In the previous article in this Machine Learning series, we looked at the Apache® Ignite™ Machine Learning Grid. Now let’s take the opportunity to drill-down further into some of the Machine Learning algorithms that are supported in Apache Ignite and try out some examples using popular datasets.
If we search for suitable datasets to use, we can find many that are available. However, one dataset…
The GridGain Systems In-Memory Computing Blog
In a previous article, we discussed the Apache® Ignite™ Machine Learning Grid. At that time, a beta release was available. Subsequently, in version 2.4, Machine Learning became Generally Available. Since the 2.4 release, more improvements and developments have been added, including support for Partitioned-Based Datasets and Genetic Algorithms. Many of the Machine Learning examples that are…
In case you hadn’t noticed, this year’s annual Spark conference is, for the first time, the Spark+AI Summit. The fact that Spark and AI should be together is predictable even without… using AI to figure it out. But there’s only one way to add continuous learning to Spark+AI, to make AI learn and adapt to new information in near real-time like a person. It is not the AllSpark, which is used to…
The GridGain in-memory computing platform has always been famous for its ability to be deployed and managed in heterogeneous environments. It doesn’t matter if you’d like GridGain to work on-premise or to operate in the cloud; to scale out across commodity servers or scale up within powerful mainframes. And if need to get GridGain provisioned by Kubernetes or Docker Swarm -- you get…
These are very exciting times for Apache Ignite. During this past year that I have been with GridGain, I have seen some significant technology additions to the Open Source project, such as support for SQL-99, Native Persistence, and Machine Learning to name but three. Earlier this year, new Genetic Algorithm (GA) code was donated to the Apache Software Foundation. Since I am not very familiar…
How to Add Speed and Scalability to Existing Applications with In-Memory Data Grids.
If you want to build a basement fix its foundation for future construction, jack it up. It’s much cheaper, faster and less disruptive than building a new house.
The same is true for applications. If you want to add speed, scalability and flexibility to your existing applications, slide an in-memory…
If you’re not interested in John Cleese, just listen to Akmal Chaudhri explain how machine and deep learning work with Apache Ignite. But if you really want to understand the problem before diving into the details, I recommend you learn from John Cleese.
Many years ago, long before machine learning but long after Lisp was invented, John Cleese made a big impression on me at a conference. …
As you may have noticed, we’ve started a new series about “The New Digital Experience." It’s meant to share the best practices companies adopted to improve the customer experience and transform into a digital business, with a particular focus on the use of in-memory computing with other technologies.
One of the most important architectural concepts that companies need to understand is why in-…
How Digital Business, Big Data, HTAP and In-Memory Computing Came Together to Improve the Customer Experience:
I’ve had two long-standing professional interests for half my life; middleware and customer experience management. I am happy to say that not only is there a focus at the executive level on improving the customer experience. The technologies needed have evolved to a point that…
Comparing Apache Ignite / GridGain and Apache Cassandra / DataStax as the Power Behind the Moment
If you’re in the process of a digital transformation and trying to improve the customer experience to compete against the Amazon, PayPal, Uber, Expedia, Netflix in your industry, you need to understand something before you fix your next performance and scalability bottleneck. There is only one way…