Generative AI & Amazon Bedrock Ultimate Hands-on Course

What You’ll Learn:

• Introduction to AWS Bedrock: Understand the fundamentals of Amazon’s powerful generative AI service.

• Navigating the Text Playground: Get hands-on experience with the console environment, where you can input prompts and adjust inference parameters to see real-time model responses.

• Model Exploration: Discover the variety of text models available and learn how to choose the right one for your needs.

• Practical Applications: Learn how to apply these models to real-world scenarios, from content generation to natural language processing tasks.

• Advanced Techniques: Dive deeper into fine-tuning models and optimizing performance for your specific use cases.

Why This Course?

• Expert Guidance: Learn from industry experts with practical insights and tips.

• Interactive Learning: Engage with interactive exercises and real-world examples to solidify your understanding.

• Comprehensive Coverage: From setup to advanced usage, this course covers all you need to know about AWS Bedrock’s Text Playground.

The Chat Playground in AWS Bedrock is a feature that allows you to experiment with chat models provided by Amazon Bedrock. Here’s a brief overview:

1. Experimentation Environment: It provides a console environment where you can test and run inferences on different chat models with various configurations before integrating them into your applications.

2. Prompt Engineering: You can enter prompts, which are sentences or questions that set up a scenario or task for the model. You can also adjust inference parameters to influence the model’s responses.

3. Configuration Options: The playground allows you to modify configurations such as temperature and top-k sampling to see how these changes affect the model’s output.

4. Multimodal Support: If the model supports it, you can include images or documents along with your prompts to get more contextually rich responses.

5. System Prompts: For some models, you can specify system prompts that provide instructions or context about the task or persona the model should adopt during the conversation. This feature is particularly useful for developers and data scientists who want to fine-tune their models and understand how different settings impact the generated responses.

What You’ll Learn:

• Introduction to AWS Bedrock: Get a comprehensive overview of Amazon’s cutting-edge generative AI service.

• Exploring the Image Playground: Learn how to navigate the Image Playground, input text prompts, and adjust parameters to generate high-quality images.

• Model Selection and Usage: Discover the various image generation models available, including Stability AI’s latest models, and understand how to choose the best one for your needs.

• Practical Applications: See how these models can be applied to real-world scenarios such as marketing, advertising, media, and more.

• Advanced Techniques: Dive into advanced features like fine-tuning models and optimizing image outputs for specific use cases.

• Best Practices for Image Generation: Learn tips and tricks to get the most out of your AI-generated images, ensuring they meet your project’s requirements.

Why This Course?

• Expert Instruction: Gain insights from industry professionals with hands-on experience in AI and image generation.

• Interactive Learning: Engage with practical exercises and real-world examples to solidify your understanding.

• Comprehensive Coverage: From setup to advanced techniques, this course covers everything you need to know about AWS Bedrock’s Image Playground.

We will use AWS Gen AI Rekognition service to analyze images from local computer like detect facial expression, type of flower etc.

Amazon Rekognition is a powerful tool for image analysis, leveraging deep learning technology to provide a wide range of capabilities.

Here are some key features and use cases:

1. Key Features: Object and Scene Detection: Identifies objects, scenes, and activities in images. This can be used for cataloging and organizing image libraries.

2. Facial Analysis: Detects faces in images and analyzes attributes such as age, gender, emotions, and more. It can also compare faces to determine if they match.

3. Text Detection: Extracts text from images, including skewed and distorted text, which is useful for reading signs, social media posts, and product packaging.

4. Content Moderation: Detects inappropriate or unsafe content in images, helping to maintain safe and compliant digital environments.

5. Celebrity Recognition: Identifies well-known individuals in images, which can be useful for media and marketing purposes.

6. Custom Labels: Allows you to train custom models to detect specific objects or scenes relevant to your business needs.

What You’ll Learn:

• Introduction to Amazon Bedrock and Lex: Get acquainted with the powerful tools and services that Amazon offers for building generative AI applications.

• Setting Up Your Environment: Learn how to configure your AWS environment and set up the necessary resources for your chatbot.

• Creating Your First Chatbot: Step-by-step instructions on building a basic chatbot, including defining intents, slots, and utterances.

• Integrating Generative AI: Discover how to enhance your chatbot with generative AI capabilities using Amazon Bedrock, enabling more natural and dynamic interactions.

• Advanced Features and Customization: Explore advanced features such as context management, multi-turn conversations, and integrating external data sources.

