Amazon Web Services, Inc.

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Amazon SageMaker Fridays
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With Amazon SageMaker, you can get started faster with ML. Join our hosts for interactive sessions including demos, conversations, and some fun the last Friday of every month April through October 2022. SageMaker Fridays demonstrate how any user including data scientists, ML engineers, and business analysts can quickly onboard to SageMaker and start generating accurate ML predictions.
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Easily build, train, and deploy an ML model
Amazon SageMaker helps data scientists to prepare data, and build, train, and deploy ML models quickly by bringing together a broad set of purpose-built capabilities. Data scientists, join us for live coding and a hands-on demo showing how to develop an ML model end-to-end using SageMaker.
Who should attend: Data Scientists, Data Engineers, ML Developers, ML Engineers
Date: March 31, 2022
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Low-Code ML
Amazon SageMaker offers low-code options for each step of the ML lifecycle so you can build, train, and deploy high quality models faster. Data scientists, join us for a demo to see how data visualizations, pre-build models, and AutoML can accelerate your machine learning model delivery, so you can focus more on refining predictions and less on low level code.
Who should attend: Data Scientists, ML Expert Practitioners
Date: April 29, 2022
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Train large deep learning models with hundreds of billions of parameters
State-of-the-art deep learning models can be difficult to train because of the cost, time, and skills required to optimize memory and compute. Join us for a discussion on how to overcome the common pitfalls of training deep learning models, and how SageMaker helps overcome these challenges by optimizing clusters of compute instances and more.
Who should attend: Data Scientists, ML Expert Practitioners
Date: May 27, 2022
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Automate ML workflows
MLOps practices help accelerate and streamline the ML development lifecycle. ML engineers, join us for a hands-on demo showing how to use Amazon SageMaker to implement MLOps practices, including automating ML workflows, building Continuous Integration/Continuous Delivery CI/CD pipelines for ML, monitoring models in production, and standardizing model governance
Who should attend: MLOps Engineers, Data Scientists
Date: June 24, 2022
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Learn ML for free
Amazon SageMaker Studio Lab is a free ML development environment that provides the compute, storage (up to 15GB), and security—all at no cost—for anyone to learn and experiment with ML. Join us for a hands-on demo where you start with an email address and end with an ML model in cloud. If you don't have an account yet, make sure to register at studiolab.sagemaker.aws for free so you can follow along.
Who should attend: Students, ML learners, ML beginners
Date: July 29, 2022
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Generate accurate ML predictions without writing code
Calling all business analysts and supporting ML professionals. AWS is changing the way businesses can gather insights and improve decision making by democratizing ML access. Join us for a demo of Amazon SageMaker Canvas showing how you can turn spreadsheets into predictions. See how anyone, regardless of your background or skills, can create machine learning models.
Who should attend: Business Analysts & Supporting ML Professionals
Date: August 26, 2022
Deploy an ML model for best performance, cost, and prediction quality
High performance and cost-effective deployment techniques can help you maximize the return on your machine learning investments. ML engineers, join us for a hands-on demo showing how to use Amazon SageMaker to optimize inference workloads and reduce infrastructure costs.
Who should attend: MLOps Engineers, ML Engineers, Data Scientists
Date: September 30, 2022
Accelerate deep learning model development with cloud custom environments
Deep learning (DL) projects often require integrating custom libraries with popular open-source frameworks such as TensorFlow, PyTorch, and Hugging Face. Setting up, managing, and scaling custom ML environments can be time consuming and cumbersome, even for experts. With AWS Deep Learning Containers, you get access to prepackaged and optimized DL frameworks that make it easy for you to customize, extend, and scale your environments. In this session, learn how to use Deep Learning Containers to build your custom ML environment and how to implement model training and inference with Deep Learning Containers in Amazon SageMaker.
Who should attend: Data Scientists, ML Expert Practitioners
Date: October 28, 2022