Today, I’m happy to introduce the ability to use natural language instructions in Amazon SageMaker Canvas to explore, visualize, and transform data for machine learning (ML). SageMaker Canvas now supports using foundation model-(FM) powered natural language instructions to complement its comprehensive data preparation capabilities for data exploration, analysis, visualization, and transformation. Using natural language instructions, you can now explore and transform your data to build highly accurate ML models. This new capability is powered by Amazon Bedrock. Data is the foundation for effective machine learning, and transforming raw data to make it suitable for ML model building and generating predictions is key to better insights. Analyzing, transforming, and preparing data to build ML models is often the most time-consuming part…
Tag: AWS News Blog
Amazon SageMaker adds new inference capabilities to help reduce foundation model deployment costs and latency
Today, we are announcing new Amazon SageMaker inference capabilities that can help you optimize deployment costs and reduce latency. With the new inference capabilities, you can deploy one or more foundation models (FMs) on the same SageMaker endpoint and control how many accelerators and how much memory is reserved for each FM. This helps to improve resource utilization, reduce model deployment costs on average by 50 percent, and lets you scale endpoints together with your use cases. For each FM, you can define separate scaling policies to adapt to model usage patterns while further optimizing infrastructure costs. In addition, SageMaker actively monitors the instances that are processing inference requests and intelligently routes requests based on which instances are available, helping…
Leverage foundation models for business analysis at scale with Amazon SageMaker Canvas
Today, I’m excited to introduce a new capability in Amazon SageMaker Canvas to use foundation models (FMs) from Amazon Bedrock and Amazon SageMaker Jumpstart through a no-code experience. This new capability makes it easier for you to evaluate and generate responses from FMs for your specific use case with high accuracy. Every business has its own set of unique domain-specific vocabulary that generic models are not trained to understand or respond to. The new capability in Amazon SageMaker Canvas bridges this gap effectively. SageMaker Canvas trains the models for you so you don’t need to write any code using our company data so that the model output reflects your business domain and use case such as completing a marketing analysis.…
Introducing highly durable Amazon OpenSearch Service clusters with 30% price/performance improvement
You can use the new OR1 instances to create Amazon OpenSearch Service clusters that use Amazon Simple Storage Service (Amazon S3) for primary storage. You can ingest, store, index, and access just about any imaginable amount of data, while also enjoying a 30% price/performance improvement over existing instance types, eleven nines of data durability, and a zero-time Recovery Point Objective (RPO). You can use this to perform interactive log analytics, monitor application in real time, and more. New OR1 Instances These benefits are all made possible by the new OR1 instances, which are available in eight sizes and used for the data nodes of the cluster: Instance Name vCPUs Memory EBS Storage Max (gp3) or1.medium.search 1 8 GiB 400 GiB…