article thumbnail

Enhance customer support with Amazon Bedrock Agents by integrating enterprise data APIs

AWS Machine Learning

AI agents , powered by large language models (LLMs), can analyze complex customer inquiries, access multiple data sources, and deliver relevant, detailed responses. In this post, we guide you through integrating Amazon Bedrock Agents with enterprise data APIs to create more personalized and effective customer support experiences.

APIs 128
article thumbnail

Governing the ML lifecycle at scale, Part 3: Setting up data governance at scale

AWS Machine Learning

This post dives deep into how to set up data governance at scale using Amazon DataZone for the data mesh. The data mesh is a modern approach to data management that decentralizes data ownership and treats data as a product.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

Protect sensitive data in RAG applications with Amazon Bedrock

AWS Machine Learning

Implementing RAG-based applications requires careful attention to security, particularly when handling sensitive data. The protection of personally identifiable information (PII), protected health information (PHI), and confidential business data is crucial because this information flows through RAG systems.

APIs 89
article thumbnail

Transforming credit decisions using generative AI with Rich Data Co and AWS

AWS Machine Learning

The mission of Rich Data Co (RDC) is to broaden access to sustainable credit globally. Making credit decisions using AI can be challenging, requiring data science and portfolio teams to synthesize complex subject matter information and collaborate productively. In this example, we start with the data science or portfolio agent.

article thumbnail

The Latest C2Perform Index: Key Support Trends From 100M+ Data Points

Download now to stay ahead of evolving support operations, arm your team with data‑driven strategies, and join a community dedicated to continuous performance improvement.

article thumbnail

Generate training data and cost-effectively train categorical models with Amazon Bedrock

AWS Machine Learning

In this post, we explore how you can use Amazon Bedrock to generate high-quality categorical ground truth data, which is crucial for training machine learning (ML) models in a cost-sensitive environment. For the multiclass classification problem to label support case data, synthetic data generation can quickly result in overfitting.

Education 109
article thumbnail

Improve Amazon Nova migration performance with data-aware prompt optimization

AWS Machine Learning

To mitigate this challenge, thorough model evaluation, benchmarking, and data-aware optimization are essential, to compare the Amazon Nova models performance against the model used before the migration, and optimize the prompts on Amazon Nova to align performance with that of the previous workload or improve upon them.

Metrics 81
article thumbnail

Activating Intent Data for Sales and Marketing

Sales and marketing leaders have reached a tipping point when it comes to using intent data — and they’re not looking back. More than half of all B2B marketers are already using intent data to increase sales, and Gartner predicts this figure will grow to 70 percent. Intent data can be overwhelming if you don’t know how to use it.

article thumbnail

ABCs of Data Normalization for B2B Marketers

Data normalization. However, if lead generation, reporting, and measuring ROI is important to your marketing team, then data normalization matters - a lot. At its core, data normalization is the process of creating context within your marketing database by grouping similar values into one common value. Why is this so essential?

article thumbnail

The 2019 Technographic Data Report for B2B Sales Organizations

In this report, ZoomInfo substantiates the assertion that technographic data is a vital resource for sales teams. reporting that technographic data is either somewhat important or very important to their organization. In fact, the majority of respondents agree—with 72.3%

article thumbnail

Digitizing Logistics: Harness the Power of Data in 4 Steps

That’s where your data comes in. In demand generation, data is essential for knowing who you should target and how. In this eBook, you’ll learn how to identify and target your ideal prospects — when they’re most receptive to hearing your message — using different types of data. Leveraging intent data.

article thumbnail

Buyer’s Checklist: How to Evaluate a B2B Contact Data Provider

Leveraging a data provider to help identify and connect with qualified prospects supports company revenue goals by alleviating common headaches associated with prospecting research and empowers sales productivity. Download ZoomInfo’s data-driven eBook for guidance on effectively assessing the vendor marketplace. So what’s the problem?

article thumbnail

Why B2B Contact and Account Data Management Is Critical to Your ROI

64% of successful data-driven marketers say improving data quality is the most challenging obstacle to achieving success. The digital age has brought about increased investment in data quality solutions. However, investing in new technology isn’t always easy, and commonly, it’s difficult to show the ROI of data quality efforts.

article thumbnail

The Time-Saving Power of Intent Data for Sales

By using the power of intent data, capturing buyer interest has become more feasible for sales. Read on to learn more about how intent data can save salespeople time -- while capturing more qualified leads in the process! Not only that, but using it will save immense time during your workflow; a win-win on all fronts.

article thumbnail

The Forrester Wave™: B2B Marketing Data Providers, Q2 2021

In our 24-criterion evaluation of B2B marketing data providers, we identified the 11 most significant vendors — Data Axle, Dun & Bradstreet, Enlyft, Global Database, InsideView, Leadspace, Oracle, SMARTe, Spiceworks Ziff Davis, TechTarget, and ZoomInfo Technologies — and researched, analyzed, and scored them.