This blog post is your gateway to unlocking a rewarding career as an AI ML Engineer & cracking the AWS AI Practitioner (AIF-C01) Certification exam. Dive into our engaging Step-By-Step Activity Guides, meticulously designed not just to enhance your CV but to land you that dream job. Gain the AI and machine learning skills employers crave, create an impressive CV, and set yourself up for success in job interviews. Plus, these resources are tailored to help you confidently conquer the AWS Certified AI Practitioner (AIF-C01) exam. Your journey to a fulfilling job in the dynamic world of AI and machine learning starts here!
List of Labs that we include in Our AWS Certified AI Practitioner Program:
- Lab 1: Create an AWS Free Trial Account
- Lab 2: CloudWatch – Create Billing Alarm & Service Limits
1.2 Amazon Bedrock and Generative AI
- Lab 1: How To Request Access to Bedrock Foundation Models on AWS Account
- Lab 2: Building a RAG-Enhanced Knowledge Management System With Amazon Bedrock
- Lab 3: Setting Up and Managing Guardrails with Amazon Bedrock Foundation Models
- Lab 4: Generating Images in Bedrock with Amazon Titan Image Generator G1
- Lab 5: Watermark Detection with Amazon Bedrock
- Lab 1: Crafting Prompts and Summarizing Text in Bedrock with Amazon Titan & Anthropic Claude in Bedrock
- Lab 2: Extracting Insights from Call Center Transcripts with Amazon Titan & Anthropic Claude in Bedrock
- Lab 1: Create, Deploy & Manage Amazon Q Business and Amazon Q Apps
- Lab 1: Exploring AWS AI Services with Amazon Comprehend, Translate, Transcribe, and Textract
- Lab 2: Enhancing Clinical Documentation with Amazon Comprehend Medical & Transcribe Medical
- Lab 1: Requesting AWS Service Quota Increases
- Lab 2: Setting Up Jupyter Notebook Environment
- Lab 3: Create SageMaker Studio & Deploy Model
- Lab 4: Text & Vector Embedding with Amazon Titan
- Lab 5: Invoke Zero-Shot Prompt for Text Generation
- Lab 6: AI Stylist Creating Personalized Outfit
- Lab 7: Automating Python Code Generation
1.7 AWS Security Services for AI Solutions
- Lab 1: Working with AWS IAM
- Lab 2: KMS Create and Use
- Lab 3: Enable CloudTrail and Store Logs In S3
- Lab 4: AWS Secrets Manager
1.1 AWS Basic Labs ^
Lab 1: Create an AWS Free Trial Account
Embark on your AWS journey by setting up a free trial account. This hands-on lab guides you through the initial steps of creating an AWS account, giving you access to a plethora of cloud services to experiment and build with.
Amazon Web Services (AWS) is providing a free trial account for 12 months to new subscribers to get hands-on experience with all the services that AWS provides. Amazon is giving us a number of different services that we can use, with some limitations, to get hands-on practice and gain more knowledge on AWS Cloud services as well as regular business use.
With the AWS Free Tier account, all the services offered have limited usage limits on what we can use without being charged. Here, we will look at how to register for an AWS FREE Tier Account.
To learn how to create a free AWS account, check our Step-by-step blog, How To Create AWS Free Tier Account
Lab 2: CloudWatch – Create Billing Alarm & Service Limits
Dive into CloudWatch, AWS’s monitoring service. This lab focuses on setting up billing alarms to manage costs effectively and keeping an eye on service limits to ensure your applications run smoothly within defined boundaries.
AWS billing notifications can be enabled using Amazon CloudWatch. CloudWatch is an Amazon Web Services service that monitors all of your AWS account activity. CloudWatch, in addition to billing notifications, provides infrastructure for monitoring apps, logs, metrics collection, and other service metadata, as well as detecting activity in your AWS account usage.
AWS CloudWatch offers a number of metrics through which you can set your alarms. For example, you may set an alarm to warn you when a running instance’s CPU or memory utilisation exceeds 90% or when the invoice amount exceeds $100. We get 10 alarms and 1,000 email notifications each month with an AWS free tier account.
