Machine learning was once something that only the largest companies could leverage effectively. That is changing. New tools that democratize machine learning are now available. This enables ordinary organizations to leverage their data for designing machine learning applications.
The concept of machine learning is not new. Since the early decades of computing, developers have experimented with strategies that allow programs to learn or make informed decisions.
Until recently, only very large organizations had the data management capabilities to leverage machine learning effectively. With few open source frameworks available for machine learning, developers had to write algorithms from scratch to teach computers how to learn from data. Integrating, storing and analyzing large volumes of data was difficult because few tools existed to automate the process.
As a result, machine learning was only possible for organizations that could dedicate programmers and data scientists to building complex machine learning frameworks from scratch. Companies like Google, Netflix (which uses machine learning to make recommendations about the shows people want to watch), and Amazon (which makes product recommendations using machine learning) were among the few whose resources allowed them to take advantage of machine learning.
Machine Learning for the Masses
This is no longer the case. New tools and technologies are enabling companies of all sizes to begin experimenting with machine learning. (In fact, it was a hot topic at this year’s Strata Data Conference)
Those technologies include open source data analytics platforms, such as Apache Spark and Hadoop. Anyone can leverage tools like these to drive machine learning algorithms within applications. They obviate the need to build analytics engines from scratch.
At the same time, open source machine learning libraries, such as TensorFlow and Torch, make it easier to write the algorithms that enable machine learning. It still takes some know-how to add machine learning to an application, but these frameworks make it much easier to do so.
Effective Data Management
Another piece of the machine learning democratization puzzle is better data management techniques.
Machine learning algorithms are only as good as the data that drives them. And that is only useful when it is free of errors and able to be moved or manipulated whenever needed.
Effective data management provides both qualities. Data management processes help ensure that data stored in any format or structure can be translated into another one. This is important when, for example, you have mainframe data that you want to analyze with Hadoop. Without a good data management solution, you’d have to move your mainframe data via manual processes into Hadoop. Not only would that take a long time, it is not reasonable to do on a large scale.
In addition, good data management techniques help to ensure data quality. They allow organizations to find and fix the errors, inconsistencies, redundancies and other problems that exist within data sets. In effect, data quality tools purify data, ensuring that the information that powers machine learning algorithms is clean and effective. (Related: Alation’s CEO Sangani Discusses Big Data Management Trends and Best Practices)
For now, machine learning at most companies is still in the experimental stages. overall, demand for production machine learning applications is generally limited to very large companies with unique use cases.
Even so, it is now much easier for ordinary organizations to experiment with machine learning and make plans for including it in their solutions sets. This opportunity will grow as machine learning libraries and analytics tools evolve.
As noted above, machine learning will only work well if it is paired with effective data management. On this front, Syncsort can help. Syncsort’s data management tools include Big Data solutions for accessing and translating data between different formats to integrate it into Hadoop and Spark. In addition, Syncsort now offers data quality tools for improving the consistency and accuracy of data sets.
Read our free eBook, Mainframe Meets Machine Learning, to learn the challenges and issues facing mainframes today, and how the benefits of machine learning could help alleviate some of these issues.