• Testing and Deployment: Learn best practices for testing your chatbot and deploying it to production environments.

Why This Course?

• Expert Instruction: Benefit from the insights and experience of industry professionals who have successfully implemented AI solutions.

• Hands-On Learning: Engage with practical exercises and real-world examples to solidify your understanding and skills.

• Comprehensive Coverage: From basics to advanced techniques, this course covers everything you need to know to build and deploy a powerful chatbot.

What You’ll Learn:

• Introduction to Sentiment Analysis: Understand the basics of sentiment analysis and its applications in various industries.

• Setting Up Your Environment: Learn how to configure your Python environment and install necessary libraries for sentiment analysis.

• Using Pre-trained Models: Discover how to leverage pre-trained sentiment analysis models to quickly get started with your projects.

• Building Custom Models: Step-by-step instructions on creating your own sentiment analysis models using Generative AI techniques.

• Data Preparation and Processing: Learn how to clean and preprocess text data for accurate sentiment analysis.

• Model Training and Evaluation: Understand the process of training your models and evaluating their performance using real-world datasets.

• Advanced Techniques: Explore advanced topics such as fine-tuning models, handling imbalanced data, and optimizing model performance.

• Practical Applications: See how sentiment analysis can be applied to social media monitoring, customer feedback analysis, and more.

  
  

Why This Course?

• Expert Guidance: Learn from industry experts with hands-on experience in AI and machine learning.

• Interactive Learning: Engage with practical exercises and real-world examples to solidify your understanding.

• Comprehensive Coverage: From basics to advanced techniques, this course covers everything you need to know about sentiment analysis with Python and GenAI.

In this video, we explore how to generate breathtaking images using Amazon Bedrock’s Stable Diffusion model. Discover the power of generative AI and how it can transform simple text prompts into high-quality visuals.

We’ll cover:

1. Introduction to Amazon Bedrock and Stable Diffusion

2. Step-by-Step Guide to Generating Images 

3. Tips and Tricks for Best Results

4, Real-World Applications and Use Cases

Whether you’re a developer, artist, or AI enthusiast, this video will provide you with the knowledge and tools to create amazing AI-generated images. Join us on this creative journey and see how easy it is to bring your ideas to life with Amazon Bedrock.

In this tutorial, we dive deep into the world of social media sentiment analysis using Amazon Comprehend. Learn how to harness the power of AWS to analyze sentiments from various social media platforms and gain valuable insights into public opinion in an end-to-end project.

What You’ll Learn:

1. Setting Up Your Environment: How to store your input files in an Amazon S3 bucket. Using Amazon Comprehend: Step-by-step guide to analyzing social media posts for sentiment.

2. Data Processing: How to organize and store output files, including the corresponding post, social media platform name, and sentiment, in a structured format.

3. Real-World Application: Practical examples and use cases to help you understand the impact of sentiment analysis on business decisions.

This Hands-On Tutorial contains follow along with detailed instructions and code.

In this video, we’ll show you how to convert handwritten text and PDF files into typed text in a .txt file using Amazon Textract. Whether you’re looking to digitize your handwritten notes or extract text from PDFs, Amazon Textract makes it easy and efficient. Follow along as we guide you through each step of the process.

What You’ll Learn:

1. Introduction to Amazon Textract: Understand what Amazon Textract is and how it works.

2. Uploading Your Documents: Step-by-step instructions on how to upload handwritten documents and PDFs to Amazon Textract.

3. Extracting Text: See how to use Textract to extract text from your documents.

4. Saving as .txt File: Learn how to save the extracted text as a .txt file for easy access and editing.

Why Use Amazon Textract?

1. Accuracy: High accuracy in recognizing both printed and handwritten text.

2. Efficiency: Quickly processes large volumes of documents.

3. Versatility: Works with various document types, including PDFs and images.

In this exciting video, we dive deep into the world of automation and data science with a hands-on project using Amazon SageMaker. Whether you’re a seasoned data scientist or just starting out, this tutorial will guide you through the process of creating training and testing datasets from user input data.

What You’ll Learn:

1. Introduction to Amazon SageMaker: Understand the basics and why it’s a game-changer for data scientists. Automating Dataset Creation: Step-by-step guide to automate the creation of training and testing datasets.

2. User Input Integration: Learn how to seamlessly integrate user input data into your datasets.

Best Practices: Tips and tricks to optimize your workflow and ensure high-quality data.

