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Fortify Cyber Defense With AI

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Organizations' attack surfaces are exponentially expanding, contributing to an unprecedented growth in cybersecurity risks. The internet of things, 5G, Wi-Fi 6, and other networking advances are driving an increase in network-connected devices that can be exploited by cybercriminals. For many employees, remote work is expected to remain the rule, not the exception, providing cybercriminals with many new opportunities. And as more organizations integrate data with third-party applications, APIs are a growing area of security concern. Expanding attack surfaces and the escalating severity and complexity of cyberthreats are exacerbated by a chronic shortage of cybersecurity talent.


How SASE uses AI

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Secure access service edge, or SASE, combines networking and security into a cloud-based service, and it's growing fast. According to Gartner projections, enterprise spending on SASE will hit almost $7 billion this year, up from under $5 billion in 2021. Gartner also predicts that more than 50% of organizations will have strategies to adopt SASE by 2025, up from less than 5% in 2020. The five core components of the SASE stack are SD-WAN, firewall-as-a-service (FWaaS), secure web gateway (SWG), cloud access security broker (CASB), and zero trust network access (ZTNA). "It's something that most, if not all, SASE vendors are working on," says Gartner analyst Joe Skorupa.


Forgeahead Solutions

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"DevOps is not a goal but a never-ending process of continual improvement." The pace of software releases has increased manifold. At that pace though, an intriguing collaboration seems in the offing between Artificial Intelligence (AI) and DevOps with the potential to further streamline operations and enhance performance. A Gitlab survey of 4,300 developers revealed that 75% of them are using AI, machine learning (ML), or automated bots to test and review their code before release. Can AI add more power to DevOps?


Are These Data 'Normal'? Anomalies & Outliers In Machine Learning

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Nobody wants outliers in their data -- especially when they have come from the likes of false entries due to fat thumbs. A couple of zeros can throw off an algorithm and can destroy summary statistics. So this is how you use machine learning to remove those pesky outliers. Historically, the first step to anomaly detection is to try and understand what's "normal", and then find examples of "not normal". These "not normal" points are what we would classify as outliers -- they didn't fit our expected distribution even at the furthest ends of it.


101 Machine Learning Algorithms for Data Science with Cheat Sheets

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These 101 algorithms are equipped with cheat sheets, tutorials, and explanations. Think of this as the one-stop shop/dictionary/directory for machine learning algorithms. The algorithms have been sorted into 9 groups: Anomaly Detection, Association Rule Learning, Classification, Clustering, Dimensional Reduction, Ensemble, Neural Networks, Regression, Regularization. In this post, you'll find 101 machine learning algorithms with useful Python tutorials, R tutorials, and cheat sheets from Microsoft Azure ML, SAS, and Scikit-Learn to help you know when to use each one (if available). At Data Science Dojo, our mission is to make data science (machine learning in this case) available to everyone.


Splunk : Getting Started with Machine Learning at Splunk

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I'm sure many of you have heard of our Machine Learning Toolkit (MLTK) app and may even have played around with it. Some of you might actually have production workloads that rely on MLTK without being aware of it, such as predictive analytics in Splunk IT Service Intelligence (ITSI) or MLTK searches in Splunk Enterprise Security. A recurring theme during my time at Splunk - and something we often hear from colleagues who don't work directly with MLTK - is that people are unsure where to start with machine learning (ML). Here I'd like to take you through some of the concepts and resources that you might need to get familiar with to use MLTK in your Splunk instance. I'll also highlight some of the new content we're working on to help you get more insight from your data using ML.


The Promise of AI in Gene and Cell Therapy Operations

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There is no longer any doubt that artificial intelligence (AI) is advancing biological discovery and biomanufacturing operations. In biological discovery, AI systems such as AlphaFold and the Atomic Rotationally Equivariant Scorer are celebrated for their uncanny ability to predict tertiary structures for proteins and RNA molecules. In biomanufacturing, AI systems usually enjoy less fanfare. Yet they can provide valuable functions such as pattern recognition, real-time assessment of batch quality, multivariable control for continuous manufacturing, prediction/optimization of critical process parameters, and anomaly detection. Such functions are critical to the success of gene and cell therapy operations.


What is an Isolation Forest? And How Does it Detect Outliers?

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Isolation Forest is a simple yet incredible unsupervised algorithm that is able to spot outliers or anomalies in a data set very quickly. I should say understanding this tool is a must for any aspiring data scientist. In this article, I will briefly go through the theories behind the algorithm and also its implementation. Its Python implementation from Scitkit Learn has been gaining tons of popularity due to its capabilities and ease of use. But before we jump right into the implementation, it's always best practice for us to study about its use cases and the theory behind it.


Anomaly/Outlier Detection using Local Outlier Factors - DataScienceCentral.com

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Outliers are patterns in data that do not confirm to the expected behavior. While detecting such patterns are of prime importance in Credit Card Fraud, Stock Trading etc. Detecting anomaly or outlier observations are also of importance when training any of the supervised machine learning models. This brings us to two very important questions: concept of a local outlier, and why a local outlier? In a multivariate dataset where the rows are generated independently from a probability distribution, only using centroid of the data might not alone be sufficient to tag all the outliers. Measures like Mahalanobis distance might be able to identify extreme observations but won't be able to label all possible outlier observations.


DSC Webinar Series: No-code ML for Forecasting and Anomaly Detection - DataScienceCentral.com

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In this latest Data Science Central webinar, we will introduce and demonstrate how you can perform common time-series Machine Learning tasks such as Forecasting and Anomaly Detection, directly within the Influx platform without the need to use external tools, languages and services During this webinar, you will learn: How to initiate Machine Learning tasks directly… Read More »DSC Webinar Series: No-code ML for Forecasting and Anomaly Detection