AWS AI/ML and Analytics Services
Domain 3: Cloud Technology and Services — Task 3.7: Identify AWS artificial intelligence, machine learning (AI/ML), and analytics services
This chapter introduces how AWS turns data into intelligence. AWS provides both machine learning platforms and ready-to-use AI services, along with powerful analytics tools for processing and visualizing data. The Cloud Practitioner exam tests whether you can match the right AWS service to a specific AI, ML, or analytics requirement.
Understanding AWS AI and Machine Learning Services
AWS offers a wide range of managed AI and ML services that remove the need to build and operate complex infrastructure. These services range from fully customizable platforms for data scientists to pre-trained services for common tasks such as speech, vision, and language processing.
Amazon SageMaker is the primary service for building, training, and deploying machine learning models at scale. For ready-to-use AI capabilities, AWS provides services such as Amazon Lex for conversational chatbots, Amazon Polly for converting text to speech, Amazon Rekognition for image and video analysis, Amazon Comprehend for natural language processing, Amazon Transcribe for speech-to-text, Amazon Translate for language translation, and Amazon Kendra for intelligent enterprise search.
Exam Tip:
Chatbots → Lex
Text-to-speech → Polly
Speech-to-text → Transcribe
Image and video analysis → Rekognition
Text and sentiment analysis → Comprehend
Document search → Kendra
Model building and training → SageMaker
Understanding AWS Analytics Services
AWS analytics services are used to collect, process, query, and visualize data at scale. These services work closely with storage services like Amazon S3 and databases to enable reporting, insights, and predictive analytics.
Amazon Athena lets you run SQL queries directly on data stored in S3 without managing servers. AWS Glue prepares and catalogs data through ETL pipelines. Amazon Kinesis ingests and processes real-time data streams. Amazon QuickSight provides dashboards and visual reports. Amazon Redshift is a fully managed data warehouse for large-scale analytics, and Amazon OpenSearch Service supports log analysis and search.
Exam Tip:
Ad-hoc SQL on S3 → Athena
Data transformation and ETL → Glue
Real-time data streams → Kinesis
Dashboards and reporting → QuickSight
Enterprise data warehouse → Redshift
AI/ML vs. Analytics Services
AI and ML services focus on adding intelligence to applications—understanding text, images, speech, and patterns. Analytics services focus on processing and querying data so that organizations can understand trends and make decisions.
In many real-world solutions, these two areas work together, with analytics preparing the data and AI/ML extracting insights from it.
Example: Combining AI and Analytics
A customer support system might capture phone calls through Amazon Connect. Amazon Transcribe converts those calls into text, and Amazon Comprehend analyzes the text for sentiment. The results are stored in Amazon S3. AWS Glue organizes the data, Amazon Athena queries it, and Amazon QuickSight displays customer sentiment trends on dashboards.
This workflow shows how AWS AI/ML and analytics services combine to create business insights.
Key Exam Reminders
Use Lex for chatbots, Polly for speech synthesis, Transcribe for speech-to-text, Rekognition for images and video, Comprehend for text analysis, Kendra for intelligent search, and SageMaker for building ML models. For analytics, Athena queries S3, Glue prepares data, Kinesis handles real-time streams, QuickSight creates dashboards, Redshift powers data warehouses, and OpenSearch analyzes logs and search data.
Exam questions often describe full pipelines such as S3 → Glue → Athena → QuickSight. Security and compliance are also important, so encryption, IAM roles, and logging should always be assumed.
Final Thoughts
AWS AI/ML and analytics services allow organizations to turn raw data into intelligent, automated, and insight-driven applications. On the exam, your goal is to identify which service fits a specific task—whether that task involves real-time streaming, data preparation, querying, visualization, or machine learning. Understanding how these services work together will help you confidently answer scenario-based questions and design real-world AWS solutions.