Welcome to our step-by-step tutorial on how to build a knowledge base for Amazon Bedrock using a PDF file! 

In this video, we’ll guide you through the entire process of setting up a knowledge base, from configuring your data source to querying your knowledge base for insightful responses. Whether you’re a developer, data scientist, or just curious about Amazon Bedrock, this tutorial is for you.

What you’ll learn:

1. Introduction to Amazon Bedrock: Understand the basics and benefits of using Amazon Bedrock for your AI applications.

2. Setting Up Your Environment: Learn how to configure your AWS environment and necessary IAM roles.

3. Uploading Your PDF File: Step-by-step instructions on how to upload your PDF file to an Amazon S3 bucket.

4. Creating a Knowledge Base: Detailed walkthrough on creating and configuring your knowledge base in the Amazon Bedrock console.

5. Data Ingestion and Parsing: How to ingest and parse your PDF file to generate embeddings.

6. Querying Your Knowledge Base: Learn how to query your knowledge base and retrieve meaningful information.

Welcome to our latest tutorial on automating text file conversions to CSV or JSON formats using Amazon Bedrock Generative AI and Amazon SageMaker! In this video, we’ll walk you through a step-by-step guide on how to leverage the powerful capabilities of Amazon Bedrock and SageMaker to streamline your data processing tasks. Whether you’re dealing with large datasets or just looking to simplify your workflow, this tutorial has got you covered.

What You’ll Learn:

1. Introduction to Amazon Bedrock & SageMaker: Understand the basics and how these tools can revolutionize your data handling.

2. Setting Up Your Environment: A quick setup guide to get you started with Amazon Bedrock and SageMaker.

3. Converting Text Files to CSV/JSON: Detailed instructions on how to automate the conversion process using generative AI.

4. Practical Examples: Real-world scenarios to demonstrate the efficiency and effectiveness of these tools.

5. Tips & Best Practices: Learn the best practices to ensure smooth and error-free conversions.

Why Watch?

1. Save time and reduce manual effort in data conversion.

2. Enhance your data processing pipeline with cutting-edge AI technology.

3. Gain insights into the practical applications of Amazon Bedrock and SageMaker.

Welcome to our comprehensive tutorial on building three essential web applications using Amazon Cloud9 and Bedrock.

In this video, we’ll guide you through the step-by-step process of creating:

1. Log Analysis and Alerting System: Learn how to set up a robust system to monitor and analyze logs in real-time, ensuring you can quickly identify and respond to critical issues.

2. Invoice Analysis System: Discover how to automate the extraction and analysis of invoice data, streamlining your financial operations and improving accuracy.

3. Customer Review Sentiment Analysis: Explore how to leverage machine learning to analyze customer reviews, providing valuable insights into customer sentiment and helping you make data-driven decisions.

   
   

What You’ll Learn:

1. Setting up your development environment with Amazon Cloud9.

2. Utilizing Bedrock for seamless integration and deployment.

3. Implementing real-time log monitoring and alerting.

4. Automating invoice data extraction and analysis.

5. Performing sentiment analysis on customer reviews using advanced ML techniques.

Tools and Technologies:

1. Amazon Cloud9: A cloud-based integrated development environment (IDE) that lets you write, run, and debug your code with just a browser.

2. Bedrock: A powerful platform for building and deploying applications with ease.

In this comprehensive tutorial, we’ll explore Amazon Guardrails and demonstrate how to create robust filters to block specific words, hate speech, violence, insults, and more. Perfect for developers, content moderators, and anyone interested in AI safety, this hands-on guide will help you maintain a respectful and safe online environment. 

What You’ll Learn:

1. Introduction to Amazon Guardrails: Understand the fundamentals and significance of content filtering.

2. Setting Up Your Environment: Step-by-step instructions to get started with Amazon Guardrails.

3. Creating Word Filters: Learn how to block specific words and phrases effectively.

4. Blocking Hate Speech: Configure filters to detect and prevent hate speech.

5. Filtering Violence and Insults: Set up filters to block violent content and insults.

6. Testing and Fine-Tuning: Ensure your filters are working correctly and make necessary adjustments.

7. Best Practices: Tips to maintain a safe and respectful environment using Amazon Guardrails.

In this video, we dive deep into the fascinating world of invisible watermarks in AI-generated images using Amazon Bedrock’s cutting-edge technology. Learn how Amazon’s Titan Image Generator embeds these hidden signatures and how the new watermark detection API can reveal them.

