AWS Partner Network (APN) Blog

Category: Amazon SageMaker

How to Use Amazon SageMaker Pipelines MLOps with Gretel Synthetic Data

Generating high-quality synthetic data protects privacy and augments scarce real-world data for training machine learning models. This post shows how to integrate the Gretel synthetic data platform with Amazon SageMaker Pipelines for a full ML workflow. Gretel’s integration with SageMaker Pipelines in a hybrid or fully managed cloud environment enables responsible and robust adoption of AI while optimizing model accuracy. With Gretel, data scientists can overcome data scarcity without compromising individuals’ privacy.

Automate Labeling for Intelligent Document Processing with Inawisdom and Amazon SageMaker Ground Truth

Intelligent document processing (IDP) automates data extraction from diverse document formats, accelerating information retrieval. Manually labeling is expensive and difficult, and Inawisdom’s IDP solution on AWS automates document labeling at scale to overcome this challenge. Its customized user interface in Amazon SageMaker Ground Truth lets subject matter experts efficiently label documents.

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How Shellkode Uses Amazon Bedrock to Convert Natural Language Queries to NoSQL Statements

Large language models like Amazon Bedrock can generate MongoDB queries from natural language questions, transforming how users access NoSQL databases. By leveraging AI and language models, this solution allows business users to query MongoDB data through conversational English instead of code. It connects to MongoDB with PyMongo, generates queries with LangChain and Bedrock, retrieves and formats results into natural language answers.

How Startups Can Fast-Track Their AWS Machine Learning Journey with Automat-IT’s MLOps Accelerator

Many startups want to use machine learning but struggle with developing scalable MLOps pipelines. Automat-IT’s MLOps Accelerator helps startups fast-track their machine learning journey and provides an end-to-end automated solution for the ML lifecycle, from data preparation to deployment, leveraging AWS services. With customizable pipelines and dedicated ML experts, Automat-IT empowers various roles to develop, operationalize, and monitor models efficiently.

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Reducing Inference Times by 87% for Darwinbox’s Talent Search Engine Using AWS Inferentia

Darwinbox wanted to reduce the time to infer resumes against job descriptions using PyTorch models. AWS Premier Partner Minfy helped them leverage Amazon SageMaker and AWS Inferentia to compile models with Neuron SDK and deploy them, achieving 87% faster inference without retraining. Key steps were compiling models with the Neuron SDK, extending SageMaker containers, using Inference Recommender to optimize configurations, and sending requests in mini-batches.

The Future of Search: Exploring Generative AI Chat-Based Solutions with AWS and Slalom

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.

How Infosys Built an Enterprise Knowledge Management Assistant Using Generative AI on AWS

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.

Amplifying Business Process Automations with UiPath and Amazon SageMaker

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.

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Automating the Know Your Customer Process Using Capgemini’s AI-Powered Solution on AWS

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.

Understanding and Monitoring Embeddings in Amazon SageMaker with WhyLabs AI Observatory Platform

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.