 Maximizing Enterprise Data: Unleashing the Productive Power of AI With the Right Approach Aug 26th 2024, 20:00, by Pan Singh Dhoni In today's digital landscape, data has become the lifeblood of organizations, much like oil was in the industrial era. Yet, the genuine hurdle is converting data into meaningful insights that drive business success. With AI and generative AI revolutionizing data platforms, the critical question is: Are we ready to harness the transformative power of data to propel growth and innovation? The answer is a mixed bag. While we can derive some benefits from our data, unlocking its full potential requires a purposeful and multi-faceted approach grounded in several essential elements: |  DORA Metrics: Tracking and Observability With Jenkins, Prometheus, and Observe Aug 26th 2024, 19:00, by Bhargavi Gorantla DORA (DevOps Research and Assessment) metrics, developed by the DORA team have become a standard for measuring the efficiency and effectiveness of DevOps implementations. As organizations start to adopt DevOps practices to accelerate software delivery, tracking performance and reliability becomes critical. DORA metrics help organizations address these critical tasks by providing a framework for understanding how well teams are delivering software and how quickly they can recover from failures. This article will delve into DORA metrics, demonstrate how to track them using Jenkins, and explore how to use Prometheus for collecting and displaying these metrics in Observe. What Are DORA Metrics? DORA metrics are a set of four key performance indicators (KPIs) that help organizations evaluate their software delivery performance. These metrics are: |  Methodcentipede Aug 26th 2024, 18:00, by Anton Belyaev When I was a child, I used to lie on the bed and gaze for a long time at the patterns on an old Soviet rug, seeing animals and fantastical figures within them. Now, I more often look at code, but similar images still emerge in my mind. Like on the rug, these images form repetitive patterns. They can be either pleasing or repulsive. Today, I want to tell you about one such unpleasant pattern that can be found in programming.
![Donald duck with methodcentipede on board]() Scenario Imagine a service that processes a client registration request and sends an event about it to Kafka. In this article, I will show an implementation example that I consider an antipattern and suggest an improved version. |  Anomaly Detection: The Dark Horse of Fraud Detection Aug 26th 2024, 17:00, by Sumit Makashir Today, machine learning-based fraud prediction has become a mainstay in most organizations. The two common types of machine learning are supervised and unsupervised machine learning. Out of the two, supervised learning is the most desired choice for fraud prediction for apparent reasons. Supervised learning that learns the patterns from known fraud cases yields more accurate predictions. On the other hand, unsupervised learning can be leveraged even when we don't have confirmed cases of fraud. The drawback is that it has a lower level of prediction accuracy compared to supervised learning. Supervised ML Models Won't Know What We Don't Know Organizations today typically only implement supervised models. A common reason for this is the belief that if a supervised model can deliver the best performance, there is no need to have an unsupervised model. This school of thought could prove dangerous in some domains, fraud detection being one of them. Supervised models only learn what they are taught. They can't evolve on their own to capture the new fraud patterns. Fraudsters, conversely, are highly creative entities constantly attempting to figure out new ways of evading detection. |  Multi-Agent System's Architecture Aug 26th 2024, 16:00, by Mohammed Talib The distribution of decision-making and interaction among the various agents that make up the system principally distinguishes multi-agent systems from single-agent systems. In a single-agent system, a centralized agent makes all decisions, with other agents acting as remote slaves. It is customary for this one agent to decide depending on the circumstances. This can lead to the overlooking of alternative viewpoints and possibilities. On the other hand, multi-agent systems consist of several intelligent agents that interact with each other, each capable of making decisions and influencing the surrounding environment. The purpose of multi-agent architecture is to construct agents that are able to bring in multiple perspectives by virtue of the roles that they play. Different contexts facilitate the creation of these agents. Despite using the same LLM, each agent's behavior is unique due to its specific function, objective, and context, just like a squad member. | |
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