AWS Certified AI Practitioner Cheat Sheet

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In the AWS Certified AI Practitioner Exam, Amazon SageMaker and Amazon Bedrock are two of the most commonly tested services. These services are central to machine learning and generative AI solutions on AWS.

Understanding these services, their features, and how they apply to real-world use cases is critical for exam success. Below, we provide the key facts that students need to know about SageMaker and Bedrock, covering their essential capabilities, tools, and use cases to help you prepare for the exam efficiently.

Amazon SageMaker

Purpose: A comprehensive, fully managed service for building, training, and deploying machine learning models. It’s designed to simplify the entire ML lifecycle from data preparation to model monitoring.
Key facts:

SageMaker is central to AWS machine learning services and is used for training custom models, deploying pre-built models, and managing ML workloads.
Focuses on both supervised and unsupervised learning, including reinforcement learning.
Supports open-source ML frameworks like TensorFlow, PyTorch, and MXNet.

SageMaker Studio

Purpose: A web-based integrated development environment (IDE) for data scientists and ML engineers.
Key facts:

Provides a unified interface for all ML development tasks (data prep, training, debugging, deployment).
It allows you to access data, build models, and deploy them all from one environment.
Key for managing experiments, tracking artifacts, and comparing model versions.

SageMaker Data Wrangler

Purpose: Simplifies the data preparation process by providing tools to clean, transform, and visualize data.
Key facts:

Integrates directly with SageMaker Studio.
Offers more than 300 built-in transformations (e.g., missing data handling, feature engineering).
Supports multiple data sources like Amazon S3, Redshift, and RDS.

SageMaker Feature Store

Purpose: Centralized repository for storing and retrieving machine learning features that can be reused across multiple models.
Key facts:

Helps in maintaining consistency between training and inference.
Can serve real-time features for low-latency model inference and batch features for large-scale jobs.
Useful for sharing features across teams and use cases.

SageMaker Clarify

Purpose: Detects bias in data and models and provides explainability for model predictions.
Key facts:

Provides fairness metrics to ensure models are not discriminating based on sensitive attributes (e.g., race, gender).
Model explainability tools like SHAP (SHapley Additive exPlanations) are used to explain predictions at both the global and local level.
Useful for regulatory compliance and responsible AI practices.

SageMaker Model Monitor

Purpose: Automatically monitors deployed models for data drift and performance degradation.
Key facts:

Tracks key metrics such as accuracy, precision, and recall over time.
Automatically detects if the input data distribution has changed, triggering alerts when anomalies are found.
Enables retraining when necessary to maintain model performance in production.

SageMaker JumpStart

Purpose: Provides access to pre-built models and solutions to quickly start ML projects.
Key facts:

Includes popular model architectures for NLP, computer vision, and more, reducing the time needed to train from scratch.
Pre-built solutions are provided for common tasks like fraud detection, demand forecasting, and personalized recommendations.
JumpStart can also be used for fine-tuning pre-trained models to fit specific use cases.

SageMaker AutoPilot

Purpose: Automatically builds and trains machine learning models with minimal intervention.
Key facts:

AutoPilot explores different ML algorithms and tuning parameters to deliver the best model.
Provides transparency into the model-building process by creating notebooks showing all steps.
Supports both classification and regression tasks, choosing the right ML pipeline based on the input data.

SageMaker Pipelines

Purpose: ML pipeline service for automating and managing workflows.
Key facts:

Enables the creation of repeatable and automated workflows for ML model building, training, and deployment.
Integrated with SageMaker’s MLOps tools, such as Model Monitor and Clarify.
Supports versioning of models and steps in the pipeline to ensure reproducibility.

SageMaker Experiments

Purpose: Manages and organizes ML experiments to track and compare models.
Key facts:

Useful for keeping track of different model versions, input data, and parameters.
Can automatically capture and store metadata from ML experiments for future analysis.
Helps in identifying which model configurations yield the best performance.