To learn About CloudWatch, check our Step-by-step blog, CloudWatch vs. CloudTrail: Comparison, Working & Benefits
1.2 Amazon Bedrock and Generative AI ^
Lab 1: How To Request Access to Bedrock Foundation Models on AWS Account
Objective: Learn how to enable and manage access to Foundation Models in Amazon Bedrock.
This lab guides you through accessing and managing Foundation Models within Amazon Bedrock on your AWS account. You’ll navigate the Bedrock console, request access to models from leading AI providers, and ensure that all required permissions are correctly configured.
By the end of this lab, you’ll have successfully enabled and managed access to Foundation Models on Amazon Bedrock, setting the stage for deploying generative AI applications.
To learn About Amazon Bedrock, check our blog, Amazon Bedrock Explained: A Comprehensive Guide to Generative AI
Lab 2: Building a RAG-Enhanced Knowledge Management System With Amazon Bedrock Using Knowledge Bases
Objective: Learn how to build and optimize a Knowledge Management System using Amazon Bedrock Knowledge Bases and Retrieval-Augmented Generation (RAG).
This lab provides hands-on experience with Amazon Bedrock Knowledge Bases and the integration of Retrieval-Augmented Generation (RAG) to enhance your knowledge management system. You’ll learn to build a robust knowledge base, integrate it with Amazon S3, and configure advanced embedding models for efficient information retrieval.
By the end of this lab, you’ll have created a scalable knowledge repository and enhanced it with RAG, improving the relevance and accuracy of responses.
To learn About Rag, check our blog, Understanding RAG with LangChain
Lab 3: Setting Up and Managing Guardrails with Amazon Bedrock Foundation Models ^
Objective: Learn how to enable and manage access to Foundation Models in Amazon Bedrock.
This lab guides you through accessing and managing Foundation Models within Amazon Bedrock on your AWS account. You’ll navigate the Bedrock console, request access to models from leading AI providers, and ensure that all required permissions are correctly configured.
By the end of this lab, you’ll have successfully enabled and managed access to Foundation Models on Amazon Bedrock, setting the stage for deploying generative AI applications.
To learn About Amazon Bedrock, check our blog, Amazon Bedrock Explained: A Comprehensive Guide to Generative AI
Lab 4: Generating Images in Bedrock with Amazon Titan Image Generator G1
Objective: Learn how to navigate the Image Playground in Amazon Bedrock, select a model, and generate images based on custom prompts.
This lab guides you through using Amazon Bedrock to access the Image Playground, where you’ll select a model and generate images using custom prompts. You’ll explore the range of foundation models available in Bedrock, focusing on running inference to create images for generative AI applications.
By the end of this lab, you’ll have successfully generated images using Amazon Titan Image Generator G1, gaining practical experience in applying generative AI techniques.
To learn About Amazon Titan, check our blog, Amazon Titan: A Foundation Model by AWS
Lab 5: Watermark Detection with Amazon Bedrock
Objective: Learn how to detect watermarks embedded in media files, specifically in images generated by the Titan Image Generator G1 model in Amazon Bedrock.
This lab guides you through the process of using Amazon Bedrock to detect watermarks in images created by the Titan Image Generator G1 model. You’ll explore watermarking as a critical technique for content authentication and rights management.
By the end of this lab, you’ll be able to detect watermarks in images generated by the Titan Image Generator G1 and understand how to integrate this functionality into your applications. Note that this capability is designed exclusively for images produced by the Titan Image Generator G1 and does not apply to images from other sources or models.
To learn About Amazon Bedrock, check our blog, Amazon Bedrock Explained: A Comprehensive Guide to Generative AI
1.3 Prompt Engineering ^
Lab 1: Crafting Prompts and Summarizing Text in Bedrock with Amazon Titan Text G1 Express & Anthropic Claude ^
Objective: Learn how to use Amazon Titan Text G1 Express and Anthropic Claude V2 to craft effective prompts and summarize text.
This lab guides you through utilizing Amazon Titan Text G1 Express and Anthropic Claude V2 within Amazon Bedrock to perform high-quality text generation and summarization tasks. You’ll explore how these advanced language models can enhance your ability to create precise prompts and generate concise summaries.
By the end of this lab, you’ll have developed the skills to effectively leverage these models for various text generation and summarization applications.
