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Salah Elhossiny for AWS Community Builders

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Top 5 AWS ML Sessions to Attend at AWS re:Invent 2021

For AWS ML newbies, spend your time learning and using Amazon SageMaker, and here are the best 5 AWS Re-invent talks to help you get started quickly this year!

1. ( AIM203 ) Turn your documents into insights with AWS machine learning services (session)

You will learn how organizations worldwide are leveraging intelligent document processing (IDP) to automate manual document-based activities, enabling them to process millions of documents in hours and automating low-value tasks to improve data management and customer satisfaction. In this session, hear from AWS customers about how they processed thousands of documents in seconds in order to audit the critical information locked inside.

2. ( AIM402 ) Develop your ML project with Amazon SageMaker (workshop)

Learn how to use Amazon SageMaker to create a whole machine learning project from start to finish in this workshop. With SageMaker Data Wrangler, get started with data exploration and analysis, data purification, and feature engineering. After that, save features in SageMaker Feature Store, extract features for training with SageMaker Processing, train a model with SageMaker training, and finally deploy it with SageMaker hosting. Learn to utilise SageMaker Studio as an IDE and SageMaker Pipelines to orchestrate the machine learning workflow.

3. ( AIM202 ) Get started with AWS computer vision services (session)

We can use computer vision to improve our ability to comprehend the visual world around us. This session will provide you an overview of AWS computer vision services and show you how these pre-trained and configurable machine learning (ML) capabilities can help you get up and running quickly, even if you don't have any ML experience. Learn how to install these models on your preferred device and execute inferences locally, or how to use cloud APIs for your specific computing requirements. Investigate how AWS clients such as Shutterstock, Mirror, Tyson, and Persona use the AWS machine vision edge and cloud models for media analysis, content control, and quality inspection.

4. ( AIM312 ) Applying AWS machine learning to next-gen DevOps (chalk talk)

While DevOps technology has advanced significantly in recent years, it remains challenging. Concurrency, security, and the handling of sensitive data all necessitate expert judgement and frequently elude established techniques such as peer code reviews and unit testing. Even for companies that can afford to hire experienced code reviewers, the rapid pace at which software is generated generates large volumes of complex code that are impossible to manually examine. Learn how the AWS next-gen DevOps portfolio may assist enhance your developer's experience with machine learning capabilities to construct automation and more proactive methods that enable teams to innovate faster and with confidence by attending this chalk presentation.

5. ( AIM319 ) Prepare data for ML with ease, speed, and accuracy (session)

In this session you will discover how to use Amazon SageMaker to prepare data for machine learning in minutes. SageMaker provides data preparation tools that make it easier to label, prepare, and analyse your data. Walk through a complete data-preparation workflow, including how to use SageMaker Ground Truth to label training datasets, as well as how to extract data from numerous data sources, convert it using SageMaker Data Wrangler's prebuilt visualisation templates, and construct model features. Learn how to use SageMaker Feature Store to establish a repository for storing, retrieving, and sharing features to increase productivity.

In conclusion, AWS provides so many awesome sessions and workshops, I find the mentioned above sessions are very useful for beginners in ML on AWS.

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