ML frameworks: AWS customers can choose from TensorFlow, PyTorch, Apache MXNet, and
other popular frameworks to experiment with and customize ML algorithms. You can use
the framework of your choice as a managed experience in Amazon SageMaker, or use the
AWS Deep Learning AMIs (Amazon Machine Images) and AWS Deep Learning Containers,
which are fully configured with the latest versions of the most popular deep learning
frameworks and tools. Amazon Elastic Compute Cloud (Amazon EC2) provides a wide selection
of instance types optimized to fit ML use cases—regardless of whether customers are training
models or running inference on trained models. These instances range from GPUs for
compute-intensive deep learning training to AWS Inferentia for low-cost inference.
Implementation support: The Amazon Machine Learning Solutions Lab pairs your team
with ML experts to help you identify and build ML solutions that address your organization's
highest ROI ML opportunities.
We also offer training to augment the level of ML expertise on your team, including developer
training, business leader training, and a hands-on event through the AWS Machine Learning
Embark Program.
Learn more about how you can transform the responsible use of AI and ML from theory into
practice with purpose-built services, resources, and training.
Learning tools: You can improve your ML capabilities with in-depth learning tools, including:
• AWS DeepRacer
• Machine Learning Training and Certification
• Amazon Machine Learning Solutions Lab
• Amazon SageMaker Studio Lab
Machine learning
with AWS, by
the numbers
100,000+ customers are using AWS
for their AI and ML workloads
20+ years of building experience
at Amazon
Up to 10x improvement in data
scientists' productivity
Hundreds of algorithms and models
in Amazon SageMaker JumpStart
21