To learn About Prompt Engineering, check our blog, What is Prompt Engineering?
Lab 2: Extracting Insights from Call Center Transcripts using Amazon Titan Text G1 Express and Anthropic Claude V2 in Bedrock
Objective: Learn how to use advanced language models to extract insights and perform sentiment analysis on customer-agent call centre transcripts.
This lab guides you through utilizing Amazon Titan Text G1 Express and Anthropic Claude V2 within Amazon Bedrock to analyze customer-agent call centre transcripts. You’ll explore how these powerful models can extract valuable insights and perform sentiment analysis to enhance customer support operations.
By the end of this lab, you’ll have gained hands-on experience with these models and understand how to apply them to real-world scenarios, improving your ability to support and optimize customer interactions.
To learn About Amazon Titan, check our blog, Amazon Titan: A Foundation Model by AWS
1.4 Amazon Q ^
Lab 1: Create, Deploy & Manage Amazon Q Business and Amazon Q Apps
Objective: Gain hands-on experience with Amazon Q Business and Amazon Q Apps by creating, managing, and querying a knowledge base to optimize knowledge management.
This lab guides you through the process of integrating data sources like Amazon S3, configuring advanced retrieval models, and setting up secure user roles using the IAM Identity Center. You’ll also explore Amazon Q Apps to automate tasks and generate actionable insights, improving both internal operations and customer support.
By the end of this lab, you’ll have a comprehensive understanding of how to leverage Amazon Q Business and Amazon Q Apps to enhance knowledge management and operational efficiency.
To learn About Amazon Q, check our blog, Amazon Q: Boosting Productivity with Advanced AI Assistance
1.5 AWS Managed AI Services ^
Lab 1: Exploring AWS AI Services with Amazon Comprehend, Translate, Transcribe, and Textract
Objective: Gain hands-on experience with AWS AI services, focusing on Amazon Comprehend, Translate, Transcribe, and Textract.
This lab guides you through analyzing text for insights, translating text between languages, transcribing audio to text, and extracting structured data from documents. You’ll start by setting up the necessary AWS environment and creating an S3 bucket for data storage. Then, you’ll explore each service, performing tasks such as text sentiment analysis, text translation, audio transcription, and data extraction from scanned documents.
By the end of this lab, you’ll have a comprehensive understanding of how to integrate these AI capabilities into your data workflows, enhancing automation and efficiency in your projects.
To learn About AWS Managed AI Service, check our blog, AWS AI, ML, and Generative AI Services and Tools
Lab 2: Enhancing Clinical Documentation with Amazon Comprehend Medical & Transcribe Medical
Objective: Gain hands-on experience with Amazon Comprehend Medical and Amazon Transcribe Medical for analyzing clinical text and transcribing medical audio.
This lab guides you through using Amazon Comprehend Medical to analyze clinical text for insights and Amazon Transcribe Medical to transcribe medical conversations into text. You’ll start by setting up the necessary AWS environment and creating S3 buckets for data storage. Then, you’ll explore each service to perform tasks such as clinical text analysis and medical transcription.
By the end of this lab, you’ll have a comprehensive understanding of how to integrate these AI capabilities into your data workflows, enhancing automation and efficiency in your clinical projects.
To learn About Amazon Comprehend, check our blog, What is AWS Comprehend: Natural Language Processing in AWS
1.6 AWS Sagemaker ^
Lab 1: Requesting AWS Service Quota Increases for EC2 Instance
Objective: Gain hands-on experience in requesting a quota increase for the ml.g5.2xlarge instance type within Amazon SageMaker using the AWS Management Console.
This lab guides you through the process of requesting a service quota increase for the ml.g5.2xlarge instance type. Managing service quotas effectively is crucial for ensuring that your cloud infrastructure can scale to meet the needs of your applications and clients.
By the end of this lab, you’ll understand how to navigate the AWS Management Console to manage and request quota increases, ensuring that your infrastructure can support growing workloads.
To learn About AWS Sagemaker, check our blog, Amazon SageMaker AI For Machine Learning: Overview & Capabilities
Lab 2: Setting Up Jupyter Notebook Environment in SageMaker Studio
Objective: Set up the Jupyter Notebook environment in SageMaker Studio for model development.
