Already well known for their exceedingly eclectic and downright COOL videos, the SUSE team applied their well-honed skills to capturing all of the presentations delivered in their booth on the show floor at SC17 last month in Denver. In our wrap-up post for SC17 we promised to follow up regarding these presentations; this is the third and final of those posts.
There’s never been a better time to dabble in Machine Learning.
More importantly, when your dabbling delivers tangible results, you’ll soon realize there’s never been a better time to be successful – successful beyond your wildest expectations through use of Deep Learning.
Why?
Simply stated, there are options presently available that can ensure your ongoing success – whether you opt for in-memory, distributed parallel computing via Apache Spark, or a framework such as TensorFlow that’s ideally suited to making use of one or many more GPUs.
And although we’d love to be there when you start dabbling, you’ll likely want us to be involved as you achieve your milestones on your path towards success. Again, why? Owing to your success, you might find yourself in contention with others who seek to make use of the same pool of shared resources. Or, you might have reached a point where you need to scale your use of resources up, out … or both!
The good news is that you don’t need to be a trailblazer, as others are already forging these paths … and this means adoption patterns are emerging. For an increasing number of enterprise customers, an emerging best practice is to employ SUSE Linux Enterprise Server (SLES) as the Linux operating environment for your containers, servers (VMs or physical), clusters and clouds. Because SLES ships with the Docker Engine enabled, your Deep Learning application and workflows can be implemented and scaled across CPUs and/or GPUs via integrated workload management technology based upon Univa Grid Engine.
Obviously, we’re just scraping the surface here in this post, in our webinar, and in the presentation, we recently made in the SUSE booth (see below). After you’ve reviewed the presentation and our slides, we hope you’ll realize that solutions from Univa and its partners are particularly appealing, enabling and available to assist you in taking your Machine Learning prototypes into Deep Learning production at scale.
While this may be our last word when it comes to the presentations we delivered in the SUSE booth during SC17, you can rest assured that there’ll be a whole lot more to share with respect to Deep Learning as we close out 2017, and have our sights set on the New Year.
