Tag: Amazon SageMaker

Deploying machine learning (ML) models as a packaged container with hardware-optimized acceleration, without compromising accuracy and while being financially feasible, can be challenging. As machine learning models become the brains of modern applications, developers need a simpler way to deploy trained ML models to live endpoints for inference. This post explores how a ML engineer can take a trained model, optimize and containerize the model using OctoML CLI, and deploy it to Amazon EKS.

Industry analyst firm Gartner has published its annual report evaluating cloud AI developer services, the 2022 Magic Quadrant for Cloud AI Developer Services (CAIDS). AWS was once again named a Leader and placed highest among 13 recognized vendors for “Ability to Execute.” Choosing the right provider for cloud AI developer services is critically important right now. AWS Partners can leverage this report with their customers to showcase the value that AWS will bring to them.

Firms spend substantial efforts to identify and collect quality data streams from different sources. However, identifying and interpreting energy, water, or gas usage patterns and consumption types is sometimes insufficient. In this post, you’ll learn how Storm Reply, combining industry knowledge with its expertise in the development of data analytics platforms in AWS, can help customers in the design, development, and maintenance of secure serverless IoT big data platforms with a focus on sustainability.

Data-centric AI (DCAI) has been described as the discipline of systematically engineering the data used to build an AI system. It prescribes prioritizing improving data quality over tweaking algorithms to improve machine learning models. In this post, explore a DCAI solution built on Snowflake and Amazon SageMaker to serve as a factory for predictive analytics solutions. Learn about Snowflake’s integrations with SageMaker and get hands-on resources to help you put these capabilities into practice.

Companies that adopt an intelligent enterprise platform such as SAP S/4HANA can ensure they are prepared with a future-ready core. Learn how intelligent ERP environments like SAP S/4HANA deployed on AWS can offer businesses the operational resilience needed to stay viable amid continuing unpredictability. Leveraging AWS-native cloud services introduces opportunities for agile, flexible, cost-effective, co-innovation, and data-driven and scalable solutions for the intelligent enterprise.

Running genomics and high performance computing (HPC) workloads is complicated. To address these customer asks, Relevance Lab developed Research Gateway—a solution that delivers secure and scalable research without customers having to do the heavy lifting. This post provides an overview of the solution architecture and standard genomic research workflow, including a walkthrough of how to access Research Gateway, provision products required for their genomics sequencing analysis, and run the analysis.

With multiple mediums of communication, like social media, television, and OTT, marketers are finding it difficult to understand the factors that impact ad conversions, and provide visibility into marketing campaigns. To address this challenge, Quantiphi worked with Advocado, an advertising focused data platform provider, to develop a solution for ad attribution of web traffic to respective offline and online sources on AWS.

Although the business case for digital analytics is well-articulated, many organizations are looking for ways to build stronger cases around transformations by consolidating data generated across the enterprise with customer behavioral data. Learn how Softcrylic developed the Analytics Shift solution which helps businesses bring Adobe Analytics data into Amazon Redshift to drive deeper insights and data integration.

A closed loop assurance system predicts network events, such as faults and congestions, that are highly probable of causing service degradation or interruption, and automatically take preventive actions to avert service disruptions. Learn how Infosys leveraged AWS data streaming, data analytics, and machine learning services to ingest, process, and analyze high volumes of data from disparate sources; and to build ML models to predict network events that cause service degradation.

Learn now Provectus looked into how machine learning models were prototyped and evaluated at VTS, and then delivered a template-based solution enabling their data scientists to more easily create Amazon SageMaker jobs, pipelines, endpoints, and other AWS resources. The resulting coherent set of templates, with usage cookbook and extension guidelines, was applied successfully on an ML model that predicted leasing outcomes.