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Agentic AI Foundations

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


Agentic AI Foundations

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

Course description

In this course, you’ll explore the core principles and strategies for designing Agentic AI systems using AWS services. You’ll learn how Agentic AI differs from traditional conversational systems, and how to use tools like Amazon Q, Kiro, Amazon Bedrock Agents, and Amazon Bedrock AgentCore to build autonomous, goal-driven solutions that solve real-world problems.

COURSE OBJECTIVES

In this course, you will learn to:

  • Summarize the evolution of Agentic AI and define what makes something “agentic” 
  • Identify core components of agentic systems
  • Distinguish between workflow, autonomous, and hybrid agents
  • Compare AWS service options for Agentic AI
  • Describe capabilities and use cases of Amazon Q Developer, Amazon Q Business, and Kiro
  • Explain Amazon Bedrock AgentCore and Amazon Bedrock Agents fundamentals
  • Identify basic implementation patterns for Agentic AI
  • Describe observability and interoperability patterns for production agentic AI systems

INTENDED AUDIENCE

This course is intended for:

  • Software developers new to Agentic AI seeking foundational knowledge
  • Technical professionals exploring AI capabilities and interested in core components and applications of agentic AI
  • Development teams evaluating Agentic AI solutions and needing to differentiate between agent types
  • AWS Users expanding into Agentic AI, including current users of Amazon Q Developer, Amazon Q Business, and Amazon Bedrock Agents

Prerequisites

We recommend that attendees of this course have:

    ACTIVITIES

    This course includes:

    • Presentations
    • Hands-on lab,
    • Group exercises

    COURSE DURATION / PRICE

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

    Course outline

    Day 1

    • Module 1: From LLMs to Agents
      • Understanding Large Language Models (LLMs)
      • Innovations powering agents
      • Evolution timeline from LLMs to Agents
    • Module 2: Exploring Agentic AI
      • Understanding Agentic AI
      • Types of AI agents
      • Agentic AI applications
    • Module 3: Understanding Agentic AI Workflows
      • Workflow patterns
      • Amazon Bedrock flows overview
    • Module 4: Introducing Autonomous Agents
      • How Autonomous Agents work
      • ReAct
      • ReWoo
      • Multi-agent collaboration
      • AWS Agentic AI solutions
    • Module 5: Amazon Q and Agentic Development Tools
      • Amazon Q Developer
      • Amazon Q Business
      • Amazon Q in AWS Services
      • Kiro: AI-powered IDE with spec-driven development
    • Module 6: Agentic AI with Amazon Bedrock
      • Amazon Bedrock Agents
      • Amazon Bedrock AgentCore
      • Hands-on lab: Explore Amazon Bedrock Agents integrated with Amazon Bedrock Knowledge Bases and Amazon Bedrock Guardrails
    • Module 7: Building DIY Solutions
      • DIY solutions
      • Observability and Monitoring
      • Agent Interoperability
    • Module 8: Course Wrap-up
      • Next steps and additional resources
      • Course summary

    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

    Exam Prep: AWS Certified AI Practitioner

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


    Exam Prep: AWS Certified AI Practitioner

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

    Course description

    This intermediate-level course prepares you for the AWS Certified AI Practitioner exam by providing a comprehensive exploration of the exam topics. You’ll delve into the key areas covered on the exam, understanding how they relate to developing AI and machine learning solutions on the AWS platform. Through detailed explanations and walkthroughs of exam-style questions, you’ll reinforce your knowledge, identify gaps in your understanding, and gain valuable strategies for tackling questions effectively. The course includes review of exam-style sample questions, to help you recognize incorrect responses and hone your test-taking abilities. By the end, you’ll have a firm grasp on the concepts and practical applications tested on the AWS Certified AI Practitioner certification exam.

    COURSE OBJECTIVES

    In this course, you will learn to:

    • Identify the scope and content tested by the AWS Certified AI Practitioner exam.
    • Practice exam-style questions and evaluate your preparation strategy.
    • Examine use cases and differentiate between them.

    INTENDED AUDIENCE

    This course is intended for:

    • individuals who are preparing for the AWS Certified AI Practitioner exam

    Prerequisites

    You are not required to take any specific training before taking this course. However, the following
    prerequisite knowledge is recommended prior to taking the AWS Certified Machine Learning Engineer –
    Associate exam.

