Imagine transforming raw business data into actionable insights with minimal effort. This is the value proposition of automated analytics, a form of data analytics fast becoming more accessible among modern business intelligence (BI) and analytics software solution vendors. Independent software vendors (ISVs) and enterprise organizations at the cusp of investing in analytics struggle with manual data processes that are time-consuming and prone to errors.

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.

Electronic Data Interchange (EDI) is a popular communication method that enterprises use to exchange information accurately and quickly with trading partners. EDI transmits data almost instantaneously — serving as a fast and efficient mode for exchanging business documents. ANSI X12 vs. EDIFACT are the two most common EDI standards used for EDI, but they have different structures, styles, and usage.

Django is one of the leading Python frameworks used to create full-stack web applications. In this comprehensive guide, you will explore the intricacies of testing and debugging within the Django framework, focusing on advanced methodologies and essential tools. Beginning with the fundamentals of Django unit and integration testing, you will delve into advanced techniques such as mocking, testing middleware, and profiling for optimal performance.

Ruby on Rails has long been celebrated for its ‘convention over configuration’ philosophy, simplifying web development for countless programmers. However, what if you’ve started with a lean Rails API-only application and now find yourself needing a front-end? This isn’t uncommon, especially with the rise of JavaScript frameworks and SPAs.

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.