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. Machine learning: From front end to inference

Explore a real-world scenario for creating an end-to-end solution using machine learning in this lab. Create a web front end that receives user input and evaluates it using a machine learning model. Then, create and run a machine learning backend application using different AWS services.

2. Develop your ML project with Amazon SageMaker

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. Get started with AWS computer vision services

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. Applying AWS machine learning to next-gen DevOps

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 (ML) capabilities to construct automation and more proactive methods that enable teams to innovate faster and with confidence by attending this chalk presentation.

5. Prepare data for ML with ease, speed, and accuracy

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.