When machine learning was first introduced, it completely transformed how data was mined and analyzed. Organizations no longer had to dedicate a handful of resources to manually – or using statistical tools – unearth insights from data. They could just feed machine learning algorithms with relevant, consistent, and up-to-date data and get all the insight they needed about their operations, employees, customers, market, competition, and more.
While the traditional way of machine learning enabled organizations to become forward-thinking, data-driven organizations, problems started to emerge when data sets became too large. Handling such large-scale data was challenging due to the limited scalability of machine learning algorithms. As data size started to outpace the computational ability of algorithms, machine learning models failed to scale and accurately process data.
This gave rise to the concept of distributed machine learning. It was built to handle huge data sets and provide outcomes that are not only far more accurate but also precise concerning the requirements of computation – namely, memory, time, and communication needs.
So, what exactly is distributed machine learning?
Distributed machine learning – an introduction
Distributed machine learning has garnered considerable attention over the last few years, thanks to its ability to solve very specific problems and show meaningful results quickly. It uses multi-node algorithms and systems that are designed to improve performance, increase accuracy, and scale to large input data sizes. Because these algorithms can be fed with large-sized data, they significantly reduce learning error and are more effective than traditional algorithms.
Unlike traditional machine learning where data is centrally processed in a database, distributed machine learning distributes models onto many machines, enabling parallel processing of data while improving the accuracy of every analysis. It overcomes the challenges of memory limitation and algorithm complexity and provides organizations with the computational power they need to drive efficient data analysis.
Popular use cases of distributed machine learning
Since it makes machine learning tasks on big data scalable, flexible, and efficient, distributed machine learning is an asset for companies, researchers, and individuals to make informed decisions and draw meaningful conclusions from large amounts of data. Here are some popular use cases of distributed machine learning:
- Invoice processing has always been a tedious job, requiring humans to spend hours browsing through invoices, manually processing payments, maintaining accounts, and entering data into a ledger. But distributed machine learning changes all of this. Through deep learning and optical character recognition (OCR) techniques, distributed machine learning algorithms automatically read invoice images, extract values from different fields, check for errors, and finally process the invoice if everything is in order. This leads to a drastic improvement in accuracy and reduces the time taken.
- Claims automation is also a great end product of distributed machine learning that allows insurance companies to automate the claims process, reduce wait time, and free agents to work on more critical tasks. By recognizing human speech and extracting relevant data from unstructured data and NLP, distributed machine learning can help digitize handwritten forms and analyze audio messages and calls in real-time – thus increasing speed and efficiency.
- In the area of service personalization, distributed machine learning can be used to analyze the ever-growing data about consumers, and create personalized profiles for them. Based on their historical and personal data, it can track, record, and analyze consumer behavior and enable organizations to deliver personalized services and enhance the customer experience.
Create a real business impact
In an age where understanding the customer needs and offering services that best fit those needs has become a business prerogative, using traditional machine learning algorithms that solve generic open-ended problems is insufficient to drive competitive advantage. What is needed is for organizations to embrace techniques that show meaningful results quickly. That’s why distributed machine learning has become more important than ever in today’s digital era.
At Wissen, we understand how important it is for organizations to drive value at every stage of their business, and hence we leverage concepts like distributed machine learning to solve problems that can be a real value-add. With our deep domain knowledge across industries and tech expertise in scalable and extensible machine learning architectures built for modern cloud stacks, we can help you unearth insights from your data and enable you to create real business impact. Contact us today!
