In this Amazon SageMaker themed episode of AWS TechChat, Shane & Tom start the show level setting on what Amazon SageMaker is and how and where it slots in to our product offerings.
Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning models quickly at scale.
We introduce Amazon SageMaker AutoPilot, which lets you automatically create the best classification and regression machine learning models, while providing full control and visibility.
We then talk about Amazon SageMaker Studio - your machine learning integrated development environment (IDE) in the cloud. Developers can write code, track experiments, visualize data, and perform debugging and monitoring all within a single, integrated visual interface. As part of Amazon SageMaker Studio, it comes with the new Amazon SageMaker Notebook, which allows developers to spin up machine learning notebooks in seconds, without needing to pick an instance and wait for it to be operational.
We also speak about Amazon SageMaker Processing, which lets you easily run your pre-processing, post-processing and model evaluation workloads on fully managed infrastructure. It also has a python SDK.
We all like to experiment and try things out, I am talking in the machine learning space here and because of that, we introduce Amazon SageMaker Model Monitor. Amazon SageMaker Model Monitor continuously monitors the quality of Amazon SageMaker machine learning models in production allowing you to set alerts for when there are deviations in the model quality.
To close the show out, we mention SageMaker Operators for Kubernetes, which you can access fully managed Amazon SageMaker ML tools and optimizations natively from Kubernetes, specifically for model training, hyperparameter optimization, real-time inference, and batch inference.
Shane Baldacchino - Solutions Architect, ANZ, AWS
Tom McMeekin - Solutions Architect, ANZ, AWS
AWS re:Invent 2019 Sessions & Podcast Feed http://aws-reinvent-audio.s3-website.us-east-2.amazonaws.com/2019/2019.html
Amazon SageMaker https://aws.amazon.com/sagemaker/
Amazon SageMaker Autopilot https://aws.amazon.com/sagemaker/autopilot/
Amazon SageMaker Studio https://aws.amazon.com/about-aws/whats-new/2019/12/introducing-amazon-sagemaker-studio-the-first-integrated-development-environment-ide-for-machine-learning/
Amazon SageMaker Notebook https://aws.amazon.com/about-aws/whats-new/2019/12/introducing-the-new-amazon-sagemaker-notebook-experience-now-in-preview/
Amazon SageMaker Processing https://aws.amazon.com/blogs/aws/amazon-sagemaker-processing-fully-managed-data-processing-and-model-evaluation/
Amazon SageMaker Experiments https://aws.amazon.com/blogs/aws/amazon-sagemaker-experiments-organize-track-and-compare-your-machine-learning-trainings/
Amazon SageMaker Debugger https://aws.amazon.com/blogs/aws/amazon-sagemaker-debugger-debug-your-machine-learning-models/
Amazon SageMaker Model Monitor https://aws.amazon.com/about-aws/whats-new/2019/12/introducing-amazon-sagemaker-model-monitor/
Amazon SageMaker Operators for Kubernetes https://aws.amazon.com/about-aws/whats-new/2019/12/introducing-amazon-sagemaker-operators-for-kubernetes/
AWS Builders Online Series https://aws.amazon.com/events/builders-online-series/
AWS Innovate AIML Edition https://aws.amazon.com/events/aws-innovate/machine-learning/
AWS Events and Webinars https://aws.amazon.com/events/