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Amazon OpenSearch Service Essentials

Written by Annika Thiemann on . Posted in tecRacer Trainings, Topseller, Training.

Amazon OpenSearch Service Essentials

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

Course description

In the “Amazon OpenSearch Service” module of our “Cloud Essentials” course series, you will get to know the OpenSearch Service, a powerful search and analysis engine for various use cases such as advanced searches and log analyses. In one day of training, you will get a comprehensive overview of cluster deployment and management, data modeling, index design, data visualization with OpenSearch dashboards, and security features. Through numerous live demos and practical labs, you will gain your first hands-on experience with the OpenSearch service.

Course objectives

In this course, you will learn:

  • The basics of the Amazon OpenSearch Service
  • Understanding and using architecture and cluster setup
  • Operate OpenSearch with managed and serverless options
  • Use index design and data modeling effectively
  • Implement vector search, semantic search and sparse search as well as traditional BM25 search
  • Optimize searches and analyses, including filters, ranking and aggregations
  • Implement security policies
  • Create dashboards to visualize data
  • Apply best practices to optimize performance and security

Intended audience

This course is intended for:

  • People who need a powerful search solution for their applications
  • People who want to migrate their search and analytics workloads to AWS
  • DevOps engineers
  • Database and system administrators
  • Web developers who want to integrate advanced search capabilities into their applications

Activities

This course includes:

  • Training with instructor
  • Live demos
  • Practical labs

Course duration / Price

1 Day / € 750,00 plus VAT per person

Course outline

  • Introduction
  • OpenSearch architecture and concepts
  • Cluster setup and options (managed/serverless)
  • Index design and data structuring
  • Vector search, semantic search and sparse search as well as traditional BM25 search
  • Security options, scaling and performance optimization
  • Dashboards and visualization

 

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 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

In this advanced two-day course, software developers learn to build and customize AI solutions by using Amazon Bedrock programmatically. Through hands-on exercises and labs, participants will invoke foundation models through Amazon Bedrock APIs, implement Retrieval Augmented Generation (RAG) patterns with Amazon Bedrock Knowledge Bases, and develop AI agents with tool integration. The course focuses on the practical implementation of prompt engineering techniques, responsible AI practices with Amazon Bedrock Guardrails, open source framework integration, and architectural patterns for real-world business applications.

COURSE OBJECTIVES

In this course, you will learn to:

  • Develop generative AI applications using Amazon Bedrock.
  • Design architecture patterns of generative AI applications.
  • Configure Amazon Bedrock APIs to invoke foundation models (FMs) programmatically.
  • Develop agentic AI applications by integrating Amazon Bedrock tools and open source frameworks.
  • Build custom solutions with Retrieval Augmented Generation (RAG) and Amazon Bedrock Knowledge Bases.
  • Integrate open source SDKs with Amazon Bedrock to build business.
  • Optimize model responses by applying prompt engineering techniques.
  • Evaluate generative AI application components.
  • Implement responsible AI practices to protect generative AI.

INTENDED AUDIENCE

This course is intended for:

  • Software developers

PREREQUISITES

We recommend that attendees of this course have:

ACTIVITIES

This course includes:

  • Presentations
  • Demonstrations
  • Hands-on labs
  • Group exercises

COURSE DURATION / PRICE

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

Course outline

  • Day 1

    • Module 1: Exploring Components of Generative AI Applications on AWS
      • Understanding generative AI concepts
      • Identifying AWS generative AI stack components
      • Designing generative AI application components
    • Module 2: Programming with Amazon Bedrock
      • Guiding model response generation
      • Using Amazon Bedrock programmatically
      • Hands-on lab: Develop with Amazon Bedrock APIs
      • Hands-on lab: Develop Streaming Patterns with Amazon Bedrock APIs
    • Module 3: Applying Prompt Engineering for Developers
      • Introducing prompt engineering
      • Introducing prompt techniques
      • Optimizing prompts for better results
    • Module 4: Using Amazon Bedrock APIs in Common Architectures
      • Implementing architecture patterns with Amazon Bedrock APIs
      • Exploring common use cases
      • Adding conversational memory to extend context
      • Hands-on lab: Develop Conversation Patterns with Amazon Bedrock APIs
    • Module 5: Customizing Generative AI Responses with RAG
      • Implementing Retrieval Augmented Generation (RAG)
      • Using Amazon Bedrock Knowledge Bases
      • Hands-on lab: Develop Retrieval Augmented Generation (RAG) Applications with Amazon Bedrock Knowledge Bases
    • Module 6: Integrating Open Source Frameworks with Amazon Bedrock
      • Invoking a foundation model in Amazon Bedrock using LangChain
      • Using LangChain for context-aware responses
      • Hands-on lab: Develop a Generative AI Application Pattern using Open Source Frameworks and Amazon Bedrock Knowledge Bases

  • Day 2

    • Module 7: Evaluating Generative AI Application Components
      • Evaluating application components • Evaluating model output
      • Evaluating RAG output
      • Optimizing latency and cost
      • Hands-on lab: Evaluating Retrieval Augmented Generation (RAG) Applications
    • Module 8: Implementing Responsible AI
      • Understanding responsible AI
      • Mitigating bias and addressing prompt misuses
      • Using Amazon Bedrock Guardrails
      • Hands-on lab: Securing Generative AI Applications Using Bedrock Guardrails
    • Module 9: Using Tools and Agents in Generative AI Applications
      • Using tools
      • Understanding AI agents
      • Understanding open source agentic frameworks
      • Understanding agent interoperability
    • Module 10: Developing Amazon Bedrock Agents
      • Implementing Amazon Bedrock Flows
      • Designing Amazon Bedrock Agents
      • Developing Amazon Bedrock Inline Agents
      • Designing multi-agent collaboration
      • Using Amazon Bedrock AgentCore
      • Hands-on lab: Developing Amazon Bedrock Agents Integrated with Amazon Bedrock Knowledge Bases and Guardrails

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

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