Machine Learning


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.


Configure Jupyter Notebook to Interact with Splunk Enterprise & the Splunk Machine Learning Toolkit

Ever wanted to manage and integrate your Splunk Enterprise deployment using your favorite data science tool? Then this blog's for you. But there are a couple things to keep in mind—this is for development and single instance deployments only, and it also requires sudo/root access to the server in order to properly map user PIDs and ownership of directories inside the Docker container to the host operating system.


Use Kubernetes to Speed Machine Learning Development

As industries shift to a microservices approach of deploying applications using containers, data scientists can reap the benefits. Data Scientists use specific frameworks and operating systems that can often conflict with the requirements of a production system. This has led to many clashes between IT and R&D departments. IT is not going to change the OS to meet the needs of a model that needs a specific framework that won’t run on RHEL 7.2.