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The Machine Learning Pipeline on AWS

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.

Planning and Designing Databases on AWS

Planning and Designing Databases 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 about the process of planning and designing both relational and nonrelational databases. You will learn the design considerations for hosting databases on Amazon Elastic Compute Cloud (Amazon EC2). You will learn about our relational database services including Amazon Relational Database Service (Amazon RDS), Amazon Aurora, and Amazon Redshift. You will also learn about our nonrelational database services including Amazon DocumentDB, Amazon DynamoDB, Amazon ElastiCache, Amazon Neptune, and Amazon QLDB. By the end of this course, you will be familiar with the planning and design requirements of all 8 of these AWS databases services, their pros and cons, and how to know which AWS databases service is right for your workloads.

Course objectives

In this course, you will learn to:

  • 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

Intended audience

This course is intended for:

  • Data platform engineers
  • Database administrators
  • Solutions architects
  • IT professionals

Prerequisites

We recommend that attendees of this course have previously completed the following AWS courses:

Activities

This course includes:

  • Training with instructor
  • Practical exercises

Course duration / Price

  • 3 days / € 2,095.00 (excl. tax) per person (DE)

Course outline

Day 1:

Module 0: Planning and Designing Databases on AWS

Module 1: Database Concepts and General Guidelines

  • Topic A: Databases in the cloud
  • Topic B: Database design principles
  • Topic C: Transactional compliance

Module 2: Database Planning and Design

  • Topic A: Workload requirements
  • Topic B: Design considerations

Module 3: Databases on Amazon EC2

  • Topic A: Amazon EC2 for hosting databases-

Module 4: Purpose-Built Databases

  • Topic A: The journey to AWS
  • Topic B: Data modeling basics

Module 5: Databases on Amazon RDS

  • Topic A: Amazon RDS databases
  • Topic B: Amazon RDS distinguishing features
  • Topic C: Amazon RDS Design considerations
  • Lab 1: Working with Amazon RDS databases

Module 6: Databases in Amazon Aurora

  • Topic A: Amazon Aurora databases
  • Topic B: Aurora distinguishing features
  • Topic C: Aurora design considerations

Day 2:

Module 6: Databases in Amazon Aurora (continued)

  • Lab 2: Working with Amazon Aurora databases

Module 7: Databases in Amazon DocumentDB (with MongoDB compatibility)

  • Topic A: Amazon DocumentDB
  • Topic B: Amazon DocumentDB design considerations
  • Lab 3: Working with Amazon DocumentDB databases

Module 8: Amazon DynamoDB Tables

  • Topic A: Amazon DynamoDB
  • Topic B: DynamoDB data modeling
  • Topic C: DynamoDB distinguishing features
  • Topic D: DynamoDB design considerations
  • Lab 4: Working with Amazon DynamoDB Tables

Day 3:

Module 9: Databases in Amazon Neptune

  • Topic A: Amazon Neptune
  • Topic B: Neptune design considerations

Module 10: Databases in Amazon Quantum Ledger Database (Amazon QLDB)

  • Topic A: Amazon Quantum Ledger Database (Amazon QLDB)
  • Topic B: Amazon QLDB Design Considerations

Module 11: Databases in Amazon ElastiCache

  • Topic A: Amazon ElastiCache
  • Topic B: ElastiCache for Memcached
  • Topic C: ElastiCache for Redis

Module 12: Data Warehousing in Amazon Redshift

  • Topic A: Amazon Redshift
  • Topic B: Amazon Redshift distinguishing features
  • Topic C: Amazon Redshift data modeling
  • Topic D: Amazon Redshift design considerations
  • Lab 5: Working with Amazon Redshift Clusters

 

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 Development Kit (CDK) Essentials

AWS Cloud Development Kit (CDK) Essentials

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

Course description

In this introductory course, you will learn about the products, services, and common solutions around the AWS Cloud Development Kit. You will learn the basics around infrastructure as code, concepts and decision support for infrastructure as code, and how to build infrastructure with the AWS Cloud Development Kit. For more information and outlines, see the course content.

