AI applications that produce human-like text, such as chatbots, virtual assistants, language translation, text generation, and more, are built on top of Large Language Models (LLMs). If you are deploying LLMs in production-grade applications, you might have faced some of the performance challenges with running these models. You might have also considered optimizing your deployment with an LLM inference engine or server.

Local governments aim to serve the needs of their communities by fostering a safe, prosperous, and vibrant environment where residents and businesses can thrive. But too often, inefficient processes and manual, paper-based tasks consume valuable time and resources. This leads to delays, errors, and inconsistencies in the delivery of essential services, eroding citizen satisfaction and trust.

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.

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.

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.

With integration to Vertex AI, you can use multimodal LLMs like Gemini Pro 1.0 and Gemini Vision Pro 1.0 in BigQuery to create marketing campaigns.

Data observability is a process that actively monitors an organization’s data for accuracy, health, and usefulness. It is the ability of an organization to have comprehensive visibility over its entire data landscape, including data pipelines, infrastructure, and applications. Data observability allows the organization to quickly identify, control, prevent, remediate, and fix data outages, all within agreed service level agreements (SLAs).

Earlier today, Qlik announced its expanded role as a Snowflake Cortex launch partner, and this article explains how the two solutions work together to benefit our customers. Let’s begin by quickly reviewing Snowflake Cortex.

As a web developer, you will often need to send emails from your application. In fact, if you've been a developer for more than a few months, you've probably already had to do this. You might want to send emails for a variety of reasons, such as sending a welcome email to new users, sending a password reset email, or sending a notification to a user. Laravel makes it easy to send these types of emails to single recipients, thanks to some handy features such as mailables and notifications.

Exploratory testing is an essential tool in any comprehensive software testing strategy. If regular scripted testing follows clear directions on a map, exploratory testing ventures off the beaten path to uncover hidden defects. This guide will teach you what exploratory testing is, why it’s critical, how to conduct exploratory testing sessions, and techniques to maximize your results.