SageMaker Debugger

Purpose: Provides real-time insights into model training to detect issues like overfitting and underfitting.
Key facts:

Automatically monitors training jobs and alerts you if anomalies occur during the training process.
Can profile system resources such as CPU and memory usage, which is essential for optimizing model training performance.
Debugger can save significant time by identifying poor model performance before the model is fully trained.

SageMaker Batch Transform

Purpose: Allows running large-scale batch inference on pre-trained models.
Key facts:

Designed for high-volume predictions without needing a live endpoint.
Particularly useful for processing large datasets that don’t require real-time predictions.
Input and output data are stored in Amazon S3, making it easy to integrate with data lakes.

SageMaker Real-Time Inference

Purpose: Deploys models to endpoints for low-latency, real-time predictions.
Key facts:

Suitable for use cases where live prediction is required, such as recommendation engines or fraud detection.
Can autoscale based on traffic to handle varying prediction loads.
Integrated with SageMaker Model Monitor to ensure consistent model performance.

SageMaker Asynchronous Inference

Purpose: Handles long-running inference requests that don’t need an immediate response.
Key facts:

Best for processing large inputs (e.g., high-resolution images, videos) that take a long time to analyze.
Enables non-blocking inference, so requests are queued, and results are stored in Amazon S3 when ready.
Useful for reducing endpoint costs, as you only pay for what you use.

SageMaker Ground Truth

Purpose: Enables the creation of highly accurate training datasets through human labeling and automatic labeling.
Key facts:

Supports multiple labeling workflows, including 2D/3D object detection, text classification, and semantic segmentation.
Can reduce labeling costs by using active learning to auto-label simple tasks and reserve human labeling for complex tasks.
Integrated with Amazon Mechanical Turk and private workforce options.

Amazon Bedrock

Purpose: A fully managed service that allows developers to build and scale generative AI applications using foundation models (FMs) without managing underlying infrastructure.
Key facts:

Supports various pre-trained Foundation Models (FMs) for generative AI tasks such as text, images, and chatbots.
Removes the need to build models from scratch by offering access to multiple pre-built FMs (e.g., GPT-based and BERT-based models).
Bedrock integrates with other AWS services (e.g., S3, SageMaker) for storing data and deploying AI solutions.

Foundation Models (FMs)

Purpose: Pre-trained models designed for tasks like text generation, summarization, and image creation.
Key facts:

Foundation models in Bedrock include large language models (LLMs) like GPT and BERT, which are pre-trained on massive datasets.
Bedrock allows fine-tuning of these FMs to fit specific business needs without starting from scratch.
Supports text generation, image creation, and code generation tasks, helping with rapid development and deployment.

Retrieval Augmented Generation (RAG)

Purpose: Enhances generative AI outputs by augmenting model responses with relevant real-time data.
Key facts:

RAG combines pre-trained model outputs with external data sources or knowledge bases to provide more accurate and contextually aware responses.
Common use case: Improving chatbot answers by pulling real-time information (e.g., product databases or knowledge repositories) when generating responses.
Helps in reducing “hallucination” (generating plausible but incorrect information) by grounding responses in real-world data.

Model Customization via Fine-Tuning

Purpose: Allows businesses to adapt foundation models to specific domains and use cases.
Key facts:

Fine-tuning is done by providing domain-specific data to adjust model weights, making the model more relevant for tasks like customer service, healthcare, or finance.
Bedrock supports this fine-tuning without needing expertise in ML model architecture.
Fine-tuned models can provide better performance on niche tasks compared to general-purpose FMs.

Prompt Engineering

Purpose: The practice of crafting input prompts to get desired outputs from generative models.
Key facts:

Important for improving model response quality by adjusting input prompts with context or examples.
Techniques include zero-shot (no examples), few-shot (few examples), and chain-of-thought (explaining reasoning in the prompt).
Prompt engineering is essential for maximizing model accuracy and relevance, especially in customer-facing applications.

