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AWS Well-Architected Best Practices

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

AWS Well-Architected Best Practices

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

Course description

The AWS Well-Architected Best Practices course will help you learn a consistent approach to evaluate architectures and implement designs from a live instructor. You’ll learn how to use the Well-Architected Review process and the AWS Well-Architected Tool to conduct reviews to identify high risk issues (HRIs). In this 1-day, classroom training course, you’ll learn to apply the five pillars of the AWS Well-Architected Framework—operational excellence, security, reliability, performance efficiency, and cost optimization—to understand the impact of design decisions. You’ll apply what you’ve learned during the course to each pillar of the Well-Architected Framework through tutorials, hands-on labs, discussions, demonstrations, presentations, and group exercises.

Course objectives

In this course, you will learn to:

  • Identify the Well-Architected Framework features, design principles, design pillars, and common uses
  • Apply the design principles, key services, and best practices for each pillar of the Well-Architected Framework
  • Use the Well-Architected Tool to conduct Well-Architected Reviews

Intended audience

This course is intended for:

  • Technical professionals involved in architecting, building, and operating AWS solutions.

Prerequisites

We recommend that attendees of this course have:

Activities

This course includes:

  • Training with instructor
  • Practical exercises

Course duration / Price

  • 1 day / € 750.00 (excl. tax) per person (DE)

Course outline

Module 1: Well-Architected Introduction

  • History of Well-Architected
  • Goals of Well-Architected
  • What is the AWS Well-Architected Framework?
  • The AWS Well-Architected Tool

Module 2: Design Principles

  • Operational Excellence
  • Lab 1: Operational Excellence
  • Reliability
  • Lab 2: Reliability
  • Security
  • Lab 3: Security
  • Performance Efficiency
  • Lab 4: Performance Efficiency
  • Cost Optimization
  • Lab 5: Cost Optimization

 

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 Security Governance at Scale

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

AWS Security Governance at Scale

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

Course description

Security is foundational to AWS. Governance at scale is a new concept for automating cloud governance that can help companies retire manual processes in account management, budget enforcement, and security and compliance. By automating common challenges, companies can scale without inhibiting agility, speed, or innovation. In addition, they can provide decision makers with the visibility, control, and governance necessary to protect sensitive data and systems.

In this course, you will learn how to facilitate developer speed and agility, and incorporate preventive and detective controls. By the end of this course, you will be able to apply governance best practices.

Course objectives

In this course, you will learn to:

  • Establish a landing zone with AWS Control Tower
  • Configure AWS Organizations to create a multi-account environment
  • Implement identity management using AWS Single Sign-On users and groups
  • Federate access using AWS SSO
  • Enforce policies using prepackaged guardrails
  • Centralize logging using AWS CloudTrail and AWS Config
  • Enable cross-account security audits using AWS Identity and Access Management (IAM)
  • Define workflows for provisioning accounts using AWS Service Catalog and AWS Security Hub

Intended audience

This course is intended for:

  • Solutions architects
  • Security DevOps
  • Security engineers

Prerequisites

We recommend that attendees of this course have:

Activities

This course includes:

  • Training with instructor
  • Practical exercises

Course duration / Price

  • 1 day / € 895.00 (excl. tax) per person (DE)

Course outline

Course Introduction

  • Instructor introduction
  • Learning objectives
  • Course structure and objectives
  • Course logistics and agenda

Module 1: Governance at Scale

  • Governance at scale focal points
  • Business and Technical Challenges

Module 2: Governance Automation

  • Multi-account strategies, guidance, and architecture
  • Environments for agility and governance at scale
  • Governance with AWS Control Tower
  • Use cases for governance at scale

Module 3: Preventive Controls

  • Enterprise environment challenges for developers
  • AWS Service Catalog
  • Resource creation
  • Workflows for provisioning accounts
  • Preventive cost and security governance
  • Self-service with existing IT service management (ITSM) tools
  • Lab 1: Deploy Resources for AWS Catalog
  • Create a new AWS Service Catalog portfolio and product
  • Add an IAM role to a launch constraint to limit the actions the product can perform
  • Grant access for an IAM role to view the catalog items
  • Deploy an S3 bucket from an AWS Service Catalog product

