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Chat with Devs | Episode - 4 | Isitio Ambient Mesh

Istio Ambient Mesh is a massive overhaul of Istio's data plane architecture. The goal is to simplify operations and make it more cost effective to run a service mesh like Istio in production. In this video we will look at: 1] What are the challenges of a traditional service mesh sidecar approach? 2] Take a look at how Istio Ambient Mesh Solves that problem.

Best LLM Inference Engines and Servers to Deploy LLMs in Production

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.

Creating an Effective Test Plan Template

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.

Case Management Studio

From service requests to employee onboarding to regulatory compliance, case management processes are a critical part of business operations. And in a digital-first world, dated methods for managing casework simply can’t keep up.Appian Case Management Studio provides a modular approach to case management that covers 80% of typical casework needs out of the box. Watch the demo to learn how you can quickly build flexible, enterprise-grade apps for managing casework with Appian Case Management Studio.

Data Provenance vs. Data Lineage: Key Differences

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.