AWS Artificial Intelligence and Data Analytics Services

Date: Aug 5, 2024

In this chapter, dive into two of today's hottest topics in the IT industry, artificial intelligence and machine learning (AI/ML) services and data analytics services in AWS.

This chapter covers the following subjects:

Artificial Intelligence/Machine Learning Services: Artificial intelligence (AI)/machine learning (ML) is one of the most popular areas of IT today. This section of the chapter details some of the most popular AI and ML services available today in AWS.
Data Analytics Services: Another hugely popular area of IT is data analytics. Sure enough, AWS provides many services in this area, and we’ll look at some of the most popular of them in this section.

In this chapter, we’ll delve into the exciting realm of artificial intelligence (AI)/machine learning (ML) services in AWS. In addition, we will explore some amazing data analytics services offered by AWS. This is an important chapter as it addresses some of the most popular and hyped up topics in IT today.

AWS, as you would expect, does a fantastic job of making what might be very complex technologies quite simple to implement. This chapter covers the AWS services available in these areas.

“Do I Know This Already?” Quiz

The “Do I Know This Already?” quiz allows you to assess whether you should read the entire chapter. Table 17-1 lists the major headings in this chapter and the “Do I Know This Already?” quiz questions covering the material in those sections so you can assess your knowledge of these specific areas. The answers to the “Do I Know This Already?” quiz questions appear in Appendix A, “Answers to the ‘Do I Know This Already?’ Quizzes and Q&A Sections.”

Table 17-1 “Do I Know This Already?” Foundation Topics Section-to-Question Mapping

Foundation Topics Section	Questions
Artificial Intelligence/Machine Learning Services	1–3
Data Analytics Services	4–6

1. You have been tasked with training a machine-learning model for a project in your organization. What AWS service can assist you with this?

Kendra
Athena
SageMaker
Lex

2. What AWS service helps you offer intelligent natural language searching in your solutions?

Lex
Kendra
SageMaker
QuickSight

3. You are interested in adding AI to your customer service chat. What AWS service should you investigate?

Kendra
SageMaker
Athena
Lex

4. What AWS service permits SQL queries against data stored in S3 buckets?

Athena
QuickSight
Kinesis
Glue

5. What is an option for ETL data services in AWS?

Glue
Athena
SageMaker
Neptune

6. What service of AWS can assist you in creating powerful data visualizations such as charts and graphs?

Athena
QuickSight
Glue
Kinesis

Artificial Intelligence/Machine Learning Services

I don’t want to take anything for granted in this section, so let’s begin by defining AI and ML. Artificial intelligence (AI) refers to computer systems or machines that are designed to perform tasks that typically require human intelligence, such as learning, reasoning, problem-solving, and decision-making. A subset of this exciting discipline is machine learning (ML), which involves the algorithms and models that enable computers to learn patterns from data and make predictions or decisions without explicit programming.

AI and ML are lofty disciplines that typically require the latest and greatest technologies and lots of available resources (like CPU, memory, and storage). AWS is perfectly positioned to help companies take advantage of these cutting-edge technologies.

SageMaker

AWS SageMaker is a smart assistant that you can use to build and train machine learning models without needing to be a coding expert. It provides easy-to-use tools to help you gather and prepare data, pick the right algorithm, and then train and deploy your model, all in one convenient place on the AWS Cloud platform. Figure 17-1 shows AWS SageMaker in the AWS Management Console.

Figure 17.1 AWS SageMaker

AWS SageMaker offers several features that simplify the ML lifecycle. Here are just some of them:

Built-in algorithms: SageMaker comes with a variety of prebuilt algorithms for common ML tasks—such as classification, regression, and clustering— which means you don’t need to create models from scratch.
Notebook instances: SageMaker provides Jupyter notebook instances, which allow you to create and share documents that contain live code, equations, visualizations, and narrative text.
Training jobs: You can use SageMaker to easily train your ML models at scale, distributing the training process across multiple instances.
Model hosting: Once your model is trained, SageMaker makes it simple to deploy, host, and integrate it with your applications.
Managed endpoints: SageMaker provides managed endpoints for deploying models, making it easy to handle real-time predictions and batch processing.
Autopilot: SageMaker enables you to automate the end-to-end process of building, training, and deploying ML models with minimal effort, making it suitable for users with limited ML expertise.

