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AWS Certified Data Engineer – Associate

Written by Gemma Corrado on . Posted in AWS Training, Certifications, Training.

Who should take this exam?

AWS Certified Data Engineer – Associate is designed for those who have experience in data engineering and understand the effects of volume, variety, and velocity on data ingestion, transformation, modeling, security, governance, privacy, schema design, and optimal data store design. You should also have hands-on experience with AWS services.

We recommend that you have the following knowledge before taking this exam:

  • Setup and maintenance of extract, transform, and load (ETL) pipelines from ingestion to destination
  • Application of high-level but language-agnostic programming concepts as required by the pipeline
  • How to use Git commands for source control
  • How to use data lakes to store data
  • General concepts for networking, storage, and compute
  • An understanding of the AWS services for encryption, governance, protection, and logging of all data that is part of data pipelines
  • The ability to compare AWS services to understand the cost, performance, and functional differences between services
  • How to structure SQL queries and how to run SQL queries on AWS services
  • An understanding of how to analyze data, verify data quality, and ensure data consistency by using AWS services

Prerequisites

The recommended experience prior to taking this exam is the equivalent of 2 to 3 years in data engineering or data architecture and at least 1 to 2 years of hands-on experience with AWS services.

Recertification

AWS Certifications are valid for three years. To maintain your AWS Certified status, we require you to periodically demonstrate your continued expertise though a process called recertification. Recertification helps strengthen the overall value of your AWS Certification and shows individuals and employers that your credential covers the latest AWS knowledge, skills, and best practices. Once you have obtained an AWS certification, you will receive a 50% discount on other AWS certification exams.

Developing Generative AI Applications on AWS

Written by Gemma Corrado on . Posted in AWS Training, Training.

  • Aws Advanced Training Partner

  • Aws Premium Consuting Partner

  • 200 Cert

Developing Generative AI Applications on AWS

Please find our upcoming course dates at the end of this page!

Course description

This course is designed to introduce generative artificial intelligence (AI) to software developers interested in using large language models (LLMs) without fine-tuning. The course provides an overview of generative AI, planning a generative AI project, getting started with Amazon Bedrock, the foundations of prompt engineering, and the architecture patterns to build generative AI applications using Amazon Bedrock and LangChain.

COURSE OBJECTIVES

In this course, you will learn to:

  • Describe generative AI and how it aligns to machine learning
  • Define the importance of generative AI and explain its potential risks and benefits
  • Identify business value from generative AI use cases
  • Discuss the technical foundations and key terminology for generative AI
  • Explain the steps for planning a generative AI project
  • Identify some of the risks and mitigations when using generative AI
  • Understand how Amazon Bedrock works
  • Familiarize yourself with basic concepts of Amazon Bedrock
  • Recognize the benefits of Amazon Bedrock
  • List typical use cases for Amazon Bedrock
  • Describe the typical architecture associated with an Amazon Bedrock solution
  • Understand the cost structure of Amazon Bedrock
  • Implement a demonstration of Amazon Bedrock in the AWS Management Console
  • Define prompt engineering and apply general best practices when interacting with foundation models (FMs)
  • Identify the basic types of prompt techniques, including zero-shot and few-shot learning
  • Apply advanced prompt techniques when necessary for your use case
  • Identify which prompt techniques are best suited for specific models
  • Identify potential prompt misuses
  • Analyze potential bias in FM responses and design prompts that mitigate that bias
  • Identify the components of a generative AI application and how to customize an FM
  • Describe Amazon Bedrock foundation models, inference parameters, and key Amazon Bedrock APIs
  • Identify Amazon Web Services (AWS) offerings that help with monitoring, securing, and governing your Amazon Bedrock applications
  • Describe how to integrate LangChain with LLMs, prompt templates, chains, chat models, text
    embeddings models, document loaders, retrievers, and Agents for Amazon Bedrock
  • Describe architecture patterns that you can implement with Amazon Bedrock for building generative AI applications
  • Apply the concepts to build and test sample use cases that use the various Amazon Bedrock models, LangChain, and the Retrieval Augmented Generation (RAG) approach

INTENDED AUDIENCE

This course is intended for:

