Want to accelerate machine learning (ML) innovation in your organization? Join us for 30 days of new Amazon SageMaker content designed to help you build, train, and deploy ML models faster. On April 20, we’re kicking off 30 days of hands-on workshops, Twitch sessions, Slack chats, and partner perspectives. Our goal is to connect you with AWS experts—including Greg Coquillio, the second-most influential speaker according to LinkedIn Top Voices 2020: Data Science & AI and Julien Simon, the number one AI evangelist according to AI magazine —to learn hints and tips for success with ML.
We built SageMaker from the ground up to provide every developer and data scientist with the ability to build, train, and deploy ML models quickly and at lower cost by providing the tools required for every step of the ML development lifecycle in one integrated, fully managed service. We have launched over 50 SageMaker capabilities in the past year alone, all aimed at making this process easier for our customers. The customer response to what we’re building has been incredible, making SageMaker one of the fastest growing services in AWS history.
To help you dive deep into these SageMaker innovations, we’re dedicating April 20 – May 21, 2021 to SageMaker education. Here are some must dos to add to your calendar:
Besides these virtual hands-on opportunities, we will have regular blog posts from AWS experts and our partners, including Snowflake, Tableau, Genesys, and DOMO. Bookmark the SageMaker Month webpage or sign up to our weekly newsletters so you don’t miss any of the planned activities.
But we aren’t stopping there!
To coincide with SageMaker Month, we launched new Savings Plans. The SageMaker Savings Plans offer a flexible, usage-based pricing model for SageMaker. The goal of the savings plans is to offer you the flexibility to save up to 64% on SageMaker ML instance usage in exchange for a commitment of consistent usage for a 1 or 3-year term. For more information, read the launch blog. Further, to help you save even more, we also just announced a price drop on several instance families in SageMaker.
The SageMaker Savings Plans are on top of the productivity and cost-optimizing capabilities already available in SageMaker Studio. You can improve your data science team’s productivity up to 10 times using SageMaker Studio. SageMaker Studio provides a single web-based visual interface where you can perform all your ML development steps. SageMaker Studio gives you complete access, control, and visibility into each step required to build, train, and deploy models. You can quickly upload data, create new notebooks, train and tune models, move back and forth between steps to adjust experiments, compare results, and deploy models to production all in one place, which boosts productivity.
You can also optimize costs through capabilities such as Managed Spot Training, in which you use Amazon Elastic Compute Cloud (Amazon EC2) Spot Instances for your SageMaker training jobs (see Optimizing and Scaling Machine Learning Training with Managed Spot Training for Amazon SageMaker), and Amazon Elastic Inference, which allows you to attach just the right amount of GPU-powered inference acceleration to any SageMaker instance type.
We are also excited to see continued customer momentum with SageMaker. Just in the first quarter of 2021, we launched 15 new SageMaker case studies and references, spanning a wide range industries including SNCF, Mueller, Bundesliga, University of Oxford, and Latent Space. Some highlights include:
- The data science team at SNFC reduced model training time from 3 days to 10 hours.
- Mueller Water Products automated the daily collection of more than 5 GB of data and used ML to improve leak-detection performance.
- Latent Space scaled model training beyond 1 billion parameters.
We would love for you to join the thousands of customers who are seeing success with Amazon SageMaker. We want to add you to our customer reference list, and we can’t wait to work with you this month!
About the Author
Kimberly Madia is a Principal Product Marketing Manager with AWS Machine Learning. Her goal is to make it easy for customers to build, train, and deploy machine learning models using Amazon SageMaker. For fun outside work, Kimberly likes to cook, read, and run on the San Francisco Bay Trail.
- Amazon EC2
- Amazon SageMaker
- Blog Posts
- Data Science
- data scientist
- machine learning
- San Francisco
- University of Oxford