Introduction to AWS Boto in Python
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Use boto3 in Python to store and share files on S3, send SNS text alerts, and analyze images and text with Rekognition and Comprehend.
Boto3 brings Amazon Web Services into a Python data workflow, so the work is no longer limited by what a single laptop can handle. You store files in S3, send text alerts through SNS when results call for them, and pass images and written feedback to Rekognition and Comprehend. The work ends with a pipeline that texts its subscribers whenever a photo contains a cat.
What You Will Achieve
- Create, list and delete S3 buckets with a boto3 client, then upload and retrieve files from them
- Control who can open an object by setting it public or private and sharing it through a presigned URL
- Set up SNS topics and subscriptions that send SMS and email alerts triggered by your analysis
- Detect objects in images with Rekognition and judge the sentiment and language of text with Comprehend
Program Overview
Cloud Storage and Secure Sharing with S3
Starts with boto3 clients and buckets, then moves to object permissions and a case study that publishes pandas reports as web pages.
- Boto3 clients and bucket management
- Uploading and retrieving files
- Public and private objects, presigned URLs
- Report repository case study with HTML tables from pandas
Notifications with SNS Topics and Subscriptions
Turns analysis results into messages by managing topics and subscriptions and routing alerts to the people who need them.
- Creating and deleting topics
- Subscriptions and single SMS messages
- Case study: multi-level alerts with different protocols per level
AWS Machine Learning Services for Images and Text
Applies Rekognition to images and Comprehend to text, including language detection and translation, closing with a scooter case study that combines storage, alerts and recognition.
- Cat detection and reading parking signs
- Language detection and translation of requests
- Sentiment of community feedback
- Scooter dispatch case study
Certificate Awarded (Paid)
Job Outlook
These skills apply in data work that runs on the cloud: keeping project files in S3, publishing reports to the web, alerting teams by text or email, and adding image and text analysis to Python pipelines.
Good for: Python users who want their data work to store files, send alerts and analyze images and text on AWS directly from code.
Keep in mind: Expect boto3 code throughout: the chapters work S3, SNS, Rekognition and Comprehend through short exercises and three case studies, with language translation added in the last chapter.
Bottom line: A practical way to call AWS from Python, built toward a cat-spotting pipeline that ties storage, alerts and image recognition together.
Frequently Asked Questions
What does the final cat pipeline do?
It brings the chapters together: Rekognition checks incoming images for cats, SNS manages the people subscribed to the alerts, and a text message goes out to them whenever a cat turns up in a picture.
How is a private file in S3 shared?
The second chapter compares making objects public with keeping them private, then shows how to generate a presigned URL and use it to open a private object stored in S3.
What do the case studies work on?
Three case studies close the later chapters: a report repository that combines February's daily requests into HTML pages on S3, a notification system with multi-level topics and a different protocol per level, and a scooter scenario covering community sentiment and dispatch.
How does the last chapter work with text?
Comprehend detects the language of a piece of text and the sentiment behind it, and Get It Done requests are translated and scored for sentiment. The scooter case study then applies the sentiment work to community feedback.
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