Amazon SageMaker JumpStart is a machine learning (ML) hub that can help you accelerate your ML journey. SageMaker JumpStart gives you access to built-in algorithms with pre-trained models from popular model hubs, pre-trained foundation models to help you perform tasks such as article summarization and image generation, and end-to-end solutions to solve common use cases. Today, I’m happy to announce that you can now share ML artifacts, such as models and notebooks, more easily with other users that share your AWS account using SageMaker JumpStart. Using SageMaker JumpStart to Share ML Artifacts Machine learning is a team sport. You might want to share your models and notebooks with other data scientists in your team to collaborate and increase productivity. Or,…
Category: AWS
Reposts from Amazon Web Services (AWS).
AWS Machine Learning University New Educator Enablement Program to Build Diverse Talent for ML/AI Jobs
AWS Machine Learning University is now providing a free educator enablement program. This program provides faculty at community colleges, minority-serving institutions (MSIs), and historically Black colleges and universities (HBCUs) with the skills and resources to teach data analytics, artificial intelligence (AI), and machine learning (ML) concepts to build a diverse pipeline for in-demand jobs of today and tomorrow. According to the National Science Foundation, Black and Hispanic or Latino students earn bachelor’s degrees in Computer Science—the dominant pathway to AI/ML—at a much lower rate than their white peers, earning less than 11 percent of computer science degrees awarded. However, research shows that having diverse perspectives among skilled practitioners and across the AI/ML lifecycle contributes to the development of AI/ML systems…
New for Amazon Redshift – Simplify Data Ingestion and Make Your Data Warehouse More Secure and Reliable
When we talk with customers, we hear that they want to be able to harness insights from data in order to make timely, impactful, and actionable business decisions. A common pattern with data-driven organizations is that they have many different data sources they need to ingest into their analytics systems. This requires them to build manual data pipelines spanning across their operational databases, data lakes, streaming data, and data within their warehouse. As a consequence of this complex setup, it can take data engineers weeks or even months to build data ingestion pipelines. These data pipelines are costly, and the delays can lead to missed business opportunities. Additionally, data warehouses are increasingly becoming mission critical systems that require high availability,…
New — Introducing Support for Real-Time and Batch Inference in Amazon SageMaker Data Wrangler
To build machine learning models, machine learning engineers need to develop a data transformation pipeline to prepare the data. The process of designing this pipeline is time-consuming and requires a cross-team collaboration between machine learning engineers, data engineers, and data scientists to implement the data preparation pipeline into a production environment. The main objective of Amazon SageMaker Data Wrangler is to make it easy to do data preparation and data processing workloads. With SageMaker Data Wrangler, customers can simplify the process of data preparation and all of the necessary steps of data preparation workflow on a single visual interface. SageMaker Data Wrangler reduces the time to rapidly prototype and deploy data processing workloads to production, so customers can easily integrate…