    Recommended AWS knowledge

    • Familiarity with the core AWS services (for example, Amazon EC2, Amazon S3, AWS Lambda, and Amazon SageMaker AI) and AWS core services use cases.
    • Suggested to have up to 6 months of exposure to AI and ML technologies on AWS.
    • Are familiar with, but do not necessarily build, solutions using AI and ML technologies on AWS.
    • Familiarity with the AWS shared responsibility model for security and compliance in the AWS Cloud.
    • Familiarity with AWS Identity and Access Management (IAM) for securing and controlling access to AWS resources.
    • Familiarity with the AWS global infrastructure, including the concepts of AWS Regions, Availability Zones, and edge locations.
    • Familiarity with AWS service pricing models.

    Recommended courses

    The following courses (or similar) are recommended but not required.

    • Fundamentals of Machine Learning and Artificial Intelligence (1 hour)
    • Exploring Artificial Intelligence Use Cases and Applications (1 hour)
    • Responsible Artificial Intelligence Practices (1 hour)
    • Developing Machine Learning Solutions (1 hour)
    • Developing Generative Artificial Intelligence Solutions (1 hour)
    • Essentials of Prompt Engineering (1 hour)
    • Optimizing Foundation Models (1 hour)
    • Security, Compliance, and Governance for AI Solutions (1 hour)
    • Generative AI for Executives (0.25 hour)
    • Amazon Q Business Getting Started (0.75 hour)
    • Amazon Bedrock Getting Started (1 hour)
    • Getting Started with Amazon Comprehend: Custom Classification (1.25 hours)
    • Build a Question-Answering Bot Using Generative AI (1.5 hours)

    ACTIVITIES

    This course includes:

    • Subject overview presentations
    • Exam-style questions
    • Use cases
    • Group discussions and activities

    COURSE DURATION / PRICE

    • 1 day
    • € 775.00 (excl. tax) per person (DE)

    Course outline

    Day 1

    • Domain 1: Fundamentals of AI and ML
      • Explain basic AI concepts and terminologies
      • Identify practical use cases for AI
      • Describe the ML development lifecycle
    • Domain 2: Fundamentals of Generative AI
      • Explain the basic concepts of generative AI
      • Understand the capabilities and limitations of generative AI for solving business problems
      • Describe AWS infrastructure and technologies for building generative AI applications
    • Domain 3: Applications of Foundation Models
      • Describe design considerations for applications that use foundation models
      • Choose effective prompt engineering techniques
      • Describe the training and fine-tuning process for foundation models
      • Describe methods to evaluate foundation model performance
    • Domain 4: Guidelines for Responsible AI
      • Explain the development of AI systems that are responsible
      • Recognize the importance of transparent and explainable models
    • Domain 5: Security, Compliance, and Governance for AI Solutions
      • Explain methods to secure AI systems
      • Recognize governance and compliance regulations for AI systems

    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

    Exam Prep: AWS Certified Machine Learning Engineer – Associate

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


    Exam Prep: AWS Certified Machine Learning Engineer – Associate

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

    Course description

    This intermediate-level course prepares you for the AWS Certified Machine Learning Engineer – Associate exam by providing a comprehensive exploration of the exam topics. You’ll delve into the key areas covered on the exam, understanding how they relate to developing AI and machine learning solutions on the AWS platform. Through detailed explanations and walkthroughs of examstyle questions, you’ll reinforce your knowledge, identify gaps in your understanding, and gain valuable strategies for tackling questions effectively. The course includes review of exam-style sample questions, to help you recognize incorrect responses and hone your test-taking abilities. By the end, you’ll have a firm grasp on the concepts and practical applications tested on the AWS Certified Machine Learning Engineer – Associate exam.

    COURSE OBJECTIVES

    In this course, you will learn to:

    • Identify the scope and content tested by the AWS Certified Machine Learning Engineer – Associate exam.
    • Practice exam-style questions and evaluate your preparation strategy.
    • Examine use cases and differentiate between them.

    INTENDED AUDIENCE

    This course is intended for:

    • individuals who are preparing for the AWS Certified Machine Learning
      Engineer – Associate exam

    Prerequisites

    You are not required to take any specific training before taking this course. However, the following
    prerequisite knowledge is recommended prior to taking the AWS Certified Machine Learning Engineer –
    Associate exam.

