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.

At its re:Invent conference, AWS today announced SageMaker Studio Lab, a free service to help developers learn machine learning techniques and experiment with the technology. 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. In addition, Amazon also today launched the AWS AI & ML Scholarship Program. The company is committing $10 million oer year to this program, which it runs in collaboration with Intel and Udacity. "The two initiatives we are announcing today are designed to open up educational opportunities in machine learning to make it more widely accessible to anyone who is interested in the technology," said Swami Sivasubramanian, Vice President of Amazon Machine Learning at AWS. "Machine learning will be one of the most transformational technologies of this generation. If we are going to unlock the full potential of this technology to tackle some of the world's most challenging problems, we need the best minds entering the field from all backgrounds and walks of life. We want to inspire and excite a diverse future workforce through this new scholarship program and break down the cost barriers that prevent many from getting started with machine learning."