  • Software developers interested in using LLMs without fine-tuning

PREREQUISITES

We recommend that attendees of this course have:

ACTIVITIES

This course includes:

  • Presentations
  • Demonstrations
  • Group exercises

COURSE DURATION / PRICE

  • 2 days
  • € 1,500.00 (excl. tax) per person (DE)

Course outline

  • Day 1

    • Module 1: Introduction to Generative AI – Art of the Possible
      • Overview of ML
      • Basics of generative AI
      • Generative AI use cases
      • Generative AI in practice
      • Risks and benefits
    • Module 2: Planning a Generative AI Project
      • Generative AI fundamentals
      • Generative AI in practice
      • Generative AI context
      • Steps in planning a generative AI project
      • Risks and mitigation
    • Module 3: Getting Started with Amazon Bedrock
      • Introduction to Amazon Bedrock
      • Architecture and use cases
      • How to use Amazon Bedrock
      • Demonstration: Setting up Bedrock access and using playgrounds
    • Module 4: Foundations of Prompt Engineering
      • Basics of foundation models
      • Fundamentals of prompt engineering
      • Basic prompt techniques
      • Advanced prompt techniques
      • Model-specific prompt techniques
      • Demonstration: Fine-tuning a basic text prompt
      • Addressing prompt misuses
      • Mitigating bias
      • Demonstration: Image bias mitigation

  • Day 2

    • Module 5: Amazon Bedrock Application Components
      • Overview of generative AI application components
      • Foundation models and the FM interface
      • Working with datasets and embeddings
      • Demonstration: Word embeddings
      • Additional application components
      • Retrieval Augmented Generation (RAG)
      • Model fine-tuning
      • Securing generative AI applications
      • Generative AI application architecture
    • Module 6: Amazon Bedrock Foundation Models
      • Introduction to Amazon Bedrock foundation models
      • Using Amazon Bedrock FMs for inference
      • Amazon Bedrock methods
      • Data protection and auditability
      • Demonstration: Invoke Bedrock model for text generation using zero-shot prompt
    • Module 7: LangChain
      • Optimizing LLM performance
      • Using models with LangChain
      • Constructing prompts
      • Demonstration: Bedrock with LangChain using a prompt that includes context
      • Structuring documents with indexes
      • Storing and retrieving data with memory
      • Using chains to sequence components
      • Managing external resources with LangChain agents
    • Module 8: Architecture Patterns
      • Introduction to architecture patterns
      • Text summarization
      • Demonstration: Text summarization of small files with Anthropic Claude
      • Demonstration: Abstractive text summarization with Amazon Titan using LangChain
      • Question answering
      • Demonstration: Using Amazon Bedrock for question answering
      • Chatbot
      • Demonstration: Conversational interface – Chatbot with AI21 LLM
      • Code generation
      • Demonstration: Using Amazon Bedrock models for code generation
      • LangChain and agents for Amazon Bedrock
      • Demonstration: Integrating Amazon Bedrock models with LangChain agents

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).
The Course language is German, on request also in English.

Neue Termine in Planung!

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Amazon SageMaker Studio for Data Scientists

Written by Gemma Corrado on . Posted in AWS Training, Training.

  • Aws Advanced Training Partner

  • Aws Premium Consuting Partner

Amazon SageMaker Studio for Data Scientists

Please find our upcoming course dates at the end of this page!

Course description

Amazon SageMaker Studio helps data scientists prepare, build, train, deploy, and monitor machine learning (ML) models quickly. It does this by bringing together a broad set of capabilities purpose-built for ML. This course prepares experienced data scientists to use the tools that are a part of SageMaker Studio, including Amazon CodeWhisperer and Amazon CodeGuru Security scan extensions, to improve productivity at every step of the ML lifecycle.

