AWS month in review: More AWS deep learning capabilities
This month, AWS gave its users more machine learning capabilities along with a few opportunities to learn, train and get certified with the technology.
Announced at the AWS Summit in Santa Clara, AWS Deep Learning Containers (DL Containers) enable developers to use Docker images preinstalled with deep learning frameworks, such as TensorFlow and Apache MXNet, and can scale machine learning workloads efficiently.
Developers often use Docker containers for machine learning workloads and custom machine learning environments, but that usually involves days of testing and configuration. DL Containers will help developers deploy these machine learning workloads more quickly on Amazon Elastic Container Service (ECS) and Amazon Elastic Container Service for Kubernetes (EKS),.
DL Containers offers the flexibility to build custom machine learning workflows for training, validation, and deployment and handles container orchestration as well. Along with EKS and ECS, DL Containers will work with Kubernetes on Amazon EC2 as well. This new capability will enable developers to focus on deep learning — building and training new models — instead of tedious container orchestration.
AWS also added a new specialty certification for machine learning. The AWS Certified Machine Learning Specialty certification validates a user’s ability to design, implement, deploy, and maintain AWS machine learning services and processes. The exam costs $40.
Concurrency Scaling for Redshift
AWS now offers Concurrency Scaling to handle high volume requests in Amazon Redshift. Before Concurrency Scaling, Redshift users encountered performance issues when too many business analysts tried to access the database concurrently; Redshift’s compute capability lacked the flexibility to adapt on-demand.
Now, when users enable the Concurrent Scaling feature, Redshift automatically adds additional cluster capacity at peak times. You pay for what you use and can remove the extra processing power when it’s no longer needed.
AWS Direct Connect console completes global transformation
The global AWS Direct Connect console is now generally available with a redesigned UI. The service establishes a dedicated connection between an organization’s datacenter and AWS, but those connections were previously limited to links to Direct Connect locations within the same AWS region. However, users now have the ability to connect to any AWS region — except China — from any AWS Direct Connect location.
AWS also increased connection capacity — available through approved Direct Connect Partners — and lowered prices for low-end users.
DeepRacer League kicks off
The AWS Santa Clara Summit was also opening day for the AWS DeepRacer League’s summer circuit, a workshop and competition with AWS’ little autonomous car that could.
Introduced at re:Invent 2018, AWS DeepRacer is a one-eighth scale car that includes a fully configured environment on Amazon’s cloud. Operators train their vehicles with reinforcement learning models, such as an autonomous driving model. Much like a human or dog, DeepRacer learns via trial and error and users can reward their DeepRacer for success. Reinforcement learning models include reward functions that reward — think of code as a treat here — the car for good behavior, which in this case, means staying on the track. AWS DeepRacer is meant to get developers hands-on experience with reinforcement learning, a recent capability added to Amazon SageMaker.
Congratulations to Cloud Brigade, who with a time of 00:10.43 sits in the pole position on the leaderboard after the first contest. AWS’ toy cars go on sale in April.