We’ll cover:

1. What are Invisible Watermarks

2. How Amazon Bedrock’s Titan Image Generator Works

3. Step-by-Step Guide to Detecting Watermarks

4. Real-World Applications and Benefits 

Whether you’re a content creator, a tech enthusiast, or just curious about AI, this video will provide you with valuable insights into the importance of watermark detection in maintaining transparency and authenticity in digital media.

In this video, we dive deep into the world of Amazon SageMaker and show you how to optimize your machine learning training costs using Managed Spot Training.

What You’ll Learn:

1. Introduction to Amazon SageMaker: Understand the basics of this powerful, fully managed machine learning service.

2. Spot Instances Explained: Learn how Amazon EC2 Spot Instances can save you up to 90% on training costs.

3. Setting Up Managed Spot Training: Step-by-step guide to configure and run your ML training jobs using Spot Instances.

4. Checkpointing and Resuming Jobs: Discover how to use checkpoints to save progress and resume interrupted jobs seamlessly.

5. Real-World Examples: See Managed Spot Training in action with popular ML frameworks like TensorFlow, PyTorch, and XGBoost.

Why Watch?

1. Cost Efficiency: Learn how to significantly reduce your ML training expenses.

2. Hands-On Tutorial: Follow along with practical examples and detailed explanations.

3. Expert Tips: Get insights and best practices from industry experts.

In this video, we’ll guide you through the process of building a robust web application tailored for the real estate and photography business using Amazon Web Services (AWS). We’ll leverage powerful tools like Amazon Cloud9, ECS, ECR, and Bedrock Generative AI to create a high-availability, secure, and intelligent platform.

What You’ll Learn:

1. Setting Up Your Development Environment: Using Amazon Cloud9 for seamless coding and collaboration.

2. Containerizing Your Application: Deploying with Amazon ECS and storing images in Amazon ECR.

3. Implementing AI Features: Utilizing Amazon Bedrock Generative AI to generate and suggest images based on user searches.

4. Ensuring High Availability: Configuring an Application Load Balancer for optimal performance.

5. Enhancing Security: Setting up security groups for your load balancer to protect your application.

   
   

Why Watch?

1. Hands-On Demonstration: Follow along with step-by-step instructions.

2. Expert Tips: Learn best practices for building scalable and secure web applications.

3. Real-World Application: See how these technologies can be applied to a real estate and photography business.

In this video, we explore how to build a Customer Shopping Order System using Amazon Transcribe. This hands-on guide covers the entire end-to-end architecture, including the use of custom vocabulary to enhance transcription accuracy. We’ll also show you how to handle .WAV files stored in an Amazon S3 bucket.

What You’ll Learn:

1. Introduction to Amazon Transcribe: Discover the basics and benefits of using Amazon Transcribe for speech-to-text conversion.

2. Setting Up AWS: Step-by-step instructions to configure your AWS account and services.

3. Integrating Amazon Transcribe: Implement Amazon Transcribe to convert customer voice inputs into actionable text.

4. Using Custom Vocabulary: Enhance transcription accuracy by incorporating custom vocabulary tailored to your business needs.

5. Handling .WAV Files in S3: Learn how to store and process .WAV files in Amazon S3.

6. End-to-End Testing: Ensure your system works seamlessly from voice input to order confirmation.

Why Watch This Video?

1. Hands-On Experience: Follow along with practical examples and real-world scenarios.

2. Expert Guidance: Get tips and tricks from industry professionals.

3. Comprehensive Coverage: From setup to deployment, we’ve got you covered.

In this video, we’ll explore how Amazon Bedrock Agents can revolutionize your workflow by automating complex, multi-step tasks using advanced AI. Whether you’re a developer, data scientist, or tech enthusiast, this tutorial will show you how to harness the power of generative AI to boost productivity and efficiency.

What You’ll Learn:

1. Introduction to Amazon Bedrock Agents: Understand the basics and benefits.

2. Setting Up Your First Agent: Step-by-step guide to creating and configuring an agent.

3. Orchestrating Multi-Step Tasks: Learn how agents can seamlessly execute tasks using natural language input. 4. Integrating with Company Systems: See how agents connect to your data sources and APIs.

5. Advanced Features: Explore memory retention, code interpretation, and chain-of-thought reasoning.

Why Watch?

