Like many cloud providers, IBM is invested and interested in developing and building out their machine learning offerings. Because more and more companies are interested in trying to figure out what machine learning can currently do and what it is on the cusp of doing, there has been a plethora of new services and companies trying to see if they can either do anything new or be better than the many other competitors in the market.
While much of the machine learning world is based around cloud computing and the ability to horizontally scale during training, the reality is that not every company is cloud based. While IBM has had many machine learning tools and services available on their various cloud platforms, the IBM Machine Learning system seems to be just an on-premise compromise to many of the previously cloud-based APIs that IBM offered under their machine learning and Watson brand. While I am sure many enterprise-level companies have a large amount of data that would previously not have been able to use these services, I’m unaware whether and skeptical that these services would be any better or more utilitarian than their cloud counterparts. This seems like it may be particularly useful to companies in industries with many regulations or worries about hosting data outside of their private network, although this mentality seems like it is being slowly eroded away and becoming outdated in many senses.
One of the great things about the IBM Machine Learning system (and many similar companies as well) are the APIs that allow developers to pick whatever language they would like and allowing multiple frameworks because there are so many available and interesting options at the moment. This is a really important aspect for something like deep learning, where there is a constant barrage of new architectures and ideas that iterate upon prior ideas, but require new architecture and developer implementations.
While I have not used IBM’s new system and will most likely not be in any particular situation where it would be available, I was lucky enough to participate in IBM’s Catalyst program and used a lot of their cloud services for a deep learning project and testing out many of their available Bluemix offerings. While I thought the system was incredibly nice and simple to use compared to many other cloud providers, I found the various machine learning APIs they offered either weren’t that useful or seemed to provide worse results than their comparable Google, Azure, and other such services. Perhaps that has changed, or their new services are much better and will be available on this new system, but it is hard to find any definitive information that this is true.
One aspect of these machine learning tools that I am not excited about is the constant focus on using machine learning to create some business cost-savings models (which the IBM Machine Learning press release touts), which companies may state is passed onto the customers, but it seems like this is rarely the truth (and one of the things that was stressed on in the IBM Machine Learning launch event). The ability for machine learning methodology to solve tedious and previously complicated problems is much more important and fascinating to me than simply saving money via optimizing business expenses. While many of these problems are much harder to solve and we are far from solving them, the current applications of machine learning in business and marketing areas often provides proof for the rhetoric that machine learning is exploitive and a toxic solution. Along with this, while the IBM Machine Learning and Data Science products may seem to be aimed at someone in a data scientist role, I can never help but wonder to what extent data scientists are actually using these tools outside of pure novelty or an extremely simple prototyping step in a more in-depth analytical pipeline.
I personally think the ability of these tools to create usefulness for someone who may otherwise not be interested in many aspects of traditional data science is where the tools are incredibly powerful and useful. While not all traditional developers or analysts are interested in in-depth data exploration, creating pipelines, and many other traditional data science skills, the ability to do so at ease and without having to learn skills and tooling can be seen as a huge burden to those not particularly interested.
While true machine learning and data science skills are unlikely to be completely replaceable, many of the skills that a traditional data scientist has will be less important as more people are capable of doing what previously may have been a quite technical or complicated process. These sorts of products and services that are a part of the IBM Machine Learning catalog reinforce that idea and are an important step in allowing services to be used regardless of data location or analytical background, and are an important step forward for machine learning and data science in general.
About the author
Graham Annett is an NLP engineer at Kip (Kipthis.com). He has been interested in deep learning and has worked with and contributed to Keras. He can be found on GitHub or on his website.
