This course is intended for: Line of Business (LoB) owners, IT leaders, and executives Overview In this course, you will learn to: Explain the role of information technology (IT) in an organization for business transformation Explain the customer value proposition for using the cloud across industries Define key characteristics of cloud computing Explain the cloud business model Identify key security practices of cloud computing Frame the cloud business value using the Cloud Value Framework In this course, you will learn the fundamental concepts of cloud computing and how a cloud strategy can help companies meet business objectives. It explores the advantages and possibilities of cloud computing. It also introduces addresses concepts such as security and compliance to help facilitate better discussions with line of business (LOB) professionals, information technology (IT) LoB, IT leaders, and executives. Module 1: Course Introduction Course Introduction Module 2: Information Technology for Business Transformation Role of IT in an organization for business transformation Brief history of IT Legacy approach to IT What drives customers to move from traditional infrastructure to the cloud Module 3: Cloud Computing Define cloud computing Key characteristics of cloud technology The cloud business model Key security practices within the cloud Module 4: Business Value of the Cloud The customer value proposition Identify who is using cloud computing Industry trends Customer examples Module 5: The Cloud Value Framework Introduction to the Cloud Value Framework Cost Savings Staff Productivity Operational Resilience Business Agility Module 6: Business Value Activity Business Value Activity Additional course details: Nexus Humans AWS Cloud Essentials for Business Leaders 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 Cloud Essentials for Business Leaders 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: 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.

Duration 1 Days 6 CPD hours This course is intended for This course is intended for: IT/Senior managers Solutions architects/Enterprise architects Operations professionals Overview This course teaches you how to: Build your cloud strategy. Develop the hiring plan for your cloud team. Choose and prioritize which applications to move to AWS. Build a migration plan for moving workloads to AWS. Manage your AWS expenditures and internal chargebacks. This course teaches you how to select the right strategy, people, migration plan, and financial management methodology needed when moving your workloads to the cloud. This course provides guidance on how to build a holistic cloud adoption plan and how to hire people who will execute that plan. You will learn best practices for choosing workloads to migrate from your on-premises environment to AWS. In addition, you will also learn best practices for managing your AWS expenses and dealing with internal chargebacks. Building Your Cloud StrategyHiring Your Cloud TeamMigration PlanningCloud Expenditure Management