Agents for Amazon Bedrock

Purpose: Automates complex workflows by chaining together multiple tasks or models.
Key facts:

Agents can manage multi-step processes, such as data retrieval followed by generative model output, without manual intervention.
Often used for automating customer service workflows where multiple actions (e.g., data lookups, response generation) are required.
Supports custom configurations for orchestrating tasks across different foundation models and external systems.

Cost Structure

Purpose: Provides a pay-as-you-go model based on model usage.
Key facts:

Pricing is typically based on the number of tokens consumed during model usage, making it flexible for varying workloads.
Additional costs may apply for fine-tuning or using specific high-demand foundation models.
Provisioned Throughput is an option for businesses requiring guaranteed high-availability performance.

Security and Governance

Purpose: Ensures safe and compliant use of foundation models and generative AI solutions.
Key facts:

Bedrock integrates with AWS services like IAM (Identity and Access Management) to control access to models and data.
Supports encryption for data at rest and in transit, ensuring that sensitive information remains secure.
Users can monitor and audit model usage with AWS CloudTrail and CloudWatch to meet governance requirements.

Model Evaluation and Monitoring

Purpose: Enables businesses to track the performance and accuracy of deployed foundation models.
Key facts:

Bedrock supports human evaluation of generative model outputs to assess quality (e.g., accuracy, coherence).
Common evaluation metrics include ROUGE (for text summarization), BLEU (for language translation), and BERTScore (for sentence similarity).
Continuous evaluation helps ensure that models perform well in changing environments and maintain alignment with business goals.

AWS Integration

Purpose: Seamlessly integrates with other AWS services to create end-to-end AI workflows.
Key facts:

Easily integrates with Amazon S3 for storing data and model outputs, Amazon SageMaker for additional model training, and Amazon OpenSearch for embedding vectors and performing vector searches.
Bedrock models can be deployed through Amazon SageMaker endpoints for real-time inference or batch processing.
Supports VPC integration, allowing models to be deployed in isolated networks to meet compliance and security standards.

Multi-Modal Models

Purpose: Models that handle multiple data types such as text, images, and audio.
Key facts:

Bedrock supports multi-modal models capable of processing various types of input, such as generating text from images or answering questions based on visual data.
Multi-modal capabilities allow for a broader range of use cases, such as content creation, enhanced search, and recommendation systems.
These models are key for AI applications that involve multiple data sources (e.g., generating captions for images or visual content summarization).

Frequently Asked Questions

What is the AWS Certified AI Practitioner certification, and who is it for?

The AWS Certified AI Practitioner certification is designed for individuals seeking foundational knowledge of artificial intelligence (AI) and machine learning (ML) within the AWS ecosystem. It’s ideal for professionals in non-technical roles who collaborate with data scientists or engineers and want to understand how AI/ML solutions are implemented on AWS.

How does AWS SageMaker relate to the AI Practitioner certification?

AWS SageMaker is a key service covered in the AI Practitioner certification, offering tools to build, train, and deploy machine learning models. While this certification focuses on conceptual knowledge, understanding SageMaker’s capabilities can provide practical insights into AWS’s ML offerings.

What topics are covered in the AWS Certified AI Practitioner exam?

The exam tests your understanding of AI/ML concepts, AWS AI services (e.g., SageMaker, Rekognition, Polly), and how to identify appropriate AI/ML solutions for different scenarios.

How can I prepare for the AWS Certified AI Practitioner exam?

To prepare, you can utilize resources like cheat sheets, video tutorials, practice exams, and hands-on labs. Digital Cloud Training offers a complete set of preparation tools to help you succeed.

What are the benefits of obtaining the AWS Certified AI Practitioner certification?

Earning this certification demonstrates foundational AI/ML knowledge and showcases your ability to identify AWS services for AI solutions. It can boost your credibility in roles requiring collaboration with technical teams or cloud-based AI/ML projects.