We are excited to announce new AWS AI Service Cards, a resource to increase transparency and help customers better understand our AWS AI services, including how to use them in a responsible way. AI service cards are a form of responsible AI documentation that provides customers with a single place to find information on the intended use cases and limitations, responsible AI design choices, and best deployment and operation best practices for our AI Services. They are part of a comprehensive development process we undertake to build our services in a responsible way with fairness, explainability, veracity and robustness, governance, transparency, privacy andsecurity, safety, and controllability.

Today, we are excited to announce the general availability of a simplified developer experience for Amazon SageMaker Pipelines. The improved Python SDK enables you to build Machine Learning (ML) workflows quickly with familiar Python syntax. Key features of the SDK include a new Python decorator (@step) for custom steps, a Notebook Jobs step type, and a workflow scheduler.

You can now use Amazon Bedrock to process prompts in batch to get responses for model evaluation, experimentation, and offline processing. 

Amazon SageMaker Canvas is a no-code tool to build ML models and generate machine learning (ML) predictions. As announced on October 5, customers can access and evaluate foundation models (FMs) from Amazon Bedrock and SageMaker JumpStart to generate and summarize content. 

We are excited to announce new capabilities on Amazon SageMaker which help customers reduce model deployment costs by 50% on average and achieve 20% lower inference latency on average. Customers can deploy multiple models to the same instance to better utilize the underlying accelerators. SageMaker actively monitors instances that are processing inference requests and intelligently routes requests based on which instances are available.

Amazon SageMaker notebook jobs allows data scientists to run their notebooks on demand or on a schedule with a few clicks on Amazon SageMaker Studio, a web-based IDE for machine learning (ML). Today, we’re excited to announce that you can programmatically run notebooks as jobs using APIs provided by SageMaker Pipelines, SageMaker’s ML workflow orchestration service. Furthermore, you can create a multi-step ML workflow with multiple dependent notebooks using these APIs.

We are excited to announce new tools and improvements that enable customers to reduce the time from days to hours to deploy machine learning (ML) models including foundation models (FMs) on Amazon SageMaker for Inference at scale . This includes a new Python SDK library that simplifies the process of packaging and deploying a ML model on SageMaker from seven steps to one with an option to do local inference. Further, Amazon SageMaker is offering new interactive UI experiences in Amazon SageMaker Studio that will help customers quickly deploy their trained ML model or FMs using performant and cost-optimized configurations in as few as three clicks.

Amazon SageMaker Canvas now supports natural language instructions for data exploration, visualization, and preparation to build machine learning (ML) models. Amazon SageMaker Canvas is a no-code tool that enables customers to easily create highly accurate ML models without writing a line of code. Starting today, you can use FM-powered natural language instructions enabled by Amazon Bedrock for data preparation. This new capability enables you to interact with your data, ask questions, visualize feature distribution and correlations, and transform data to the right structure for your business problems, using natural language queries.

With credit card debt at record highs and more and more (young) consumers falling behind on their credit card payments, Buy Now, Pay Later (BNPL) services are expected to see …

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Thanks to IDC for this one – the top five enterprise WLAN companies:  

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