Overview:
This lab will guide you through setting up a Jupyter Notebook environment within SageMaker Studio. You’ll learn how to configure the environment, import necessary libraries, and prepare for building and testing machine learning models. By the end of this lab, you’ll be equipped to use Jupyter Notebooks for data exploration, model training, and evaluation.
To learn About AWS Sagemaker, check our blog, Amazon SageMaker AI For Machine Learning: Overview & Capabilities
Lab 3: Create & Manage SageMaker Studio: Deploy & Test SageMaker JumpStart Foundation Models
Objective: Learn how to create and manage SageMaker Studio, utilize the SageMaker JumpStart Foundation Model Hub, and deploy and test pre-built machine learning models.
This lab provides practical experience in setting up a SageMaker Studio environment, deploying a model from SageMaker JumpStart, and performing inference tests to validate the model’s performance. You’ll explore the process of creating and managing your SageMaker Studio, ensuring that you can effectively deploy and test machine learning models.
By the end of this lab, you’ll have a solid understanding of how to utilize SageMaker Studio and SageMaker JumpStart to deploy and validate machine learning models.
To learn About AWS Sagemaker, check our blog, Amazon SageMaker AI For Machine Learning: Overview & Capabilities
Lab 4: Text & Vector Embedding with Amazon Titan
Objective: Learn how to generate text embeddings and perform similarity testing using Amazon Titan within SageMaker Studio.
In this lab, you’ll set up your environment in AWS, launch SageMaker Studio, and create a Jupyter lab space. After making the necessary settings adjustments, you’ll use Amazon Titan to generate text embeddings and perform similarity testing. This hands-on experience will help you build and utilize text embeddings to support sophisticated NLP applications.
By the end of this lab, you’ll have the skills to create and apply text embeddings in natural language processing tasks.
To learn About Amazon Titan, check our blog, Amazon Titan: A Foundation Model by AWS
Lab 5: Invoke Zero-Shot Prompt for Text Generation in Bedrock using SageMaker Studio
Objective: Learn how to use Amazon Bedrock’s Titan model to generate text based on zero-shot prompts, specifically for crafting email responses to negative customer feedback.
In this lab, you’ll set up your AWS environment, launch SageMaker Studio, and create a JupyterLab workspace. You’ll then utilize the Titan model in Amazon Bedrock to generate text using zero-shot prompts, focusing on crafting effective email responses to negative customer feedback. This hands-on experience will equip you with the skills to leverage large language models (LLMs) for various text-generation tasks in real-world applications.
By the end of this lab, you’ll be proficient in using zero-shot prompts for text generation, enabling you to apply LLMs to a range of text-based tasks.
To learn About Amazon Bedrock, check our blog, Amazon Bedrock Explained: A Comprehensive Guide to Generative AI
Lab 6: AI Stylist: Creating Personalized Outfit Recommendations with Amazon Bedrock, Sagemaker and Stable Diffusion
Objective: Learn how to use Amazon Bedrock’s Titan model to generate text-based prompts for personalized outfit recommendations and create visual representations using Stable Diffusion.
In this lab, you’ll set up your AWS environment, launch SageMaker Studio, and create a JupyterLab workspace. You’ll then use the Titan model in Amazon Bedrock to generate prompts for personalized outfit recommendations. These prompts will be paired with Stable Diffusion to create visual representations of the suggested outfits. This lab equips you with the skills to leverage large language models (LLMs) and image generation tools to create AI-driven solutions for real-world applications.
By the end of this lab, you’ll have the ability to integrate LLMs and image generation tools to develop innovative AI solutions.
To learn About Amazon Bedrock, check our blog, Amazon Bedrock Explained: A Comprehensive Guide to Generative AI
Lab 7: Automating Python Code Generation with Amazon Bedrock’s Claude Model
Objective:
Learn how to automate Python code generation using Amazon Bedrock’s Claude model within SageMaker Studio.
In this lab, you’ll set up your environment in AWS, configure Boto3, and launch SageMaker Studio to create a Jupyter lab space. After making the necessary settings adjustments, you’ll use Amazon Bedrock’s Claude model to generate Python scripts for analyzing sales data from a CSV file. This hands-on experience will help you understand and utilize AI-driven code generation to streamline development processes and enhance efficiency.
