Giuseppe Angelo Porcelli Boost productivity with generative AI and scalable development using Jupy

Boost productivity with generative AI and scalable development using Jupyter, Amazon Code Whisperer, and SageMaker solutions for efficient machine learning workflows.

Key takeaways

Amazon Code Whisperer is an AI coding company as a service that was launched to help developers write code faster and more efficiently.
Jupyter AI provides integration points for various AWS services, including SageMaker, to enable data engineers and scientists to work together more efficiently.
The SageMaker distribution is a new open source project that provides a scalable and unified way to run machine learning workflows on AWS.
The SageMaker studio is a fully managed IDE for machine learning that provides integration with GitHub and other services.
The SageMaker distribution provides a standardized runtime for machine learning that allows for easy conversion of local code to scalable cloud-based code.
The Code Whisperer extension for JupyterLab provides AI-powered coding assistance for developers to generate code and provide explanations for complex concepts.
The SageMaker Distribution Lab provides a free, fully managed environment for data scientists and machine learning developers to test and validate their code.
The Jupyter notebooks are gaining popularity, with the number of notebooks available on GitHub increasing over the past 5 years.
The SageMaker studio provides seamless collaboration and integration with other AWS services, such as Amazon S3 and Amazon PCIe.
The SageMaker distribution provides a scalable way to run machine learning workloads on AWS, with features such as GPU acceleration and distributed computing.
The Code Whisperer extension for JupyterLab provides real-time collaboration and feedback for developers to improve their code quickly and efficiently.
The SageMaker studio provides reusable, managed notebooks that can be easily shared and collaborated on with others.
The SageMaker distribution provides a unified runtime for machine learning that allows for easy scaling and deployment of models.
The Code Whisperer extension for JupyterLab provides AI-powered code suggestions and explanations for developers to improve their coding skills.
The SageMaker studio provides a cloud-based environment for data scientists and machine learning developers to collaborate and develop models.
The SageMaker distribution provides a scalable and unified way to run machine learning workflows on AWS, with features such as GPU acceleration and distributed computing.
The Code Whisperer extension for JupyterLab provides AI-powered coding assistance for developers to generate code and provide explanations for complex concepts.
The SageMaker studio provides a fully managed IDE for machine learning that provides integration with GitHub and other services.
The SageMaker distribution provides a standardized runtime for machine learning that allows for easy conversion of local code to scalable cloud-based code.

Giuseppe Angelo Porcelli Boost productivity with generative AI and scalable development using Jupy

More talks