Category: Amazon SageMaker

Financial institutions use “Know Your Customer” (KYC) as the process of identifying and verifying a customer’s identity prior to providing any financial service. Learn how Capgemini’s KYC solution helps institutions automate identity documents validation, extraction of information present in them, and forgery detection using AI. It provides customers an extensible automated solution for validating government-issued documents, while reducing the overall time and manual intervention required to onboard customers.

With the rise of large language models, natural language processing, and generative AI models, embeddings are becoming a critical piece of data in more machine learning use cases. In this post, explore different ways embeddings are used in machine learning and where problems can show up that impact your ML models, and how you can use WhyLabs to identify those problems and create monitors to avoid them showing up again in the future.

Migrating to a new hosting provider to save costs presents an opportunity to fine-tune application performance and the DevOps processes supporting a company’s applications. This was the case for MasterWorks, which is based near Seattle and helps move the hearts and minds of people to act for Christian ministries across America. Learn how Avahi Technologies and AWS collaborated to help MasterWorks migrate an application that uses machine learning models from a SaaS provider to AWS.

Adversarial machine learning (AML) attacks, also known as “artificial intelligence attacks” (AI attacks), involve deliberate attempts to manipulate or compromise machine learning models or even make it reveal sensitive information. Explore how AIShield‘s seamless integration within the Amazon SageMaker environment alleviates AI security concerns by mitigating risks before and after deployment, enabling customers to develop and deploy AI applications with confidence.

Maintaining multiple machine learning models across different teams can be challenging. Having a centralized platform to monitor and manage them can significantly reduce operational overhead and improve efficiency. Learn how the models trained and deployed in Amazon SageMaker can be monitored by DataRobot in a highly scalable fashion. In this way, customers can monitor both DataRobot-originated models and SageMaker-originated models under a single pane of glass.

Managing existing brownfield buildings is a challenging task because teams usually lack accurate ground truth data. Object detection algorithms are a key technology to automate and scale the creation of a digital building twin, providing a solution to this challenge. For detecting objects in indoor environments with machine learning, learn how NavVis and AWS collaborated to build a digital building twin for a large industry customer.

When migrating on-premises MLOps to Amazon SageMaker Pipelines, customers often find it challenging to monitor metrics in training scripts and add inference scripts for custom machine learning models. Learn how Mission Cloud implemented an end-to-end SageMaker Pipeline to build the workflow of model development to production, accelerating their customer’s computer vision model production process. SageMaker Pipelines is a workflow orchestration tool for building ML pipelines with CI/CD capabilities.

Organizations have matured and have overcome the initial hurdles of proving the capabilities of AI. The challenge now is operationalizing AI and building engineering excellence to successfully adopt and manage machine learning at scale. Learn how Quantiphi assisted Venterra Realty in bringing in the best ML solution development and deployment practices through NeuralOps—a framework built on Amazon SageMaker.

We’re excited to announce the launch of the Amazon SageMaker Ready specialization for AWS Partners with Amazon SageMaker software offerings. Through this specialization, customers can identify software solutions that integrate with Amazon SageMaker—allowing them to seamlessly solve use cases and innovate with machine learning. Software offerings include data platforms, data pre-processing and feature stores, ML frameworks, MLOps tools, and business decisioning and applications.

Teradata has expanded its collaboration with AWS by adding integration capabilities for Teradata Vantage, the data platform for enterprise analytics and AWS cloud services. Vantage, with its NOS read/write connector to Amazon S3 data, already provides data integration with S3 data and Vantage enterprise data. Now, Teradata introduces an API integration with Amazon SageMaker and Amazon Forecast. This enables business users to drive outcomes with real-time analytics.