Scalable machine learning is a major buzzword in the machine learning industry, partly because getting machine learning processes to scale is an important and challenging aspect of many machine learning projects.
For example, some smaller machine learning projects may not need to scale as much, but when engineers are contemplating various kinds of productive modeling, trying to drive analysis of gigantic sets of data, or trying to apply machine learning to different hardware environments, scalability can mean everything.
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Scalable machine learning is important when it's clear that the scope of the project will outpace the original setup. Different algorithm approaches may be needed to help machine learning processes match other data analytics processes. Machine learning may require more resources for the same set of data.
In terms of the tools that are used, Apache Hadoop is often used for extremely large data sets, for instance, about 5 TB. Below this mark, there are other mid-level tools that may do the job well, such as Pandas, Matlab and R. IT professionals will match the tools to the needed level of scalability. They'll understand how much work machine learning programs need to do, and how they have to be outfitted to achieve those goals.
Along with the ability to scale to much larger sets of data on the order of several terabytes, another challenge with scalable machine learning is developing a system that can work across multiple nodes. Some basic machine learning systems may only be set up to run on an individual computer or hardware component. But when machine learning processes have to interact with multiple nodes, that will require a different approach. Getting machine learning to work in a distributed architecture is another major part of scalable machine learning. Consider a situation where machine learning algorithms have to access data from dozens or even hundreds of servers – this is going to require significant scalability and versatility.
Another driver of scalable machine learning is the process of deep learning, where engineers and stakeholders may get more results from going deeper into data sets and manipulating them in more profound ways. Deep learning projects are an excellent example of how companies may need to adopt scalable machine learning strategy to achieve the capability that they need. As deep learning continues to evolve, it will place pressure on machine learning systems to scale more efficiently.