Amazon OpenSearch is a popular fully managed analytics engine that makes it easier for customers to do interactive log analytics, real-time application monitoring, and semantic and keyword searches. It can also be used as a vector engine that helps organizations build and augment GenAI applications without managing infrastructure (we’ll talk about this in future blogs). Additionally, the service provides a reliable, scalable infrastructure designed to handle massive data volumes.

LLM evaluation is the process of assessing the performance and capabilities of LLMs. This helps determine how well the model understands and generates language, ensuring that it meets the specific needs of applications. There are multiple ways to perform LLM evaluation, each with different advantages. In this blog post, we explain the role of LLM evaluation in AI lifecycles and the different types of LLM evaluation methods. In the end, we show a demo of a chatbot that was developed with crowdsourcing.

Making operational decisions in a tight timeframe is critical to the success of an organization. Real-time data ingestion enables quicker data availability, in turn enabling timely decision-making. Real-time ingestion is foundational to our digital transformation at Discover Financial Services. As a senior manager leading the streaming and real-time data platforms at Discover, I don’t want to be in the data replication business manually.

In this post we are going to look at the importance of volume testing. We are going to consider how this type of non-functional test can have a significant impact on your size and scale your production environment based on evidence this test provides.

When embarking on a software development project, a well-structured test plan is essential for ensuring the quality and reliability of the final product. A comprehensive test plan outlines the scope, approach, resources, and schedule for testing activities, serving as a blueprint for the testing process. In this blog post, we will delve into the key elements necessary to create a robust test plan template for software testing.

Two related concepts often come up when data teams work on data governance: data provenance and data lineage. While they may seem similar at first glance, there are fundamental differences between the two concepts. Data provenance covers the origin and history of data, including its creation and modifications. On the other hand, data lineage tracks the data’s journey through various systems and processes, highlighting its flow and transformation across different data pipeline stages.