Lex

AWS Lex makes it easy to create virtual assistants for your applications. It’s a service that helps you build chatbots and conversational interfaces using natural language understanding. Think of it as the brain behind a chatbot. Lex understands user inputs, extracts key information, and can respond in a way that makes sense.

This service is handy for creating interactive experiences in your applications, whether for answering customer queries, handling reservations, or guiding users through processes. You can integrate Lex into various platforms, such as mobile apps or websites, to make it easier for users to interact with your applications using just their words. Plus, Lex is powered by the same technology as Amazon Alexa, so it’s got some serious language smarts under the hood.

Kendra

AWS Kendra is a super-smart search engine that is designed to help you find information effortlessly. It’s a powerful search service that uses ML to understand the context and meaning behind your queries. Instead of just matching keywords, Kendra comprehends natural language, making it feel like you’re having a conversation with your search engine. It’s great for handling complex searches across vast amounts of data in documents, FAQs, or other sources. Figure 17-2 shows the Kendra service in the AWS Management Console.

Figure 17.2 AWS Kendra

AWS Kendra includes the following features:

Semantic search: Kendra uses machine learning algorithms to understand the semantics of the content and improve the accuracy of search results by recognizing nuances of and relationships between words.
Relevance tuning: Kendra allows you to fine-tune search results to prioritize certain documents or sources based on your preferences. It enables you to ensure that the most important information is surfaced first.
Rich document support: Kendra can handle a variety of document types, including PDFs, Word documents, HTML, and more, making it versatile for different types of content.
Query suggestions: Kendra provides query suggestions to guide users and help them refine their search queries for better results.
Natural language query enhancement: Kendra assists users in constructing more effective queries by suggesting natural language improvements, making the search process more intuitive.

Data Analytics Services

AWS offers a suite of data analytics services that can help a small startup or a large enterprise make informed decisions by extracting meaningful patterns from data. AWS provides scalable and flexible solutions to analyze data efficiently. With services like AWS Athena, and AWS Glue, you can turn raw data into actionable intelligence. The AWS Cloud makes data analytics accessible, allowing you to focus on uncovering valuable insights without the hassle of managing complex infrastructure.

Athena

When I first tried the AWS Athena service, I thought it was pure magic. Athena is a serverless, interactive query service that makes it possible to analyze data residing in AWS S3 buckets using standard SQL expressions. With Athena, you don’t need complex data transformation or loading processes. You dump the data into S3, and you are ready to directly query the data in its raw, native format.

Athena uses Trino and Presto, which are open-source distributed SQL query engines that enable you to execute SQL queries across your data stored in S3. Athena supports various data formats, including Avro, Parquet, ORC, JSON, and CSV, ensuring compatibility with a wide range of data structures. In addition, Athena integrates with the AWS Glue Data Catalog (discussed next) to streamline the metadata management process and enhance query efficiency.

Glue

The AWS Glue service provides ETL (extract, transform, and load) services for your data analytics. An ETL system in data analytics is like an automated data organizer that collects information from different places, cleans it up, and arranges it neatly so that your analysts can easily make sense of it. It’s basically the behind-the-scenes work that ensures your data is ready and polished for analysis.

The Glue Data Catalog acts as a central repository for metadata about your data sources, transformations, and targets. AWS Glue crawlers automatically scan and catalog data in various formats across different storage systems, creating a searchable and organized metadata store.

The ETL process is handled by Glue Jobs, which allows you to define and execute Python or Scala code for data transformation. Glue provides a serverless execution environment and allows you to scale your ETL jobs based on demand without managing the underlying infrastructure.

