According to a 2020 survey of data scientists conducted by Anaconda, data preparation is one of the critical steps in machine learning (ML) and data analytics workflows, and often very time consuming for data scientists. Data scientists spend about 66% of their time on data preparation and analysis tasks, including loading (19%), cleaning (26%), and visualizing data (21%). Amazon SageMaker Studio is the first fully integrated development environment (IDE) for ML. With a single click, data scientists and developers can quickly spin up Studio notebooks to explore datasets and build models. If you prefer a GUI-based and interactive interface, you can use Amazon SageMaker Data Wrangler, with over 300 built in visualizations, analyses, and transformations to efficiently process data backed by Spark without writing a single line of code.

Amazon SageMaker now allows you to compare the performance of a new version of a model serving stack with the currently deployed version prior to a full production rollout using a deployment safety practice known as shadow testing. Shadow testing can help you identify potential configuration errors and performance issues before they impact end-users. With SageMaker, you don't need to invest in building your shadow testing infrastructure, allowing you to focus on model development. SageMaker takes care of deploying the new version alongside the current version serving production requests, routing a portion of requests to the shadow version. You can then compare the performance of the two versions using metrics such as latency and error rate.

As companies are increasingly adopting machine learning (ML) for their mainstream enterprise applications, more of their business decisions are influenced by ML models. As a result of this, having simplified access control and enhanced transparency across all your ML models makes it easier to validate that your models are performing well and take action when they are not. In this post, we explore how companies can improve visibility into their models with centralized dashboards and detailed documentation of their models using two new features: SageMaker Model Cards and the SageMaker Model Dashboard. Both these features are available at no additional charge to SageMaker customers. Model governance is a framework that gives systematic visibility into model development, validation, and usage.

Administrators of machine learning (ML) workloads are focused on ensuring that users are operating in the most secure manner, striving towards a principal of least privilege design. They have a wide variety of personas to account for, each with their own unique sets of needs, and building the right sets of permissions policies to meet those needs can sometimes be an inhibitor to agility. In this post, we look at how to use Amazon SageMaker Role Manager to quickly build out a set of persona-based roles that can be further customized to your specific requirements in minutes, right on the Amazon SageMaker console. Role Manager offers predefined personas and ML activities combined with a wizard to streamline your permission generation process, allowing your ML practitioners to perform their responsibilities with the minimal necessary permissions. If you require additional customization, SageMaker Role Manager allows you to specify networking and encryption permissions for Amazon Virtual Private Cloud (Amazon VPC) resources and AWS Key Management Service (AWS KMS) encryption keys, and attach your custom policies.

Amazon SageMaker Studio provides a fully managed solution for data scientists to interactively build, train, and deploy machine learning (ML) models. In addition to the interactive ML experience, data workers also seek solutions to run notebooks as ephemeral jobs without the need to refactor code as Python modules or learn DevOps tools and best practices to automate their deployment infrastructure. Previously, when data scientists wanted to take the code they built interactively on notebooks and run them as batch jobs, they were faced with a steep learning curve using Amazon SageMaker Pipelines, AWS Lambda, Amazon EventBridge, or other solutions that are difficult to set up, use, and manage. With SageMaker notebook jobs, you can now run your notebooks as is or in a parameterized fashion with just a few simple clicks from the SageMaker Studio or SageMaker Studio Lab interface. You can run these notebooks on a schedule or immediately.

AI tech can make retail storefronts relevant and engaging to the modern consumer. With the continued dominance of artificial intelligence in business applications, we've started to see a dramatic shift in how people shop for and purchase products. At least 60% of the U.S. population have made mobile purchases; 82% of mobile phone users use their devices while in-store to help them make a product decision. As mobile commerce continues to grow, retail stores will need to adopt new technologies to stay afloat. Consumer reliance on smart devices will only become greater, so brick-and-mortar stores must act quickly if they don't want to become outdated.

We're thrilled to announce that Stability AI has selected AWS as its preferred cloud provider to power its state-of-the-art AI models for image, language, audio, video, and 3D content generation. Stability AI is a community-driven, open-source artificial intelligence (AI) company developing breakthrough technologies. With Amazon SageMaker, Stability AI will build AI models on compute clusters with thousands of GPU or AWS Trainium chips, reducing training time and cost by 58%. Stability AI will also collaborate with AWS to enable students, researchers, startups, and enterprises around the world to use its open-source tools and models. "Our mission at Stability AI is to build the foundation to activate humanity's potential through AI. AWS has been an integral partner in scaling our open-source foundation models across modalities, and we are delighted to bring these to SageMaker to enable tens of thousands of developers and millions of users to take advantage of them. We look forward to seeing the amazing things built on these models and helping our customers customize and scale their models and solutions."

Today we are excited to announce that AI21 Jurassic-1 (J1) foundation models are available for customers using Amazon SageMaker. Jurassic-1 models are highly versatile, capable of both human-like text generation, as well as solving complex tasks such as question answering, text classification, and many others. You can easily try out this model and use it with Amazon SageMaker JumpStart. JumpStart is the machine learning (ML) hub of SageMaker that provides access to foundation models in addition to built-in algorithms and end-to-end solution templates to help you quickly get started with ML. In this post, we walk through how to use the Jurassic-1 Grande model in SageMaker.

Cyber Monday has come and gone, but if you're still looking to pick up a new smart display or two ahead of the holidays, a newer deal on Amazon's Echo Shows may be of interest: The retailer is currently offering a bundle that pairs its Echo Show 8 with the Kids edition of its Echo Show 5 for $70. We've seen the Echo Show 8 alone go for $70 for much of the last two months, but that still equals the lowest price we've tracked. Normally, it retails closer to $100. With this deal, you're effectively getting an Echo Show 5 Kids thrown in at no extra cost. That device is currently available on its own for $40, but its average street price over the last few months has sat closer to $60.

Netail, a technology that enables retailers to auto-identify competitors across the internet and track their assortments, availability and optimize prices in real time, announced the closing of $5 Million in seed funding. The round was co-led by Magarac Venture Partners (MVP), which provides early-stage venture capital to dynamic entrepreneurs and successful technology companies throughout the Midwest, and Dr. Andrew Ng's AI Fund. Other investors include HKSTP Ventures. Consumer behavior has changed dramatically, and the majority of purchase decisions are now made online via search, marketplaces and social media. With retailers struggling to adapt, Netail's AI technology is designed to help them succeed by attracting, converting and retaining customers in these increasingly competitive digital arenas.