Course objectives

In this course, you will learn:

  • What is infrastructure as code
  • Concepts and decision guidelines for infrastructure as code frameworks.
  • Building Infrastructure with the AWS Cloud Development Kit

Intended audience

This course is intended for:

  • Developers
  • System administrators
  • DevOps team members

Activities

This course includes:

  • Training with instructor
  • Practical exercises. Bookable as TypeScript and Python course. For in-house training C#, Java or GO is also possible.
  • Knowledge queries

Course duration / Price

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

Course outline

Module 1: Infrastructure as Code (IaC) Overview

  • What is IaC
  • Introduction and comparison of CloudFormation, Terraform, and CDK.
  • Conceptual differences between the frameworks

Module 2: Developing with the Cloud Development Kit

  • Architecture of the CDK
  • Abstraction with constructs
  • How to get started with the CDK

Module 3: Development process

  • Initialization
  • Project start
  • Test stages

Module 4: VPC and IAM with the CDK

  • Creating network infrastructure with the CDK
  • Rights management
  • EC2 instances

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.

Practical Data Science with Amazon SageMaker

Practical Data Science with Amazon SageMaker

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

Course description

In this course, learn how to solve a real-world use case with machine learning and produce actionable results using Amazon SageMaker. This course teaches you how to use Amazon SageMaker to cover the different stages of the typical data science process, from analyzing and visualizing a data set, to preparing the data and feature engineering, down to the practical aspects of model building, training, tuning and deployment.

Course objectives

In this course, you will learn to:

  • Prepare a dataset for training.
  • Train and evaluate a machine learning model.
  • Automatically tune a machine learning model.
  • Prepare a machine learning model for production.
  • Think critically about machine learning model results.

Intended audience

This course is intended for:

  • A technical audience at an intermediate level

Prerequisites

We recommend that attendees of this course have:

  • Working knowledge of a programming language

Activities

This course includes:

  • Training with instructor
  • Practical exercises

Course duration / Price

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

Course outline

  • Business problem: Churn prediction
  • Load and display the dataset
  • Assess features and determine which Amazon SageMaker algorithm to use
  • Use Amazon Sagemaker to train, evaluate, and automatically tune the model
  • Deploy the model
  • Assess relative cost of errors

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.

Terraform Essentials

Terraform Essentials

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

Course description

Terraform Essentials explains the benefits of Terraform in the cloud computing / AWS environment and gives participants practical examples in the “Infrastructure as Code” (IaC) topic area. In this course, we also train the advantages of open source description languages in the DevOps environment. By means of exercises, practical experience is gained quickly and participants learn to write reusable code. Demos teach concepts and good practice outside the box.

 

Course objectives

In this course, you will learn to:

  • Advantages of Terraform for creating cloud resources
  • Terraform basic commands and functionality
  • Understand and apply language concepts to efficiently write readable and reusable code
  • Hands-on experience working with Terraform

Prerequisites

We recommend that attendees of this course have:

  • Basic understanding of general networking concepts and multi-layer architectures.
  • Understanding of cloud computing concepts
  • Basic knowledge of basic AWS services (VPC, EC2, S3)
  • Basic knowledge of software development and system administration (git, Bash, advanced editor)

Course duration / Price

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

Course outline

  • Introduction
  • Basic commands
  • Core concepts 1 (State, Resources, Variables)
  • Coding block 1 – Practical exercise
  • Core concepts 2 (References, Outputs, Naming, Functions)
  • Coding block 2 – Practical exercise
  • Core Concepts 3 (External Resources, Loops & If/Else, Locals, Meta Arguments)
  • Coding Block 3 – Practical Exercise
  • Reusable Code: Modules
  • Quiz, outlook and certification 

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

The practical exercises are performed in prepared working environments available via web browser – no software needs to be installed. The course materials are in English, the trainer’s course language is German.