Module 4: Detective Controls

  • Operations aspect of governance at scale
  • Resource monitoring
  • Configuration rules for auditing
  • Operational insights
  • Remediation Clean up accounts
  • Lab 2: Compliance and Security Automation with AWS Config
  • Apply Managed Rules through AWS Config to selected resources
  • Automate remediation based on AWS Config rules
  • Investigate the Amazon Config dashboard and verify resources and rule compliance
  • Lab 3: Taking Action with AWS Systems Manager
  • Setup Resource Groups for various resources based on common requirements
  • Perform automated actions against targeted Resource Groups

Module 5: Resources

  • Explore additional resources for security governance at scale

 

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.

MLOps Engineering on AWS Training

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

MLOps Engineering on AWS Training

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

Course description

This course builds upon and extends the DevOps practice prevalent in software development to build, train, and deploy machine learning (ML) models. The course stresses the importance of data, model, and code to successful ML deployments. It will demonstrate the use of tools, automation, processes, and teamwork in addressing the challenges associated with handoffs between data engineers, data scientists, software developers, and operations. The course will also discuss the use of tools and processes to monitor and take action when the model prediction in production starts to drift from agreed-upon key performance
indicators.

The instructor will encourage the participants in this course to build an MLOps action plan for their organization through daily reflection of lesson and lab content, and through conversations with peers and instructors.

Course objectives

In this course, you will learn to:

  • Describe machine learning operations
  • Understand the key differences between DevOps and MLOps
  • Describe the machine learning workflow
  • Discuss the importance of communications in MLOps
  • Explain end-to-end options for automation of ML workflows
  • List key Amazon SageMaker features for MLOps automation
  • Build an automated ML process that builds, trains, tests, and deploys models
  • Build an automated ML process that retrains the model based on change(s) to the model code
  • Identify elements and important steps in the deployment process
  • Describe items that might be included in a model package, and their use in training or inference
  • Recognize Amazon SageMaker options for selecting models for deployment, including support for ML frameworks and built-in algorithms or bring-your-own-models
  • Differentiate scaling in machine learning from scaling in other applications
  • Determine when to use different approaches to inference
  • Discuss deployment strategies, benefits, challenges, and typical use cases
  • Describe the challenges when deploying machine learning to edge devices
  • Recognize important Amazon SageMaker features that are relevant to deployment and inference
  • Describe why monitoring is important
  • Detect data drifts in the underlying input data
  • Demonstrate how to monitor ML models for bias
  • Explain how to monitor model resource consumption and latency
  • Discuss how to integrate human-in-the-loop reviews of model results in production

Intended audience

This course is intended for:

  • DevOps Engineers
  • ML Engineers
  • Developers/operations with responsibility for operationalizing ML models

Prerequisites

We recommend that attendees of this course have:

Activities

This course includes:

  • Training with instructor
  • Practical exercises

Course duration / Price

  • 3 days / € 2,685.00 (excl. tax) per person (DE)

Course outline

Module 1: Security on AWS

  • Machine learning operations
  • Goals of MLOps
  • Communication
  • From DevOps to MLOps
  • ML workflow
  • Scope
  • MLOps view of ML workflow
  • MLOps cases

Module 2: MLOps Development

  • Intro to build, train, and evaluate machine learning models
  • MLOps security
  • Automating
  • Apache Airflow
  • Kubernetes integration for MLOps
  • Amazon SageMaker for MLOps
  • Lab: Bring your own algorithm to an MLOps pipeline
  • Demonstration: Amazon SageMaker
  • Lab: Code and serve your ML model with AWS CodeBuild
  • Activity: MLOps Action Plan Workbook

