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Developing Serverless Solutions on AWS

Written by wamrhein on . Posted in AWS Training, Training.

Developing Serverless Solutions on AWS

current course dates can be found at the bottom of this page … company training available on request!

Course description

This course gives developers exposure to and practice with best practices for building serverless applications using AWS Lambda and other services in the AWS serverless platform. You’ll use AWS frameworks to deploy a serverless application in hands-on labs that progress from simpler to more complex topics. You will use AWS documentation throughout the course to develop authentic methods for learning and problem-solving beyond the classroom.

Course objectives

In this course, you will learn to:

  • Apply event-driven best practices to a serverless application design using appropriate AWS services
  • Identify the challenges and trade-offs of transitioning to serverless development, and make recommendations that suit your development organization and environment
  • Build serverless applications using patterns that connect AWS managed services together, and account for service characteristics, including service quotas, available integrations, invocation model, error handling, and event source payload
  • Compare and contrast available options for writing infrastructure as code, including AWS CloudFormation, AWS Amplify, AWS Serverless Application Model (AWS SAM), and AWS Cloud Development Kit (AWS CDK)
  • Apply best practices to writing Lambda functions inclusive of error handling, logging, environmen re-use, using layers, statelessness, idempotency, and configuring concurrency and memory
  • Apply best practices for building observability and monitoring into your serverless application
  • Apply security best practices to serverless applications
  • Identify key scaling considerations in a serverless application, and match each consideration to the methods, tools, or best practices to manage it
  • Use AWS SAM, AWS CDK, and AWS developer tools to configure a CI/CD workflow, and automate deployment of a serverless application
  • Create and actively maintain a list of serverless resources that will assist in your ongoing serverless development and engagement with the serverless community

Intended audience

This course is intended for:

  • Developers who have some familiarity with serverless and experience with development in the AWS Cloud

Prerequisites

We recommend that attendees of this course have:

  • Familiarity with the basics of AWS Cloud architecture
  • An understanding of developing applications on AWS equivalent to completing the “Developing on AWS“
  • Knowledge equivalent to completing the following serverless digital trainings: AWS Lambda Foundations and Amazon API Gateway for Serverless Applications

Activities

This course includes:

  • presentations
  • hands-on labs
  • demonstrations
  • videos
  • knowledge checks
  • group exercises

Course duration / Price

  • 3 days / € 1,995.00 (excl. tax) per person (DE)

Course outline

Day 1

Module 0: Introduction

Module 1: Thinking Serverless

Module 2: API-Driven Development and Synchronous Event Sources

Module 3: Introduction to Authentication, Authorization, and Access Control

Module 4: Serverless Deployment Frameworks

Module 5: Using Amazon EventBridge and Amazon SNS to Decouple Components

Module 6: Event-Driven Development Using Queues and Streams

Hands-On Labs

Day 2

Module 7: Writing Good Lambda Functions

Module 8: Step Functions for Orchestration

Module 9: Observability and Monitoring

Hands-On Labs

Day 3

Module 10: Serverless Application Security

Module 11: Handling Scale in Serverless Applications

Module 12: Automating the Deployment Pipeline

Hands-On Labs

IMPORTANT: Please bring your notebook (Windows, Linux or Mac) to our trainings. If this is not possible, please contact us in advance.

Course materials are in English, on request also in German (if available).
Course language is German, on request also in English.

Video Streaming Essentials für AWS Media Services

Written by wamrhein on . Posted in AWS Training, Training.

Video Streaming Essentials für AWS Media Services

current course dates can be found at the bottom of this page … company training available on request!

Course description

In this course, you will learn best practices for designing and using cloud-based video workflows. It 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, machine learning, and analytics concepts to help you consider how AWS Media Services fit into your overall cloud strategy.

Course objectives

In this course, you will learn to:

  • Articulate the essential terms and concepts fundamental to making video streaming workflow decisions, including video metrics, compression, distribution, and protocols.
  • Describe the four fundamental stages of video streaming workflows: ingest, process, store, and deliver.
  • Describe which AWS services can be used in each stage of a video streaming workflow, including ingest, processing, storage, and delivery.
  • Analyze video streaming workflow diagrams using AWS services, based on simple to complex use cases.
  • Recognize the key variables that influence workflow decisions.
  • Recognize how AWS services for compliance, storage, and compute interact with AWS Media Services in video streaming workflows and the functions they perform.
  • 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 Machine Learning and explore media-specific use cases for these services.
  • Explain the importance of security in the AWS Cloud and how it is applied in video streaming workflows.

