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There are many articles on applying machine learning for log analysis. However, most of them are dated, academic in nature, or don’t focus on practical outcomes. On DZone, the last time an article covering how ML can be used for log analysis was published 5 years ago.
In this article, we want to share our real-life experience on using ML/AI for log analysis and anomaly detection with the specific purpose of automatically uncovering the root cause of software issues.
Today, most companies are using Python for AI and Machine Learning. With predictive analytics and pattern recognition becoming more popular than ever, Python development services are a priority for high-scale enterprises and startups. Python developers are in high-demand — mostly because of what can be achieved with the language. AI programming languages need to be powerful, scalable, and readable. Python code delivers on all three.
While there are other technology stacks available for AI-based projects, Python has turned out to be the best programming language for this purpose. It offers great libraries and frameworks for AI and Machine Learning (ML), as well as computational capabilities, statistical calculations, scientific computing, and much more.
"Many organizations claim that their business decisions are data-driven. But they often use the term "data-driven" to mean reporting key performance metrics based on historical data — and using analysis of these metrics to support and justify business decisions that will, hopefully, lead to desired business outcomes. While this is a good start, it is no longer enough".
The traditional role of data and analytics has always been in supporting decision-making. Now, they are applied where they have never been before. Today data and analytics are not only used for describing, diagnosing, predicting, or even recommending the best actions but also triggering those actions automatically. The motivation behind this new area of application is the goal of many businesses to reduce task performance time and the volume of human labor.
Deep Reinforcement Learning is a somewhat new field within Machine Learning or Artificial Intelligence (you may pick your favorite term between these two, even if they’re not strictly the same), which combines Deep Learning and Reinforcement Learning and is based on the general idea that an agent can learn by observing its actions and their consequences. No, it is not a return to John B. Watson and B. F. Skinner’s behavioral psychology. We are talking, instead, about a set of pretty advanced machine learning algorithms that, when properly mastered, allow computers to achieve remarkable results in some complex tasks. That’s what this book is about, so let’s dive in…
Foundations of Deep Reinforcement Learning - Theory and Practice in Python begins with a brief preliminary chapter, which serves to introduce a few concepts and terms that will be used throughout all the other chapters: agent, state, action, objective, reward, reinforcement, policy, value function, model, trajectory, transition.
Facebook and Twitter have left most other companies around the world far behind when it comes to using machine learning to improve their business model. And while their practices haven’t always resulted in the best reactions from end-users, there’s much to be learned from these companies on what to do–and what not to do–when it comes to scaling and applying data analytics.
While Facebook seemingly uses machine learning for everything — it is used for content detection and content integrity, sentiment analysis, speech recognition, and fraudulent account detection, as well as operating functions like facial recognition, language translation, and content search functions. The Facebook algorithm manages all this while offloading some computation to edge devices in order to reduce latency.
Artificial intelligence which gives machines the ability to think and behave like humans are gaining traction since the last decade. These features of artificial intelligence are only there because of its ability to predict certain things accurately, these predictions are based upon one certain technology which we know as machine learning (ML). Machine learning as the name suggests is the computer’s ability to learn new things and improve its functionality over time. The main focus of machine learning is on the development of computer programs that are capable of accessing data and using it to learn for themselves.
To implement machine learning algorithms, two programming languages, R and Python for machine learning are normally used. Generally, selecting features for training data on machine learning in python is a very complex and technical process. But here we will go over some basic techniques and details regarding what is machine learning and how it works. So, let us start by going into detail regarding what ML is, what feature selection is and how can one select feature using python.
While I was referring to the following articles to learn the basics, I realized that I could add a few more things so that the community and developers like me can benefit
https://www.analyticsvidhya.com/blog/2017/12/introduction-to-recurrent-neural-networks/
Fraud Prevention isn’t everyone’s cup of tea. By the time financial institutions catch up with the latest criminal tactic, fraudsters come up with a new one to take its place. Because of this obligation to constantly upgrade against scammers, it is always an ongoing challenge for financial institutes to stay neck and neck with criminals.
At the same time, the finance sector is spending considerable budget, time, and effort to develop or adopt more advanced technologies for fraud prevention. However, one thing they may be lacking is the technology that could adapt and change as hastily as fraud tactics.
Walking by the hottest IT streets in these days means you've likely heard about achieving Streaming Machine Learning, i.e. moving AI towards streaming scenario and exploiting the real-time capabilities along with new Artificial Intelligence techniques. Moreover, you will also notice the lack of research related to this topic, despite the growing interest in it.
If we try to investigate it a little bit deeper then, we realize that a step is missing: nowadays, well-known streaming applications still don't get the concept of Model Serving properly, and industries still lean on lambda architecture in order to achieve the goal. Suppose a bank has a concrete frequently updated batch trained Machine Learning model (e.g. an optimized Gradient Descent applied to past buffer overflow attack attempts) and it wants to deploy the model directly to their own canary.
Miklov et al. introduced the world to the power of word vectors by showing two main methods: Skip–Gram and Continuous Bag of Words (CBOW). Soon after, two more popular word embedding methods built on these methods were discovered. In this post, we’ll talk about GloVe and fastText, which are extremely popular word vector models in the NLP world.
Pennington et al. argue that the online scanning approach used by word2vec is suboptimal since it does not fully exploit the global statistical information regarding word co-occurrences.
The last decade has seen rapid advancements in machine learning techniques enabling automation and predictions in scales never imagined before. This further prompts researchers and engineers to conceive new applications for these beautiful techniques. It wasn’t long before machine learning techniques were used in reinforcing network security systems.
The most common risk to a network’s security is an intrusion such as brute force, denial of service, or even an infiltration from within a network. With the changing patterns in network behavior, it is necessary to switch to a dynamic approach to detect and prevent such intrusions. A lot of research has been devoted to this field, and there is a universal acceptance that static datasets do not capture traffic compositions and interventions.
SAE Institute from Australia is considered the front-runner in creative studies. They recently ran a survey to understand the general sentiment of students and identify key areas of improvement. This case study describes how ParallelDots carried out a detailed survey analysis to make sense of the student responses and derived specific insights from this data. 750 students responded to the survey, resulting in a total of 4500 comments.
Traditional methods of survey analysis suffer from multiple human biases and are intensive in terms of effort as well as time. Knowing this, the institute decided to outsource the project to ParallelDots.