Glue features tight integration with other AWS services and supports a variety of potential data sources and destinations, including S3, Redshift, and RDS.

QuickSight

AWS QuickSight is a business intelligence service that makes it easy to visualize and explore your data. It allows you to create interactive dashboards and reports, providing insights from various data sources with just a few clicks.

QuickSight is designed to be user friendly, enabling both technical and nontechnical users to derive meaningful insights from their data through intuitive and customizable visualizations.

AWS QuickSight offers several key features that make it a powerful and user-friendly business intelligence service:

Easy data integration: QuickSight seamlessly connects to various data sources, including AWS services, databases, and third-party applications, making it convenient to analyze data from different platforms.
Intuitive visualizations: QuickSight provides a wide range of customizable and interactive visualizations, such as charts, graphs, and maps, to allow users to represent data in ways that best communicate insights.
Insights: QuickSight’s Auto Insights feature uses machine learning to automatically discover hidden trends, patterns, and anomalies in data, saving users time in the analysis process.
Smart recommendations: QuickSight offers intelligent recommendations for the most suitable visualizations based on the type of data and the analysis performed, enhancing the user experience and aiding in data exploration.
SPICE: QuickSight uses the Super-fast, Parallel, In-memory Calculation Engine (SPICE), which provides high-performance data processing for quick and responsive analytics, even with large datasets.
Ad hoc analysis: Users can perform ad hoc analysis by dragging and dropping fields to create new visualizations on the fly, enabling quick exploration and understanding of data.
Dashboard storytelling: QuickSight supports the creation of interactive dashboards and stories that allow users to present and share insights in a narrative format and enhance the communication of data-driven stories.

Kinesis

AWS Kinesis is a fully managed platform designed for real-time processing of streaming data at scale. It enables an organization to ingest, process, and analyze large volumes of real-time data from diverse sources, such as Internet of Things (IoT) devices, applications, and logs. Figure 17-3 shows AWS Kinesis in the AWS Management Console.

Figure 17.3 AWS Kinesis

Kinesis offers a suite of services that cater to specific aspects of streaming data workflows:

Kinesis Data Streams: This service allows you to collect and process real-time data streams. It enables you to scale the number of streaming data shards based on the volume of data, ensuring efficient handling of varying workloads. With Data Streams, developers can build applications that rapidly respond to changing data and extract valuable insights in real time.
Kinesis Data Firehose: This service simplifies the process of loading streaming data into other AWS services or external destinations and eliminates the need for manual intervention. It automates data delivery, transformation, and compression, streamlining the data pipeline and reducing management overhead.
Kinesis Data Analytics: This service facilitates the real-time analysis of streaming data. It enables you to run SQL queries on streaming data, extract meaningful information, and derive insights on the fly. Kinesis Data Analytics enables an organization to gain actionable intelligence from its streaming data in order to make informed decisions and drive innovation.

As you can see, AWS Kinesis is a comprehensive and scalable solution for managing the entire lifecycle of your streaming data, from ingestion and processing to analysis and delivery.

Review All Key Topics

Review the most important topics in this chapter, noted with the Key Topics icon in the outer margin of the page. Table 17-2 lists these key topics and the page number on which each is found.

Table 17-2 Key Topics for Chapter 17

Key Topic Element	Description	Page Number
List	SageMaker features	214
Overview	Lex	215
Overview	Athena	217
Overview	Kinesis	219

Define Key Terms

Define the following key terms from this chapter and check your answers in the Glossary:

SageMaker

Lex

Kendra

Athena

Glue

QuickSight

Kinesis

Q&A

The answers to these questions appear in Appendix A. For more practice with exam format questions, use the Pearson Test Prep Software Online.

1. Name and briefly describe the AWS AI/ML service that is powered by the same technology as Amazon Alexa.

2. Name and briefly describe the AWS technology that aims to manage your data streaming lifecycle from ingestion to analysis.