Intended audience

This course is intended for:

  • Business Leaders, including managers/supervisors in the following roles:
    • Video Operator/Engineer
    • Developer
    • Architect
    • Project Manager/Engineer

Prerequisites

We recommend that attendees of this course have:

Activities

This course includes:

  • Instructor-led training
  • hands-on labs
  • video

Course duration / Price

  • 2 days / € 1,295.00 (excl. tax) per person (DE)

Course outline

Day 1

Module 1: Important Video Concepts

  • Resolution, bitrate, frame rate, latency, and compression
  • Codecs and containers
  • Group of pictures (GOP) encoding
  • ABR, packaging and distribution
  • Internet protocols used in video streaming

Module 2: Anatomy of Streaming Workflows

  • Four stages of video streaming
  • Variables that affect design decisions

Module 3: Using AWS Services in Video-on-Demand (VOD) Workflows

  • Converting a film or tape library for internet streaming
  • Increasing reach and accessibility using multiple languages and captions
  • Streaming edited highlights from a live event
  • Analyzing and tagging VOD files for media content analysis using machine learning and data analytics

Day 2
Module 4: Optimizing Workflows

  • Security
  • Migrating to the cloud
  • Cloud financial management

Module 5: Using AWS Services in Live Workflows

  • Challenges of live streaming
  • Live streaming a simple interview show
  • Live streaming a major sporting event to a global audience
  • Live switching between multiple inputs
  • Saving segments from a live show to create VOD segments

Module 6: Recap and Review

Module 7: Next Steps

IMPORTANT: Please bring your notebook (Windows, Linux or Mac) to our trainings. If this is not possible, please contact us in advance.

Course materials are in English, on request also in German (if available).
Course language is German, on request also in English.

AWS Cloud Financial Management for Builders

Written by wamrhein on . Posted in AWS Training, Training.

AWS Cloud Financial Management for Builders

current course dates can be found at the bottom of this page … company training available on request!

Course description

This course is for individuals who seek an understanding of how to manage, optimize, and predict costs as you run workloads on AWS. You learn how to implement architectural best practices, explore cost optimization strategies, and design patterns to help you architect cost-efficient solutions on AWS.

Course objectives

In this course, you will learn to:

  • Explain the cost of core AWS services
  • Determine and predict costs associated with current and future cloud workloads
  • Use strategies and best practices to reduce AWS costs
  • Use AWS tools to manage, monitor, alert, and optimize your AWS spend
  • Apply strategies to monitor service costs and usage
  • Implement governance standards, including resource tagging, account structure, provisioning, permissions, and access

Intended audience

This course is intended for:

  • Solutions architects
  • Developers
  • Cost-optimization leads
  • System administrators

Prerequisites

We recommend that attendees of this course have:

Activities

This course includes:

  • presentations
  • hands-on labs
  • demonstrations

Course duration / Price

  • 3 days / € 1,995.00 (excl. tax) per person (DE)

Course outline

Day 1
Module 0: Couse Overview

  • Course introduction

Module 1: Introduction to Cloud Financial Management

  • Introduction to Cloud Financial Management
  • Four pillars of Cloud Financial Management

Module 2: Resource Tagging

  • Tagging resources
  • Hands-On Lab: Cost optimization: Control Resource Consumption Using Tagging and AWS Config

Module 3: Pricing and Cost

  • Fundamentals of pricing
  • AWS Free Tier
  • Volume discounts
  • Savings plans and Reserved Instances
  • Demonstration: AWS Pricing Calculator

Module 4: AWS Billing, Reporting, and Monitoring

  • Understanding AWS invoices
  • Reporting and planning
  • AWS Cost Explorer
  • AWS Budgets
  • Demonstration: AWS Billing Console
  • Demonstration: AWS Cost Explorer
  • Demonstration: Trusted Advisor
  • Hands-On Lab: Cost optimization: Deploy Ephemeral Environments Using Amazon EC2 Auto Scaling