By the end of this lab, you’ll have the skills to automate Python code generation and apply AI in data analysis tasks.
To learn About Amazon Bedrock, check our blog, Amazon Bedrock Explained: A Comprehensive Guide to Generative AI
1.7 AWS Security Services for AI Solutions ^
Lab 1: Working with AWS IAM
Objective: Learn how to create & use IAM users, groups, roles & policies.
In this lab, you will set up AWS IAM effectively, and start by configuring a robust password policy for IAM users, ensuring security. Regularly review and update permissions to align with evolving requirements and best practices.
To learn About Amazon IAM, check our blog, AWS Identity And Access Management (IAM)
Lab 2 : KMS: Create and Use
Learn how to create and manage encryption keys using AWS Key Management Service (KMS).
This lab guides you through setting up and using KMS to encrypt data and manage secure access to sensitive information.
By the end of this lab, you will be able to create, manage, and use encryption keys to secure data within AWS.
To learn About KMS, check our blog, AWS Key Management Service (AWS KMS) for Data Encryption
Lab 3: Enable CloudTrail and Store Logs In S3Lab
Objective: Enhance your AWS security by enabling AWS CloudTrail.
This lab guides you in setting up CloudTrail and storing logs in Amazon S3 for comprehensive audit trails and compliance.
To learn About CloudTrail, check our Step-by-step blog, CloudWatch vs. CloudTrail: Comparison, Working & Benefits
Lab 4: AWS Secrets Manager
Learn how to securely store and manage secrets using AWS Secrets Manager.
This lab guides you through storing and retrieving sensitive data like passwords and API keys securely.
By the end of this lab, you will be able to manage secrets securely and access them programmatically in your applications.
To learn About AWS Secrets Manager, check our blog, AWS Secrets Manager
Frequently Asked Questions ^
Q1) How do these hands-on labs align with the AWS Certified AI Practitioner (AIF-C01) exam objectives?
Ans: These labs are specifically designed to cover the practical skills needed for the AIF-C01 exam. Each lab focuses on essential tasks and AWS services like Amazon Bedrock, SageMaker, and Comprehend that are directly aligned with the exam’s objectives. By completing these labs, you’ll gain hands-on experience that reinforces the theoretical knowledge required to pass the certification.
Q2) What prior knowledge or skills do I need before attempting these labs?
Ans: While these labs are designed to help you prepare for the AWS Certified AI Practitioner certification, a basic understanding of AWS services, AI/ML concepts, and cloud computing fundamentals is recommended. The labs are structured to guide you through each process step-by-step, making them accessible even if you’re new to some of the services.
Q3) How much time should I allocate to complete all 15 labs?
Ans: Each lab is designed to be concise and focused, typically taking around 20-30 minutes to complete. Therefore, you should plan to allocate around 5-7.5 hours in total to work through all 15 labs, depending on your pace.
Q4) Can these labs be applied to real-world AI/ML projects outside of the certification?
Ans: Absolutely! The skills and knowledge gained from these labs are directly applicable to real-world AI/ML projects. Whether you’re setting up models in Amazon Bedrock, performing sentiment analysis with Amazon Comprehend, or deploying machine learning models using SageMaker, these labs provide practical experience that you can use in actual AI/ML implementations.
Q5) How does the hands-on experience from these labs compare to theoretical learning?
Ans: Hands-on experience is invaluable when preparing for certifications like the AWS Certified AI Practitioner. While theoretical learning provides the foundation, these labs allow you to apply that knowledge in a practical context, reinforcing your understanding and giving you the confidence to tackle real-world challenges. Practical experience is crucial for mastering AI/ML concepts and AWS tools, which is why these labs are a core part of the preparation process.
Related References
- Join Our Generative AI Whatsapp Community
- Introduction To Amazon SageMaker Built-in Algorithms
- Introduction to Generative AI and Its Mechanisms
- Mastering Generative Adversarial Networks (GANs)
- Generative AI (GenAI) vs Traditional AI vs Machine Learning (ML) vs Deep Learning (DL)
- AWS Certified AI Practitioner (AIF-C01) Certification Exam
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