Amazon Bedrock Agents streamline workflows, reduce costs, and enhance user experiences by automating repetitive tasks. This video will provide you with practical insights and tips to get started quickly and effectively

Welcome to our comprehensive guide on building a cybersecurity-focused face recognition web application! In this video, we’ll take you through the entire process of creating a robust and secure face recognition system using Claude 3 and Amazon Rekognition.

What You’ll Learn:

1. Setting Up AWS Services: Learn how to configure essential AWS services like Security Groups, Application Load Balancer, and Route 53 for a secure and scalable architecture.

2. Development Environment: Get hands-on with AWS Cloud9 to set up your development environment.

3. Containerization: Understand how to create and manage Docker containers using Amazon ECS Cluster and Task Definition.

4. Image Storage and Management: Discover how to use Amazon ECR for storing and managing Docker images.

5. Face Recognition Integration: Integrate Claude 3 with Amazon Rekognition to build a powerful face recognition system.

6. Security First: We emphasize best practices in cybersecurity to ensure your application is secure from the ground up. 

7. End-to-End Architecture: Follow along as we build the entire architecture from scratch, ensuring you have a clear understanding of each component and how they work together.

Who Is This For?

Whether you’re a developer, a cybersecurity enthusiast, or someone looking to enhance their AWS skills, this video has something for you!

In this video, we’ll guide you through the entire process of creating a sophisticated chatbot application leveraging various AWS services. Whether you’re a beginner or an experienced developer, this tutorial has something for everyone!

What You’ll Learn:

1. Setting Up Your Development Environment: Using AWS Cloud9 for seamless coding and collaboration.

2. Containerizing Your Application: Creating Docker images and storing them in Amazon ECR.

3. Deploying with ECS: Setting up an ECS cluster and defining tasks for scalable deployment.

4. Domain Management: Configuring Route 53 for custom domain names.

5. Load Balancing: Implementing an Application Load Balancer for efficient traffic distribution.

Why Watch?

1. Hands-On Demonstration: Follow along with practical examples and real-time coding.

2. Expert Tips: Learn best practices and tips for optimizing your chatbot’s performance.

3. Comprehensive Coverage: From setup to deployment, we cover all the essential steps.

Who Is This For?

1. Developers: Looking to enhance their skills with AWS services.

2. Tech Enthusiasts: Interested in the latest advancements in AI and cloud computing.

3. Business Owners: Wanting to integrate intelligent chatbots into their customer service strategy.

In this video, we’ll guide you through the process of creating a powerful Image Understanding Application using Amazon Bedrock and Amazon ECS. We’ll also show you how to seamlessly integrate it with an Application Load Balancer (ALB) and set up DNS using Route 53.

What You’ll Learn:

1. Introduction to Amazon Bedrock: Understand the basics and benefits of using Amazon Bedrock for image processing.

2. Setting Up Amazon ECS: Step-by-step instructions to deploy your application using Amazon ECS.

3. Integrating ALB: Learn how to attach an Application Load Balancer to your ECS service for better traffic management.

4. Configuring Route 53: Set up DNS to ensure your application is easily accessible.

Why Watch?

1. Hands-On Demonstration: Follow along with a practical, hands-on demonstration.

2. Expert Tips: Gain insights and tips from industry experts.

Who Is This For?

1. Developers: Looking to enhance their skills with AWS services.

2. Cloud Engineers: Interested in deploying scalable applications.

3. Tech Enthusiasts: Keen on exploring the latest in cloud technology.

Load Balancer (ALB) and DNS management using Route 53 to ensure high availability, scalability, and robust performance.

What You’ll Learn:

1. Introduction to Amazon Bedrock: Understand the capabilities of Amazon Bedrock and how it enhances knowledge base search functionalities.

2. Amazon ECS Integration: See how Amazon ECS simplifies container management and deployment.

3. ALB Configuration: Learn about setting up an Application Load Balancer for efficient traffic distribution.

4. Route 53 DNS Management: Discover the benefits of using Route 53 for reliable DNS routing.

5. Live Demo: Watch a live demonstration of the knowledge base search tool in action, showcasing real-time query handling and response generation.

Why Watch?

1. Gain insights into modern cloud architecture and best practices.

2. Learn how to leverage AWS services to build scalable and efficient applications.

3. Get inspired to implement similar solutions in your own projects.

Welcome to this hands-on tutorial on mastering AWS S3 and Amazon Transcribe! In this comprehensive session, you'll learn how to upload .wav files to an AWS S3 bucket and create transcription jobs using Amazon Transcribe to convert audio recordings into text format.