    General IT knowledge
    Learners are recommended to have the following:

    • Suggested 1 year of experience in a related role such as a backend software developer, DevOps
      developer, data engineer, or data scientist.
    • Basic understanding of common ML algorithms and their use cases
    • Data engineering fundamentals, including knowledge of common data formats, ingestion, and
      transformation to work with ML data pipelines
    • Knowledge of querying and transforming data
    • Knowledge of software engineering best practices for modular, reusable code development,
      deployment, and debugging
    • Familiarity with provisioning and monitoring cloud and on-premises ML resources
    • Experience with continuous integration and continuous delivery (CI/CD) pipelines and
      infrastructure as code (IaC)
    • Experience with code repositories for version control and CI/CD pipelines

    Recommended AWS knowledge
    Learners are recommended to be able to do the following:

    • Suggested 1 year of experience using Amazon SageMaker AI and other AWS services for ML
      engineering.
    • Knowledge of Amazon SageMaker AI capabilities and algorithms for model building and
      deployment
    • Knowledge of AWS data storage and processing services for preparing data for modeling
    • Familiarity with deploying applications and infrastructure on AWS
    • Knowledge of monitoring tools for logging and troubleshooting ML systems
    • Knowledge of AWS services for the automation and orchestration of CI/CD pipelines
    • Understanding of AWS security best practices for identity and access management, encryption,
      and data protection

    ACTIVITIES

    This course includes:

    • Subject overview presentations
    • Exam-style questions
    • Use cases
    • Group discussions and activities

    COURSE DURATION / PRICE

    • 1 day
    • € 775.00 (excl. tax) per person (DE)

    Course outline

    Day 1

    • Domain 1: Data Preparation for Machine Learning (ML)
      • Ingest and store data.
      • Transform data and perform feature engineering.
      • Ensure data integrity and prepare data for modeling.
    • Domain 2: ML Model Development
      • Choose a modeling approach.
      • Train and refine models.
      • Analyze model performance.
    • Domain 3: Deployment and Orchestration of ML Workflows
      • Select deployment infrastructure based on existing architecture and requirements.
      • Create and script infrastructure based on existing architecture and requirements.
      • Use automated orchestration tools to set up continuous integration and continuous delivery (CI/CD) pipelines.
    • Domain 4: ML Solution Monitoring, Maintenance, and Security
      • Monitor model interference.
      • Monitor and optimize infrastructure costs.
      • Secure AWS resources.

    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

    Machine Learning Engineering on AWS

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


    Machine Learning Engineering on AWS

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

    Course description

    Machine Learning (ML) Engineering on AWS is a 3-day intermediate course designed for ML professionals seeking to learn machine learning engineering on AWS. Participants learn to build, deploy, orchestrate, and operationalize ML solutions at scale through a balanced combination of theory, practical labs, and activities. Participants will gain practical experience using AWS services such as Amazon SageMaker AI and analytics tools such as Amazon EMR to develop robust, scalable, and production-ready machine learning applications.

    COURSE OBJECTIVES

    In this course, you will learn to do the following:

    • Explain ML fundamentals and its applications in the AWS Cloud.
    • Process, transform, and engineer data for ML tasks by using AWS services.
    • Select appropriate ML algorithms and modeling approaches based on problem
      requirements and model interpretability.
    • Design and implement scalable ML pipelines by using AWS services for model training,
      deployment, and orchestration.
    • Create automated continuous integration and delivery (CI/CD) pipelines for ML workflows.
    • Discuss appropriate security measures for ML resources on AWS.
    • Implement monitoring strategies for deployed ML models, including techniques for
      detecting data drift.

    INTENDED AUDIENCE

    This course is intended for:

    • This course is designed for professionals who are interested in building, deploying, and
      operationalizing machine learning models on AWS. This could include current and in-training
      machine learning engineers who might have little prior experience with AWS.
    • DevOps Engineer
    • Developer
    • SysOps Engineer

    Prerequisites

    We recommend that attendees of this course have the following:

    • Familiarity with basic machine learning concepts
    • Working knowledge of Python programming language and common data science libraries
      such as NumPy, Pandas, and Scikit-learn
    • Basic understanding of cloud computing concepts and familiarity with AWS
    • Experience with version control systems such as Git (beneficial but not required)

    ACTIVITIES

    This course includes:

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

    COURSE DURATION / PRICE

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

    Course outline

    • Day 1

      • Module 0: Course Introduction
      • Module 1: Introduction to Machine Learning (ML) on AWS
        • Introduction to ML
        • Amazon SageMaker AI
        • Responsible ML
      • Module 2: Analyzing Machine Learning (ML) Challenges
        • Evaluating ML business challenges
        • ML training approaches
        • ML training algorithms
      • Module 3: Data Processing for Machine Learning (ML)
        • Data preparation and types
        • Exploratory data analysis
        • AWS storage options and choosing storage
      • Module 4: Data Transformation and Feature Engineering
        • Handling incorrect, duplicated, and missing data
        • Feature engineering concepts
        • Feature selection techniques
        • AWS data transformation services
        • Lab 1: Analyze and Prepare Data with Amazon SageMaker Data Wrangler and Amazon EMR
        • Lab 2: Data Processing Using SageMaker Processing and the SageMaker Python SDK

