Generative AI for Executives

Written by Annika Thiemann on 7. October 2024. Posted in AWS Training, Training.

Aws Advanced Training Partner
Aws Premium Consuting Partner

Generative AI for Executives

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 leverage generative artificial intelligence (AI) within your organization. We’ll cover how to drive business value with generative AI, the use cases across various industries, and the considerations to implement generative AI safely and responsibly. The goal of this course is to provide you with the fundamental concepts and tools you’ll need to successfully lead generative AI initiatives within your organization.

Course objectives

In this course, you will learn to:

Recognize the potential business value of generative AI
Identify real world use cases that you can implement today
Manage the people, process, and technology changes needed to be successful
Use generative AI safely and responsibly
Identify the specific steps you can take to get started with generative AI

Intended audience

This course is intended for experienced:

Executives and Senior Business Leaders

Prerequisites

None

Activities

This course includes:

Presentations
Group discussions

Course duration / Price

0,5 days
€ 395.00 (excl. tax) per person

Course outline

Modul 1: Introduction to Generative AI

Definitions and terminology
AWS approach to generative AI

Modul 2: Generative AI Use Cases

Common use cases
Real-world case studies

Modul 3: Overcoming Technical and Organizational Challenges

Security
Accuracy
Cost
People and culture

Modul 4: Implementation

Identifying your use case
Assessing data, technology, people, and processes
Evaluating business impact and scaling

Modul 5: Next Steps in Your Generative AI Journey

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

Neue Termine in Planung!

Designing and Implementing Storage on AWS

Written by Annika Thiemann on 2. October 2024. Posted in AWS Training, Training.

Aws Advanced Training Partner
Aws Premium Consuting Partner

Designing and Implementing Storage on AWS

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

Course description

AWS offers a broad portfolio of storage services and solutions with diverse capabilities for storing, accessing, and protecting your data. In this course, you will learn where, how, and when to take advantage of these different service offerings. You will learn which services to consider when looking to solve your data storage challenges. You will learn how to best evaluate your options in selecting the appropriate AWS storage service to meet your use case and business requirements. You will also gain a better understanding of how to store, manage, and protect your data in the cloud. Through a series of hands-on exercises that demonstrate the ease and power of AWS platform, you will learn how to quickly provision powerful storage solutions in minutes.

Course objectives

In this course, you will learn to:

Describe the benefits of the core AWS storage services and identify some of their primary
use cases
Select and design an appropriate storage solution according to application and business
requirements
Configure storage resources to work with the broad array of AWS service offerings
Select the right method to move data between on-premises workloads and the AWS Cloud
Design storage solutions to protect data at rest and in transit
Set up monitoring and observability for Cloud storage to gain insight into access patterns,
utilization, and efficiency
Design and optimize storage solutions according to cost, scalability, and performance
requirements

Intended audience

This course is intended for:

Solutions Architects
Cloud Storage Engineers
Cloud Operations Specialists
DevOps Engineers

Prerequisites

We recommend that attendees of this course have:

Attended the AWS Cloud Practitioner Essentials Training
Attended the AWS Technical Essentials Training
or equivalent realworld experience

Activities

This course includes:

Presentations
Hands-on labs
Demonstrations
Group exercises

Course duration / Price

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

Course outline

Day 1
- Modul 1: Introduction to Cloud Storage
  - Storage in the AWS cloud
  - Designing Well Architected Storage Solutions
  - Designing Durable and Available Storage Solutions
  - Building Accessible and Secure Storage Solutions
- Modul 2: Designing Object Storage Solutions in AWS
  - What is object storage?
  - Planning and designing your Amazon S3 deployment
  - Managing Amazon S3
  - Access Control with Amazon S3
  - Hands-On Lab: Exploring S3 Access Control and S3 Object Lambda
- Modul 3: Implementing Object Storage solutions with S3
  - Cost management and the data lifecycle
  - Managing data transfers into Amazon S3
  - Data protection in Amazon S3
  - Manage objects stored in Amazon S3 at scale
  - Hands-on Lab: Multi-Part Uploads, Batch Operations, and Cross-Region Replication with
    Amazon S3
- Modul 4: Designing Block storage solutions in AWS
  - Block storage fundamentals
  - Amazon Elastic Block Store (Amazon EBS)
  - Configuring EBS volume types
  - EC2 and EBS encryption
Day 2
- Module 5: Implementing Block Storage Solutions with Amazon EBS
  - Creating EBS volumes
  - Managing EBS volumes
  - Managing EBS snapshots at scale
  - Hands-On Lab: Managing EBS Volumes: Capacity, Performance, and Data Protection
- Modul 6: File Storage and Amazon EFS
  - Cloud-based file storage
  - Amazon EFS overview
  - Accessing Amazon EFS
  - Securing and protecting Amazon EFS file systems
  - Hands-On Lab: Using Amazon EFS with AWS Lambda and Amazon ECS
- Modul 7: Cloud file storage with Amazon FSx
  - Amazon FSx overview
  - Amazon FSx for Windows File Server
  - Amazon FSx for NetAPP ONTAP
  - Amazon FSx for OpenZFS
  - Amazon FSx for Lustre
  - Choosing an Amazon FSx service
  - Hands-On Lab: Working with FSx for NetApp ONTAP and FSx for OpenZFS
Day 3
- Modul 8: Hybrid and Edge Cloud Storage
  - Hybrid and edge cloud storage overview
  - Introduction to AWS Storage Gateway
  - AWS Storage Gateway architectures
  - AWS Snow Family
- Modul 9: Moving data to AWS
  - Moving data to AWS
  - Working with AWS DataSync
  - Implementing AWS Transfer Family
  - Hands-On Lab: Moving Data with Storage Gateway and DataSync
- Modul 10: Backup and Disaster Recovery with AWS
  - Designing a data protection strategy
  - AWS Backup
  - Creating backup plans
  - Working with AWS DRS
  - Hands-On Lab: Creating and Restoring Backups with AWS Backup
- Modul 11: Monitoring, Automating, and Optimizing your AWS Storage
  - AWS Observability Services
  - Amazon S3 Storage Lens
  - Amazon CloudWatch
  - AWS CloudTrail
  - AWS Config
  - AWS Compute Optimizer
  - Hands-On Lab: Storage Monitoring, Automation, and 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.

Neue Termine in Planung!

AWS Certified Data Engineer – Associate

Written by Gemma Corrado on 5. July 2024. 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 11. March 2024. 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:

Completed AWS Technical Essentials
Intermediate-level proficiency in Python

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!

Amazon SageMaker Studio for Data Scientists

Written by Gemma Corrado on 11. January 2024. 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.