What You'll Learn:

• Uploading .wav Files to S3: Step-by-step guide on how to upload your .wav files to an AWS S3 bucket.

• Creating Transcription Jobs: Learn how to set up and initiate transcription jobs in Amazon Transcribe.

• Interpreting Audio to Text: Discover how Amazon Transcribe converts your audio recordings into accurate text format.

• Managing Transcriptions: Explore best practices for managing and retrieving your transcriptions from AWS.

Welcome to this hands-on tutorial on mastering text analysis with Amazon Comprehend! In this comprehensive session, you'll learn how to leverage Amazon Comprehend to extract valuable insights from text, including entities, key phrases, Personally Identifiable Information (PII), sentiment, and language.

What You'll Learn:

• Entity Recognition: Understand how to identify and categorize entities within your text data.

• Key Phrase Extraction: Learn how to extract key phrases to summarize and understand the main points of your text.

• PII Detection: Discover techniques for detecting and managing Personally Identifiable Information (PII) in your text data.

• Sentiment Analysis: Explore how to analyze the sentiment of your text to gauge positive, negative, or neutral tones.

• Language Detection: Gain skills in identifying the language of your text data for multilingual applications.

Welcome to our comprehensive tutorial on real-time translation using Amazon Translate! In this video, we'll guide you through the process of translating text from Spanish to English in real-time using the AWS console. Additionally, we'll demonstrate how to upload a Dutch text file and translate it to English using the Document tab.

What You'll Learn:

1. Real-Time Translation: Translate text from Spanish to English instantly using Amazon Translate.

2. Document Translation: Upload a Dutch text file from local computer and translate it to English using the Document tab in Amazon Translate.

3. AWS Console Navigation: Learn to navigate the AWS console to access Amazon Translate.

4. Step-by-Step Instructions: Follow detailed steps to perform translations and review the results.

Steps Covered:

1. Introduction to Amazon Translate: Overview of the service and its capabilities.

2. Setting Up the Environment: How to set up your AWS environment for using Amazon Translate.

3. Real-Time Translation: Enter Spanish text in the AWS console and translate it to English in real-time.

4. Uploading Dutch Text File: Steps to upload a Dutch text file suing Document Tab in Amazon Translate.

5. Document Translation: Use the Document tab in Amazon Translate to translate the uploaded Dutch text file to English.

6. Reviewing Results: How to interpret and utilize the translated text.

Welcome to our hands-on project tutorial on redacting PII data using Amazon Transcribe! In this video, we'll guide you through an end-to-end project designed for restaurant businesses that accept orders via phone calls. Learn how to use Amazon Transcribe to redact sensitive customer information such as names, phone numbers, and addresses from recorded audio files.

What You'll Learn:

1. Recording Orders: How to record customer orders via phone calls and save them as .wav files.

2. Uploading to S3: Steps to upload the recorded .wav files to an Amazon S3 bucket.

3. Creating Transcription Jobs: How to create transcription jobs using Amazon Transcribe to extract text from the audio files.

4. Redacting PII Data: Techniques to automatically redact personally identifiable information (PII) from the transcriptions.

5. Forwarding to Chef: How to forward the redacted transcription to the chef for preparing the dishes while ensuring customer privacy.

Steps Covered:

1. Introduction to Amazon Transcribe: Overview of the service and its capabilities.

2. Setting Up the Environment: How to set up your AWS environment for using Amazon Transcribe.

3. Recording and Uploading Audio Files: Record customer orders and upload the .wav files to an S3 bucket.

4. Creating Transcription Jobs: Create transcription jobs in Amazon Transcribe and select the uploaded audio files for transcription.

5. Redacting PII Data: Use Amazon Transcribe's PII redaction feature to automatically hide sensitive customer information.

6. Reviewing and Forwarding Transcriptions: Review the redacted transcriptions and forward them to the chef for order preparation.

Welcome to our hands-on project tutorial on building and creating an index for searching using Amazon Kendra! In this video, we'll guide you through an end-to-end project where you'll learn how to leverage Amazon Kendra to create a powerful search index.

