AWS has introduced SageMaker Studio Lab, a free service to help developers learn machine-learning techniques and experiment with the technology. SageMaker Studio Lab provides users with all of the basics to get started, including a JupyterLab IDE, model training on CPUs and GPUs and 15 GB of persistent storage.
SageMaker Studio Lab has all the basics to create data analytics, scientific computing, and machine-learning projects with notebooks, which can be easily imported and exported via the Git repo or a private Amazon S3 bucket.
SageMaker Studio Lab becomes an alternative to the popular Google Colab environment, providing free CPU/GPU access.
Another enhancement for AWS SageMaker is a visual, no-code tool called SageMaker Canvas. Canvas allows business analysts to build machine-learning models and generate predictions by browsing disparate data sources in the cloud or on premises, combining datasets, and training models once updated data is available. The new service exposes a wizard-style user interface to upload data, train models, and perform predictions.
In addition, AWS also introduced a new turnkey service that employs an expert workforce to deliver high-quality training datasets while eliminating the need for companies to manage their own labeling applications. This new service is the SageMaker Ground Truth Plus. With SageMaker Ground Truth, data scientists have options to work with labelers inside and outside of their organization.
SageMaker Training Compiler, another new SageMaker capability, aims to accelerate the training of deep-learning models by automatically compiling developers’ Python programming code and generating GPU kernels specifically for their model. The compiler optimizes deep-learning models to accelerate training by more efficiently using SageMaker machine-learning GPU instances. The service is available for free within the SageMaker platform.
Last, is the SageMaker Serverless Inference, a new inference option that enables users to deploy machine-learning models for inference without having to configure or manage the underlying infrastructure. With Serverless Inference, SageMaker automatically provisions, scales, and turns off compute capacity based on the volume of inference requests. Customers only pay for the duration of running the inference code and the amount of data processed, not for idle time.
You can request a free SageMaker Studio Lab account. The number of new account registrations will be limited to ensure a high quality of experience for customers. You can find sample notebooks in the Studio Lab GitHub repository.