Last week the annual AWS technology conference re:Invent 2020 kicked off virtually. Typically a week-long physical conference held in Las Vegas this year, with the ongoing COVID19 pandemic, the conference moved to a virtual 3-week event. If you have paid attention to re:Invent, the past couple of years, machine learning has always taken center stage during re:Invent keynotes. So much that it has overshadowed other releases, this year, AWS decided, and rightly so, that Machine Learning deserved its own dedicated keynote.

Why should you care? While most machine learning experiments might start locally, you eventually end up in the cloud once you start doing machine learning at a production scale. Besides, when you look at these production workloads, they are overwhelmingly composed of algorithms in TensorFlow, PyTorch, and MXNet. And finally, over 90% of cloud machine learning based on TensorFlow and PyTorch runs on AWS.

Let start with the hardware.

AWS Trainium A machine learning chip custom-designed by AWS, specifically for training machine learning models in the cloud. This is the second silicon from AWS after AWS Infrentia and shares the same AWS Neuron SDK for developers. Why it matters? Cost-effective and high performance for your deep learning training on the cloud.

EC2 instances powered by Habana Gaudi EC2 instances powered by Habana Gaudi accelerators. Also available via Amazon SageMaker, AWS ECS, and AWS EKS.

#sagemaker #machine-learning #aws #aws re:invent