Day 2
Module 5: Architecting for Cost: Compute

  • Evolution of compute efficiency
  • Amazon EC2 right-sizing
  • Purchasing options
  • Amazon CloudWatch monitoring and automation
  • Architect for Amazon EC2 Spot Instance
  • Automate the infrastructure provision
  • Manage capacity to optimize resource usage
  • Impact of software licensing
  • Serverless solutions to reduce costs
  • Demonstration: Compute Optimizer
  • Demonstration: Spot Instance Advisor
  • Hands-On Lab: Cost optimization: Right Size Amazon EC2 Instances Using Amazon CloudWatch Metrics

Module 6: Architecting for Cost: Networking

  • Data transfer costs
  • Understand data costs for different services
  • How to triage network costs
  • Hands-On Lab: Cost optimization: Reduce Data Transfer Costs Using Amazon CloudFront and Endpoints

Day 3
Module 7: Architecting for Cost: Storage

  • Amazon EBS cost, pricing, and best practices
  • Amazon S3 cost, pricing, and best practices
  • Amazon EFS cost, pricing, and best practices
  • Hands-On Lab: Cost optimization: Reduce Storage Costs Using Amazon S3 Lifecycle Management

Module 8: Architecting for Cost: Databases

  • Amazon RDS cost, pricing, and best practices
  • Amazon Aurora cost, pricing, and best practices
  • Amazon DynamoDB cost, pricing, and best practices
  • Amazon ElastiCache cost, pricing, and best practices
  • Amazon Redshift cost, pricing, and best practices

Module 9: Cost Governance

  • Setting up AWS Organizations
  • AWS Systems Manager

Module 10: Course Summary

  • Course review
  • Hands-On Lab: Cost optimization: Reduce Compute Costs Using AWS Instance Scheduler

IMPORTANT: Please bring your notebook (Windows, Linux or Mac) to our trainings. If this is not possible, please contact us in advance.

Course materials are in English, on request also in German (if available).
Course language is German, on request also in English.

Running Containers on Amazon Elastic Kubernetes Service (Amazon EKS)

Written by wamrhein on . Posted in AWS Training, Training.

Running Containers on Amazon Elastic Kubernetes Service (Amazon EKS)

current course dates can be found at the bottom of this page … company training available on request!

Course description

Amazon EKS makes it easy for you to run Kubernetes on AWS without needing to install, operate, and maintain your own Kubernetes control plane. In this course, you will learn container management and orchestration for Kubernetes using Amazon EKS.

You will build an Amazon EKS cluster, configure the environment, deploy the cluster, and then add applications to your cluster. You will manage container images using Amazon Elastic Container Registry (ECR) and learn how to automate application deployment. You will deploy applications using CI/CD tools. You will learn how to monitor and scale your environment by using metrics, logging, tracing, and horizontal/vertical scaling. You will learn how to design and manage a large container environment by designing for efficiency, cost, and resiliency. You will configure AWS networking services to support the cluster and learn how to secure your Amazon EKS environment.

Course objectives

In this course, you will learn to:

  • Describe Kubernetes and Amazon EKS fundamentals and the impact of containers on workflows.
  • Build an Amazon EKS cluster by selecting the correct compute resources to support worker nodes.
  • Secure your environment with AWS Identity and Access Management (IAM) authentication and Kubernetes Role Based Access Control (RBAC) authorization.
  • Deploy an application on the cluster. Publish container images to Amazon ECR and secure access via IAM policy.
  • Deploy applications using automated tools and pipelines. Create a GitOps pipeline using WeaveFlux.
  • Collect monitoring data through metrics, logs, and tracing with AWS X-Ray and identify metrics for performance tuning. Review scenarios where bottlenecks require the best scaling approach using horizontal or vertical scaling.
  • Assess the tradeoffs between efficiency, resiliency, and cost and the impact of tuning for one over the others. Describe and outline a holistic, iterative approach to optimizing your environment. Design for cost, efficiency, and resiliency.
  • Configure AWS networking services to support the cluster. Describe how Amazon Virtual Private Cloud (VPC) supports Amazon EKS clusters and simplifies inter-node communications. Describe the function of the VPC Container Network Interface (CNI). Review the benefits of a service mesh.
  • Upgrade your Kubernetes, Amazon EKS, and third party tools.