What You'll Learn:

1. Introduction to Amazon Kendra: Understand the basics of Amazon Kendra and its capabilities.

2. Setting Up the Environment: How to set up your AWS environment for using Amazon Kendra.

3. Creating an Index: Step-by-step instructions on creating an index in Amazon Kendra.

4. Adding Data Sources: Learn how to add data sources, including uploading a .json file to an S3 bucket.

5. Searching Text: Perform searches using Amazon Kendra to find relevant information in the .json file stored in S3.

Steps Covered:

1. Introduction to Amazon Kendra: Overview of the service and its features.

2. Setting Up the Environment: How to set up your AWS environment for using Amazon Kendra.

3. Creating an Index: Detailed steps to create an index in Amazon Kendra.

4. Uploading .json File to S3: Steps to upload your .json file to an Amazon S3 bucket.

5. Adding Data Sources: How to add the S3 bucket as a data source in Amazon Kendra.

6. Performing Searches: Use Amazon Kendra to search the text within the .json file and retrieve relevant results.

7. Reviewing Results: How to interpret and utilize the search results provided by Amazon Kendra.

Welcome to our comprehensive hands-on project tutorial on Amazon OpenSearch and OpenSearch Dashboard! In this video, we'll guide you through an end-to-end project where you'll learn how to create and manage an OpenSearch Service Domain, and explore flight sample datasets using OpenSearch Dashboards.

What You'll Learn:

1. Introduction to Amazon OpenSearch: Understand the basics of Amazon OpenSearch and its capabilities.

2. Creating an OpenSearch Service Domain: Step-by-step instructions on setting up an OpenSearch Service Domain.

3. Uploading Sample Datasets: How to upload flight sample datasets to your OpenSearch domain.

4. Using OpenSearch Dashboards: Explore and visualize data using OpenSearch Dashboards.

5. Data Analysis: Perform data analysis and gain insights from the flight sample datasets.

Steps Covered:

1. Introduction to Amazon OpenSearch: Overview of the service and its features.

2. Setting Up the Environment: How to set up your AWS environment for using Amazon OpenSearch.

3. Creating an OpenSearch Service Domain: Detailed steps to create and configure an OpenSearch Service Domain.

4. Uploading Flight Sample Datasets: Steps to upload flight sample datasets to your OpenSearch domain.

5. Exploring OpenSearch Dashboards: How to use OpenSearch Dashboards to visualize and analyze data.

6. Performing Data Analysis: Techniques to analyze the flight sample datasets and extract valuable insights.

Welcome to our hands-on tutorial on Automatically Detecting PII Data in Amazon S3 Using Amazon Macie In this video, we'll guide you through the process of setting up Amazon Macie to identify and protect sensitive information in your S3 buckets using End-to-End Architecture Diagram Automation.

What You'll Learn:

1. Introduction to Amazon Macie: Understand the basics of Amazon Macie and its capabilities.

2. Uploading PII Files to S3: Steps to upload files containing PII data to an Amazon S3 bucket.

3. Configuring Amazon Macie: How to configure Amazon Macie to detect PII data and use Custom Data Identifiers with RegEx as per business requirements.

4. Integrating with Amazon EventBridge and SNS: Set up integrations to send email notifications for any PII data findings in S3 buckets.

Steps Covered:

1. Introduction to Amazon Macie: Overview of the service and its features.

2. Setting Up the Environment: How to set up your AWS environment for using Amazon Macie.

3. Uploading PII Files to S3: Steps to upload files containing PII data to an Amazon S3 bucket.

4. Configuring Amazon Macie: Detailed steps to configure Amazon Macie to detect PII data and create Custom Data Identifiers using RegEx.

5. Integrating with Amazon EventBridge: How to integrate Amazon Macie findings with Amazon EventBridge.

6. Setting Up SNS Notifications: Configure Amazon SNS to send email notifications for PII data findings.

7. Reviewing Results: How to interpret and utilize the findings provided by Amazon Macie.

In this video, we'll walk you through the process of defining PII masking policies using AWS Bedrock Guardrails. Learn how to use the "add new PII" button to choose PII types such as Name and Email, and how to implement custom RegEx patterns for comprehensive data protection.

What You'll Learn:

1. How to navigate AWS Bedrock Guardrails

2. Adding new PII types (Name, Email) for masking

3. Implementing custom RegEx patterns for PII detection

4. Ensuring data privacy and compliance with AWS Join us for this detailed, step-by-step tutorial and enhance your data protection strategies with AWS Bedrock Guardrails.