Category: Amazon SageMaker

In a recent webinar, Slalom and AWS showcased the incredible potential of chat-based enterprise search powered by AWS generative AI services like Amazon Bedrock. We’re excited to share key takeaways and a more in-depth exploration of the transformative landscape that chat-based search creates. Learn how technologies like Amazon Bedrock empower businesses to build intelligent chat-based interfaces that allow employees to interact with company data conversationally.

A common challenge faced by many companies involves the requirement to enhance the clarity and availability of internal documents. These scenarios present significant hurdles for support teams, business users, and new members who often encounter difficulties locating the relevant documentation. This post discusses how Infosys built an enterprise knowledge management assistant using generative AI technologies on AWS.

Organizations are increasingly turning to intelligent automation technologies to streamline their business processes and improve efficiency. Learn how UiPath Business Automation Platform and Amazon SageMaker can be integrated to help businesses automate complex processes, improve decision making, and drive innovation by leveraging the power of AI. The solution allows customers to bring machine learning inference from SageMaker directly into their business automation.

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.