Intended audience

This course is intended for:

  • people who provide container orchestration management in the AWS Cloud including:
    • DevOps engineers
    • Systems administrators

Prerequisites

We recommend that attendees of this course have:

  • Completed Amazon Elastic Kubernetes Service (EKS) Primer
  • Completed AWS Cloud Practitioner Essentials (or equivalent real-world experience)
  • Basic Linux administration experience
  • Basic network administration experience
  • Basic knowledge of containers and microservices

Activities

This course includes:

  • Training with instructor
  • Practical exercises

Course duration / Price

  • 3 days
  • € 2,685.00 (excl. tax) per person (DE)
  • CHF 2,500.00 (excl. tax) per person (CH)

Course outline

Day 1:

  • Module 0: Course Introduction
  • Module 1: Kubernetes Fundamentals
  • Hands-On Lab 1: Deploying Kubernetes Pods
  • Module 2: Amazon EKS Fundamentals
  • Module 3: Building an Amazon EKS Cluster
  • Hands-On Lab 2: Building an Amazon EKS cluster

Day 2:

  • Module 4: Deploying Applications to Your Amazon EKS Cluster
  • Hands-On Lab 3: Deploying applications
  • Module 5: Architecting on Amazon EKS Part 1: Observe and Optimize
  • Hands-On Lab 4: Monitoring Amazon EKS
  • Module 6: Architecting on Amazon EKS Part 2: Balancing Efficiency, Resiliency, and Cost

Day 3:

  • Module 7: Managing Networking in Amazon EKS
  • Hands-On Lab 5: Exploring Amazon EKS Communication
  • Module 8: Securing Amazon EKS Clusters
  • Hands-On Lab 6: Securing Amazon EKS
  • Module 9: Managing Upgrades in Amazon EKS

 

IMPORTANT: Please bring your notebook (Windows, Linux or Mac) to our trainings. If this is not possible, please contact us in advance.

Course materials are in English, on request also in German (if available).
Course language is German, on request also in English.

The Machine Learning Pipeline on AWS

Written by wamrhein on . Posted in AWS Training, Training.

The Machine Learning Pipeline on AWS

current course dates can be found at the bottom of this page … company training available on request!

Course description

Learn how to use the machine learning (ML) pipeline with Amazon SageMaker with hands-on exercises and four days of instruction. You will learn how to frame your business problems as ML problems and use Amazon SageMaker to train, evaluate, tune, and deploy ML models. Hands-on learning is a key component of this course, so you’ll choose a project to work on, and then apply the knowledge and skills you learn to your chosen project in each phase of the pipeline. You’ll have a choice of projects: fraud detection, recommendation engines, or flight delays.

Course objectives

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 in 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

Intended audience

This course is intended for:

  • Developers
  • Solutions architects
  • Data engineers
  • Anyone who wants to learn about the ML pipeline via Amazon SageMaker, even if you have little to no experience with machine learning

Prerequisites

We recommend that attendees of this course have:

  • Basic knowledge of Python
  • Basic understanding of AWS Cloud infrastructure (Amazon S3 and Amazon CloudWatch)
  • Basic understanding of working in a Jupyter notebook environment

Activities

This course includes:

  • Training with instructor
  • Practical exercises
  • Group exercices

Course duration / Price

  • 4 days
  • € 2,795.00 (excl. tax) per person (DE)
  • CHF 3,700.00 (excl. tax) per person (CH)

Course outline

Day 1:

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

Day 2:

Module 3: Problem Formulation (continued)
• Practice problem formulation
• Formulate problems for projects

Checkpoint 1 and Answer Review
Module 4: Preprocessing
• Overview of data collection and integration, and techniques for data preprocessing and visualization
• Practice preprocessing
• Preprocess project data and discuss project progress

Day 3:

Checkpoint 2 and Answer Review
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, then present findings

Day 4:

Checkpoint 3 and Answer Review
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

IMPORTANT: Please bring your notebook (Windows, Linux or Mac) to our trainings. If this is not possible, please contact us in advance.

Course materials are in English, on request also in German (if available).
Course language is German, on request also in English.