Module 3: MLOps Deployment

  • Introduction to deployment operations
  • Model packaging
  • Inference
  • Lab: Deploy your model to production
  • SageMaker production variants
  • Deployment strategies
  • Deploying to the edge
  • Lab: Conduct A/B testing
  • Activity: MLOps Action Plan Workbook

Module 4: Model Monitoring and Operations

  • Lab: Troubleshoot your pipeline
  • The importance of monitoring
  • Monitoring by design
  • Lab: Monitor your ML model
  • Human-in-the-loop
  • Amazon SageMaker Model Monitor
  • Demonstration: Amazon SageMaker Pipelines, Model Monitor, model registry, and Feature Store
  • Solving the Problem(s)
  • Activity: MLOps Action Plan Workbook

Module 5: Wrap-up

  • Course review
  • Activity: MLOps Action Plan Workbook
  • 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.

Building Data Analytics Solutions Using Amazon Redshift

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

Building Data Analytics Solutions Using Amazon Redshift

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

Course description

In this course, you will build a data analytics solution using Amazon Redshift, a cloud data warehouse service. The course focuses on the data collection, ingestion, cataloging, storage, and processing components of the analytics pipeline. You will learn to integrate Amazon Redshift with a data lake 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 Redshift.

Course objectives

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 data warehouse 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

Intended audience

This course is intended for:

  • Data Warehouse Engineers
  • Data Platform Engineers
  • Architects and Operators who build and manage data analytics pipelines

Prerequisites

We recommend that attendees of this course have:

Activities

This course includes:

  • Training with instructor
  • Practical exercises

Course duration / Price

  • 1 day / € 750.00 (excl. tax) per person (DE)

Course outline

Module A: Overview of Data Analytics and the Data Pipeline

  • Data analytics use cases
  • Using the data pipeline for analytics

Module 1: Using Amazon Redshift in the Data Analytics Pipeline

  •  Why Amazon Redshift for data warehousing?
  • Overview of Amazon Redshift

Module 2: Introduction to Amazon Redshift

  • Amazon Redshift architecture
  • Interactive Demo 1: Touring the Amazon Redshift console
  • Amazon Redshift features
  • Practice Lab 1: Load and query data in an Amazon Redshift cluster

Module 3: Ingestion and Storage

  • Ingestion
  • Interactive Demo 2: Connecting your Amazon Redshift cluster using a Jupyter notebook with Data API
  • Data distribution and storage Interactive Demo 3: Analyzing semi-structured data using the SUPER data type
  • Querying data in Amazon Redshift
  • Practice Lab 2: Data analytics using Amazon Redshift Spectrum

Module 4: Processing and Optimizing Data

  • Data transformation
  • Advanced querying Practice Lab 3: Data transformation and querying in Amazon Redshift
  • Resource management
  • Interactive Demo 4: Applying mixed workload management on Amazon Redshift
  • Automation and optimization
  • Interactive demo 5: Amazon Redshift cluster resizing from the dc2.large to ra3.xlplus cluster

Module 5: Security and Monitoring of Amazon Redshift Clusters

  • Securing the Amazon Redshift cluster
  • Monitoring and troubleshooting Amazon Redshift clusters

Module 6: Designing Data Warehouse Analytics Solutions

  • Data warehouse use case review
  • Activity: Designing a data warehouse analytics workflow

Module B: Developing Modern Data Architectures on AWS

  • Modern data architectures

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.

Building Data Lakes on AWS

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

Building Data Lakes on AWS

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 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.

Course objectives

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

Intended audience

This course is intended for:

  • Data platform engineers
  • Solutions architects
  • IT professionals

Prerequisites

We recommend that attendees of this course have:

  • Completed thes AWS Technical Essentials training
  • One year of experience building data analytics pipelines or have completed the Data Analytics Fundamentals course

Activities

This course includes:

  •  presentations
  • lecture
  • hands-on labs,
  • group exercises

Course duration / Price

  • 1 day / € 750.00 (excl. tax) per person (DE)

Course outline

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

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.