The AI Project Cycle is a cycle/order of an AI Project which defines every step an organization must take to harness/get value (Monetary or others) from that AI Project to get more ROI (Return on Investment). You might have seen AI Project Cycle images Starting from'Problem Scoping', ignoring'Problem Identification', But in this article we will discuss about the one with'Problem Identification' which is a more accurate representation. In Today's Article, we will discuss the various stages of the AI Project Cycle, starting with Problem Identification, followed by Problem Scoping, Data Acquisition, Data Exploration, Data Modelling, Evaluation and finally Deployment. You may think that the Tip of the Iceberg is the problem, but in most cases, it's not. In many cases, the problems are not obvious, the problem may look small, but digging deep and down into the problem, we will realize that the problem has a lot to it, and that the beginning is nothing.

The AI Project Cycle is a cycle/order of an AI Project which defines every step an organization must take to harness/get value (Monetary or others) from that AI Project to get more ROI (Return on Investment). You might have seen AI Project Cycle images Starting from'Problem Scoping', ignoring'Problem Identification', But in this article we will discuss about the one with'Problem Identification' which is a more accurate representation. In Today's Article, we will discuss the various stages of the AI Project Cycle, starting with Problem Identification, followed by Problem Scoping, Data Acquisition, Data Exploration, Data Modelling, Evaluation and finally Deployment. You may think that the Tip of the Iceberg is the problem, but in most cases, it's not. In many cases, the problems are not obvious, the problem may look small, but digging deep and down into the problem, we will realize that the problem has a lot to it, and that the beginning is nothing.

APM (Application Performance Management) is on the precipice of dramatic change. The complexity of cloud native and new stack applications is making many of the traditional ways of monitoring applications irrelevant. With applications which can elastically scale using containers to meet unprecedented amounts of demand, the APM industry needs to reconsider the focus on simple problem identification to provide value for businesses. Understanding how application performance visibility can provide better data as to what should get scaled and what does not need to get scaled is just as important to the business. Managing complexity is the challenge of the next generation of applications.