COURSE OBJECTIVES

In this course, you will learn to:

  • Accelerate the process to prepare, build, train, deploy, and monitor ML solutions using Amazon SageMaker Studio

INTENDED AUDIENCE

This course is intended for:

  • Experienced data scientists who are proficient in ML and deep learning fundamentals

PREREQUISITES

We recommend that attendees of this course have:

  • Experience using ML frameworks
  • Python programming experience
  • At least 1 year of experience as a data scientist responsible for training, tuning, and deploying models
  • AWS Technical Essentials 

ACTIVITIES

This course includes:

  • presentations
  • demonstrations
  • hands-on labs
  • discussions
  • a capstone project

COURSE DURATION / PRICE

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

Course outline

  • Day 1

    • Module 1: Amazon SageMaker Studio Setup
      • JupyterLab Extensions in SageMaker Studio
      • Demonstration: SageMaker user interface demo
    • Module 2: Data Processing
      • Using SageMaker Data Wrangler for data processing
      • Hands-On Lab: Analyze and prepare data using Amazon SageMaker Data Wrangler
      • Using Amazon EMR
      • Hands-On Lab: Analyze and prepare data at scale using Amazon EMR
      • Using AWS Glue interactive sessions
      • Using SageMaker Processing with custom scripts
      • Hands-On Lab: Data processing using Amazon SageMaker Processing and SageMaker Python SDK
      • SageMaker Feature Store
      • Hands-On Lab: Feature engineering using SageMaker Feature Store
    • Module 3: Model Development
      • SageMaker training jobs
      • Built-in algorithms
      • Bring your own script
      • Bring your own container
      • SageMaker Experiments
      • Hands-On Lab: Using SageMaker Experiments to Track Iterations of Training and Tuning Models

  • Day 2

    • Module 3: Model Development (continued)
      • SageMaker Debugger
      • Hands-On Lab: Analyzing, Detecting, and Setting Alerts Using SageMaker Debugger
      • Automatic model tuning
      • SageMaker Autopilot: Automated ML
      • Demonstration: SageMaker Autopilot
      • Bias detection
      • Hands-On Lab: Using SageMaker Clarify for Bias and Explainability
      • SageMaker Jumpstart
    • Module 4: Deployment and Inference
      • SageMaker Model Registry
      • SageMaker Pipelines
      • Hands-On Lab: Using SageMaker Pipelines and SageMaker Model Registry with SageMaker Studio
      • SageMaker model inference options
      • Scaling
      • Testing strategies, performance, and optimization
      • Hands-On Lab: Inferencing with SageMaker Studio
    • Module 5: Monitoring
      • Amazon SageMaker Model Monitor
      • Discussion: Case study
      • Demonstration: Model Monitoring

  • Day 3

    • Module 6: Managing SageMaker Studio Resources and Updates
      • Accrued cost and shutting down
      • Updates
    • Capstone
      • Environment setup
      • Challenge 1: Analyze and prepare the dataset with SageMaker Data Wrangler
      • Challenge 2: Create feature groups in SageMaker Feature Store
      • Challenge 3: Perform and manage model training and tuning using SageMaker Experiments
      • (Optional) Challenge 4: Use SageMaker Debugger for training performance and model optimization
      • Challenge 5: Evaluate the model for bias using SageMaker Clarify
      • Challenge 6: Perform batch predictions using model endpoint
      • (Optional) Challenge 7: Automate full model development process using SageMaker Pipeline

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).
The Course language is German, on request also in English.

Neue Termine in Planung!

Continue reading

Networking Essentials for Cloud Applications on AWS

Written by Gemma Corrado on . Posted in AWS Training, Training.

  • Aws Advanced Training Partner

  • Aws Premium Consuting Partner

Networking Essentials for Cloud Applications on AWS

Please find our upcoming course dates at the end of this page!

Course description

The Networking Essentials for Cloud Applications on AWS course provides a comprehensive understanding of networking concepts and services within the Amazon Web Services (AWS) cloud environment. Designed for novice and experienced networking engineers, this course covers essential topics, best practices, and hands-on labs. Its purpose is to equip learners with the knowledge and skills that are required to design, configure, and optimize network infrastructure on AWS.

COURSE OBJECTIVES

In this course, you will learn to:

  • Design a networking infrastructure for a scalable production application, considering design trade-offs between different networking services.
  • Configure networking services for a highly available, resilient, and scalable application.
  • Implement the networking infrastructure according to evolving business requirements.
  • Implement networking best practices to align towards AWS Well-Architected Framework.

