A couple days ago, I had the honor of doing a live stream on generative AI, discussing recent innovations and concepts behind the current generation of large language and vision models and how we got there. In today’s roundup of news and announcements, I will share some additional information—including an expanded partnership to make generative AI more accessible, a blog post about diffusion models, and our weekly Twitch show on Generative AI. Let’s dive right into it! Last Week’s Launches Here are some launches that got my attention during the previous week: Integrated Private Wireless on AWS – The Integrated Private Wireless on AWS program is designed to provide enterprises with managed and validated private wireless offerings from leading communications…
Tag: Antje Barth
AWS Week in Review – January 16, 2023
Today, we celebrate Martin Luther King Jr. Day in the US to honor the late civil rights leader’s life, legacy, and achievements. In this article, Amazon employees share what MLK Day means to them and how diversity makes us stronger. Coming back to our AWS Week in Review—it’s been a busy week! Last Week’s Launches Here are some launches that got my attention during the previous week: AWS Local Zones in Perth and Santiago now generally available – AWS Local Zones help you run latency-sensitive applications closer to end users. AWS now has a total of 29 Local Zones; 12 outside of the US (Bangkok, Buenos Aires, Copenhagen, Delhi, Hamburg, Helsinki, Kolkata, Muscat, Perth, Santiago, Taipei, and Warsaw) and 17…
New – Bring ML Models Built Anywhere into Amazon SageMaker Canvas and Generate Predictions
Amazon SageMaker Canvas provides business analysts with a visual interface to solve business problems using machine learning (ML) without writing a single line of code. Since we introduced SageMaker Canvas in 2021, many users have asked us for an enhanced, seamless collaboration experience that enables data scientists to share trained models with their business analysts with a few simple clicks. Today, I’m excited to announce that you can now bring ML models built anywhere into SageMaker Canvas and generate predictions. New – Bring Your Own Model into SageMaker Canvas As a data scientist or ML practitioner, you can now seamlessly share models built anywhere, within or outside Amazon SageMaker, with your business teams. This removes the heavy lifting for your…
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…