It wasn’t too long ago that you needed to put on a white lab coat to work with artificial intelligence. The science was arcane, complex, and something that very few human intelligences could grok.That’s changed. The scientists in their lab coats recognized the power of distributing software as a service and they bundled together their code and turned it into an API that anyone could use. Just post your data to the service and artificial genius comes back in a few milliseconds. Well, it could take longer if you’ve got a big data set.What does that artificial intelligence do with your data set behind the curtain? You won’t need to pay much attention. That’s the point of software as a service. Data goes in. Genius comes out.Okay, that’s exaggerating the progress. You may not need to understand all of the math that’s deep inside the AI code and you may not need to feel completely comfortable with “tensor this” and “vector … [Read more...] about 10 machine learning APIs developers will love
As I discussed in “What is Julia?,” Julia is a free open source high-level, high-performance dynamic programming language for numerical computing that combines the development convenience of a dynamic language with the performance of a compiled statically typed language. It was designed to be good for scientific computing, machine learning, data mining, large-scale linear algebra, distributed computing, and parallel computing, and to have the ease of use of Python, R or even Matlab.There are five major options for working with Julia: JuliaBox online; an installation of the Julia command line; an installation of JuliaPro; Visual Studio Code plus a plug-in and a Julia or JuliaPro installation; and Jupyter notebooks with IJulia. Let’s consider the pros and cons of each.JuliaBox online requires no installation or maintenance, and you can use a free account to get started. It is set up for Jupyter notebooks, has more than 300 packages already added, and has … [Read more...] about Julia tutorial: Get started with the Julia language
A distributed file system, a MapReduce programming framework, and an extended family of tools for processing huge data sets on large clusters of commodity hardware, Hadoop has been synonymous with “big data” for more than a decade. But no technology can hold the spotlight forever.While Hadoop remains an essential part of the big data platforms, and the major Hadoop vendors—namely Cloudera, Hortonworks, and MapR—have changed their platforms dramatically. Once-peripheral projects like Apache Spark and Apache Kafka have become the new stars, and the focus has turned to other ways to drill into data and extract insight. Let’s take a brief tour of the three leading big data platforms, what each adds to the mix of Hadoop technologies to set it apart, and how they are evolving to embrace a new era of containers, Kubernetes, machine learning, and deep learning.Cloudera Enterprise Data HubCloudera was the first to market with a Hadoop distribution—not … [Read more...] about 3 big data platforms look beyond Hadoop
In the previous article, I’ve described why financial services companies should care about machine learning and what use cases they should pay attention to. Now, it’s time to see how exactly financial businesses can adopt this technology to make it a success.The problem is, in spite of all the advantages of AI and machine learning, even companies with deep pockets often have a hard time extracting the real value from this technology. Financial services incumbents want to exploit the unique opportunities of machine learning but, realistically, they have a vague idea of how data science works, and how to use it.Time and again, they encounter similar challenges like the lack of business KPIs. This, in turn, results in unrealistic estimates and drains budgets. It is not enough to have a suitable software infrastructure in place (although that would be a good start). It takes a clear vision, solid technical talent, and determination to deliver a … [Read more...] about How to make use of machine learning in finance?
Machine learning, a subset of artificial intelligence, is the practice of using algorithms and large data sets or Big Data to develop insights ranging from which movie a Netflix user may want to watch next to recommendations about cybersecurity incident handling.According to consulting firm McKinsey, “the unmanageable volume and complexity of the big data that the world is now swimming in have increased the potential of machine learning—and the need for it.”For security professionals, machine learning capabilities can increase responder productivity and enable leaner, more efficient security operations. Humans however, not machines, must direct and guide machine learning algorithms to achieve the business goals and objectives that the computers are given.Machine Learning, Big Data, and SecurityThe best way to understand how machine learning can be beneficial for security analysts is to perhaps look at another field with similar operational efficiency goals that is … [Read more...] about Machine learning: The perfect partner for security analysts