INTENDED AUDIENCE

This course is intended for:

  • Newly hired cloud engineers
  • On-premises IT engineers
  • Cloud architects
  • Cloud engineers
  • Network engineers

PREREQUISITES

We recommend that attendees of this course have:

ACTIVITIES

This course includes:

  • presentations
  • demonstrations
  • knowledge checks
  • three hands-on labs that revolve around a use case story

COURSE DURATION / PRICE

  • 1 day
  • € 750,00 (excl. tax) per person (DE)

Course outline

Day 1

  • Module 0: Course Introduction
    • Introductions
    • Course overview
    • Use case introduction
  • Module 1: Networking on AWS
    • IP addressing
    • Amazon Virtual Private Cloud (Amazon VPC) fundamentals
    • Subnets
    • Amazon VPC IP Address Manager (IPAM)
    • Elastic Network Interfaces
    • Elastic IP addressing
    • Route table
    • Internet and NAT gateways
    • Basic traffic filtering mechanisms for a VPC
    • Knowledge check
  • Module 2: Load Balancing and Scaling on AWS
    • Elastic Load Balancing (ELB)
    • Cross-zone load balancing
    • Auto Scaling group (ASG) basics
    • Knowledge check
    • Use case part one
    • Hands-on lab: Building a Multi-Availability Zone VPC Architecture
  • Module 3: VPC Interconnectivity and Content Delivery
    • VPC interconnectivity
    • VPC peering
    • VPC Transit Gateway
    • VPC endpoints
    • Edge locations
    • AWS Global Accelerator
    • Knowledge check
    • Use case part two
    • Hands-on lab: Accelerating Performance with Amazon CloudFront
  • Module 4: High Availability with Amazon Route 53
    • Amazon Route 53
    • Knowledge check
    • Use case part three
    • Hands-on lab: Achieving Fault Tolerance and Global Traffic Optimization
  • Module 5: Course Wrap-Up
    • Course reflection
    • Use case labs recap
    • Use case conclusion
    • Course feedback survey

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).
The Course language is German, on request also in English.

Neue Termine in Planung!

Continue reading

AWS Discovery Day – AWS Cloud Practitioner Essentials

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

  • Aws Advanced Training Partner

  • Aws Premium Consuting Partner

AWS Discovery Day – AWS Cloud Practitioner Essentials

In this free three-hour online event, executives and IT professionals will learn about the benefits of computing in the AWS Cloud. AWS services for computing, storage, database management, networking, and content delivery will be covered. You will learn how to leverage AWS services to improve business processes and how to benefit from AWS services for security and compliance.

The event is particularly relevant for customers who have recently signed up to AWS or for those who simply want to learn the basics of the AWS cloud platform.

Sign up for the free webinar here

COURSE OBJECTIVES

By attending this event, participants can expect to learn:

  • What the cloud is and how it works
  • The benefits of the AWS Cloud
  • Key AWS services and common use cases (computing, storage, databases, etc.)
  • The basic, global infrastructure of the cloud
  • Basic features of deploying and using the AWS Cloud
  • The shared responsibility model
  • Basic aspects of AWS security and compliance

INTENDED AUDIENCE

This webinar is intended for:

  • Business owners and IT professionals
  • Individuals interested in learning more about basic cloud concepts, AWS cloud terminology, and key AWS services

PREREQUISITES

No prior knowledge is required.

ACTIVITIES

This webinar includes:

  • Presentation
  • No questions can be asked during the webinar. After the webinar, questions asked in the chat will be addressed.

COURSE DURATION / PRICE

  • 3 hours / € 0.00

COURSE OUTLINE

  • MODULE 1: INTRODUCTION TO THE CLOUD AND AWS

    • Benefits of Cloud Computing
    • Components of Cloud Computing
    • Deployment models for cloud computing

    • Module 2: AWS Services Overview

      • AWS service domains
      • Computing
      • Storage
      • Database
      • Networking and content delivery

    • Module 3: Security and compliance overview

      • Overview of security
      • The shared responsibility model
      • AWS Security Services
      • AWS Compliance

    Neue Termine in Planung!

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