    • Day 2

      • Module 5: Choosing a Modeling Approach
        • Amazon SageMaker AI built-in algorithms
        • Amazon SageMaker Autopilot
        • Selecting built-in training algorithms
        • Model selection considerations
        • ML cost considerations
      • Module 6: Training Machine Learning (ML) Models
        • Model training concepts
        • Training models in Amazon SageMaker AI
        • Lab 3: Training a model with Amazon SageMaker AI
      • Module 7: Evaluating and Tuning Machine Learning (ML) models
        • Evaluating model performance
        • Techniques to reduce training time
        • Hyperparameter tuning techniques
        • Lab 4: Model Tuning and Hyperparameter Optimization with Amazon SageMaker AI
      • Module 8: Model Deployment Strategies
        • Deployment considerations and target options
        • Deployment strategies
        • Choosing a model inference strategy
        • Container and instance types for inference
        • Lab 5: Shifting Traffic

    • Day 3

      • Module 9: Securing AWS Machine Learning (ML) Resources
        • Access control
        • Network access controls for ML resources
        • Security considerations for CI/CD pipelines
      • Module 10: Machine Learning Operations (MLOps) and Automated Deployment
        • Introduction to MLOps
        • Automating testing in CI/CD pipelines
        • Continuous delivery services
        • Lab 6: Using Amazon SageMaker Pipelines and the Amazon SageMaker Model Registry with Amazon SageMaker Studio
      • Module 11: Monitoring Model Performance and Data Quality
        • Detecting drift in ML models
        • SageMaker Model Monitor
        • Monitoring for data quality and model quality
        • Automated remediation and troubleshooting
        • Lab 7: Monitoring a Model for Data Drift
      • Module 12: 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).
    The Course language is German, on request also in English.

    Neue Termine in Planung!

    Continue reading

    Generative AI Essentials on AWS

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


    Generative AI Essentials on AWS

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

    Course description

    In this course, you will learn about the fundamental concepts, methods, and strategies for using generative AI. You will gain a solid understanding of use cases where generative AI can provide solutions and address business needs. Additionally, you will learn about practical insights into technologies related to generative AI and how you can use those technologies to solve real-world problems. By the end of the course, you will explore project planning and how to discuss implementation of generative AI in your organization.

    COURSE OBJECTIVES

    In this course, you will learn to:

    • Summarize generative AI concepts, methods, and strategies
    • Discuss the appropriate use of generative AI and machine learning and their technologies
    • Describe how to use generative AI responsibly and safely
    • Recognize the types of generative AI solutions with specific use cases
    • Explain implementation and project planning of generative AI to your organization

    INTENDED AUDIENCE

    This course is intended for:

    • Business analysts
    • IT supports
    • Marketing professionals
    • Product or project managers
    • Line-of-business or IT managers
    • Sales professionals

      ACTIVITIES

      This course includes:

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

      COURSE DURATION / PRICE

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

      Course outline

      Day 1

      • Module 1: Introducing Generative AI
        • Generative AI explained
        • Foundation models
        • AWS generative AI services
        • Demo: Generative AI solution
      • Module 2: Exploring Generative AI Use Cases
        • Identify suitable use cases
        • Generative AI applications and use cases
        • Explore generative AI use case scenarios
        • Use case for class
      • Module 3: Essentials of Prompt Engineering
        • Introduction to prompt engineering
          Prompt design best practices
        • Advanced prompting strategies
        • Model settings and parameters
        • Hands-on Lab: Optimizing Slogan Generation with Amazon Bedrock
      • Module 4: Responsible AI Principles and Considerations
        • Introduction to responsible AI
        • Core dimensions of responsible AI
        • Generative AI considerations
        • Hands-on Lab: Implementing Responsible AI Principles with Amazon Bedrock Guardrails
      • Module 5: Security, Governance, and Compliance
        • Security overview
        • Adverse prompts
        • Generative AI security services
        • Governance
        • Compliance
      • Module 6: Implementing Generative AI Projects
        • Introduction – Generative AI application
        • Define a use case
        • Select a foundational model
        • Improve performance
        • Evaluate results
        • Deploy the application
        • Demo: Amazon Q Business
      • Module 7: Integrating Generative AI into the Development Lifecycle
        • Introduction
        • Hands-on Lab: Capstone – Creating a Project Plan with Generative AI
      • Module 8: Course Wrap-up
        • Next steps and additional resources
        • Course summary

      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