Machine Learning


Predicting and Preventing Crime with Machine Learning - Part 2

In the first part of this blog series, we presented a use case on how machine learning can help to improve police operations. The use case demonstrates how operational planning can be optimized by means of machine learning techniques using a crime dataset of Chicago. However, this isn’t the only way to predict and prevent crime. Our next example takes us to London to have a look at what NCCGroup’s Paul McDonough and Shashank Raina have worked on.


Kafka Data Pipelines for Machine Learning Enterprise Applications

Traditional enterprise application platforms are usually built with Java Enterprise technologies and this is the case as well for OpsRamp. However, in machine learning (ML) world, Python is the most commonly used language, with Java rarely used. To develop ML components within enterprise platforms, such as the AIOps capabilities in OpsRamp, we have to run ML components as Python microservices and they communicate with Java microservices in the platform.


4 Ways Australia's Job Market Will Change in 2019 Thanks to AI and ML

2018 was the year that saw Australia break out of its hesitation and slowly change its concerned attitudes towards new technologies. Artificial intelligence (AI) was adopted in numerous ways, from the protection of our native flora and fauna to the observation of Australia’s ageing bridges, which used AI and high-tech sensors to update engineers in real time on a bridge’s coping load.


How to Gather Data for Machine Learning

Unless you’ve been living in a cave these last few months (a cave that somehow carries sufficient WiFi coverage to reach our blog), you’ll doubtless have heard about machine learning. If you’re a developer, chances are you’re intrigued. The machine learning algorithm, which solves problems without requiring detailed instructions, is one of the most exciting technologies on the planet.


How Siemplify Uses Machine Learning to Drive SOC Efficiency

The promise of machine learning in cybersecurity, specifically inside the security operations center, is vast, but let’s not get ahead of ourselves. Machine learning can’t solve all your problems. Yet if you’re using the Siemplify Security Operations Platform, machine learning is playing an increasingly prominent role.


Distributed Machine Learning With PySpark

Spark is known as a fast general-purpose cluster-computing framework for processing big data. In this post, we’re going to cover how Spark works under the hood and the things you need to know to be able to effectively perform distributing machine learning using PySpark. The post assumes basic familiarity with Python and the concepts of machine learning like regression, gradient descent, etc.


Stop shackling your data-scientists: tap into the dark side of ML / AI models

Developing Artificial Intelligence and Machine Learning models comes with many challenges. One of those challenges is understanding why a model acts in a certain way. What’s really happening behind its ‘decision-making’ process? What causes unforeseen behavior in a model? To offer a suitable solution we must first understand the problem. Is it a bug in the code? A structural error within the model itself? Or, perhaps it’s a biased dataset?


Part 4: How machine learning, AI and automation could break the BI adoption barrier

In the last three parts of this four-part series, we have looked at: research on the state of analytics today and the lack of BI adoption; the history of BI and how we have arrived at the augmented era; and the four main blockers to BI adoption that is stunting the growth your business data culture. Today, let's take a look at how AI and machine learning (ML) can close that adoption gap.