New for Amazon SageMaker – Perform Shadow Tests to Compare Inference Performance Between ML Model Variants

As you move your machine learning (ML) workloads into production, you need to continuously monitor your deployed models and iterate when you observe a deviation in your model performance. When you build a new model, you typically start validating the model offline using historical inference request data. But this data sometimes fails to account for current, real-world conditions. For example, new products might become trending that your product recommendation model hasn’t seen yet. Or, you experience a sudden spike in the volume of inference requests in production that you never exposed your model to before. Today, I’m excited to announce Amazon SageMaker support for shadow testing! Deploying a model in shadow mode lets you conduct a more holistic test by…

Next Generation SageMaker Notebooks – Now with Built-in Data Preparation, Real-Time Collaboration, and Notebook Automation

In 2019, we introduced Amazon SageMaker Studio, the first fully integrated development environment (IDE) for data science and machine learning (ML). SageMaker Studio gives you access to fully managed Jupyter Notebooks that integrate with purpose-built tools to perform all ML steps, from preparing data to training and debugging models, tracking experiments, deploying and monitoring models, and managing pipelines. Today, I’m excited to announce the next generation of Amazon SageMaker Notebooks to increase efficiency across the ML development workflow. You can now improve data quality in minutes with the built-in data preparation capability, edit the same notebooks with your teams in real time, and automatically convert notebook code to production-ready jobs. Let me show you what’s new! New Notebook Capability for…

New – Share ML Models and Notebooks More Easily Within Your Organization with Amazon SageMaker JumpStart

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,…

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…