How Machine Learning Tools Can Help Solve Organizational and Real-World Problems

Artificial Intelligence is making an appearance in business computing today. Many IT departments are adopting machine learning technologies to help improve and automate processes to propel organizations forward. Having a solid understanding of what machine learning and robotic process automation (RPA) truly consists of and how it works is the first step to accelerating your business light years ahead of the competition.

What is RPA?

One of the major benefits to machine learning is that intelligent automation software is designed to solve complex organizational problems. RPA automates the actions of a human, reducing the need for a human to perform repetitive tasks. Intelligent automation software has also proven to reduce costs and errors as well as improve productivity and efficiency. The impact? A bigger bottom line.

Machine learning can enhance organizational performance across the board, paving the way for increased levels of automation in the future. As RPA and machine learning drive organizational operations and decision-making, more and more companies are investing in various machine learning tools. The trick is to find the best machine learning tool that does not involve a lot of complex coding and that is the best fit for an organization.

Here are some of the most widely used machine learning tools available today.

1. Amazon Machine Learning –

Amazon Machine Learning, or AML is an introductory machine learning tool that can be purchased and utilized as a part of the Amazon Web Service (AWS) package. AML uses a series of wizards and other visualization tools.

One of the major limitations to this particular tool is that machine learning operations can only be performed on data stored in AWS, which can be problematic if you have data stored elsewhere

2. Microsoft Azure Machine Learning Studio –

The Azure Machine Learning Studio brought to us by Microsoft offers a number of machine learning tools in their library, Microsoft Azure Machine Learning (ML) is a service used by developers to build predictive analytics models. Those models and workflows can then be executed in cloud web services.

Azure ML Studio supports various functionalities for building these models. It also allows access to data sources, data exploration and visualization, ML algorithms, experimentation and many others.

The biggest disadvantages to Azure ML is that it has fewer algorithms than some other machine learning tools. It is also a costly tool that deters many organizations.

3. TensorFlow –

TensorFlow by Google is a machine learning tool that was once built only for Google’s systems, but it is now available in open source software. The TensorFlow architecture is incredibly flexible, however, the tool is accessed through Python or C++ interfaces. This means that some coding knowledge may be necessary to begin using the tool, which may be an extreme disadvantage for some tech teams.

4. MLlib –

MLlib is another machine learning library that is comprised of tools designed for Apache Spark. In fact, Spark is widely used in various open source projects. There are a number of resources that fit within its data processing framework. Some common algorithms include image classification, decision trees, and data clustering. New algorithms are continuously being improved and developed.

On the other hand, although MLib offers a number of data processing functions, it does not support real-time processing. It also lacks a dedicated file management system. Many tech teams and organizations also believe that it is an expensive machine learning tool even with its many limitations.

5. Torch –

Torch is another commonly used machine learning development framework that is utilized in a number of open source projects. It allows complex algorithms to communicate via GPU-accelerated computing and hardware, and without the need for hardware coding.

Torch applications are scripted using LUA, which isn’t a very popular programming language. As a result, this could be a huge disadvantage for many tech teams.

RPA and Real-World Problems

RPA can help solve a number of problems, particularly those that may differ from enterprise to enterprise. Yes, some machine learning tools may involve various levels of coding and prior knowledge for set up, deployment, operation. However, once fully implemented, machine learning tools can uncover errors, inefficient operations, or areas of non-compliance.

WorkFusion in the Workforce

The WorkFusion SaaS platform automates human data analysis by leveraging machine learning algorithms. WorkFusion provides business users the tools they need to optimize information processes and better manage global workforces. In fact, WorkFusion is specifically designed and developed for businesses and executives to help them significantly improve data quality, speed, and ROI.

For more information about WorkFusion, download this enterprise architecture view to see how WorkFusion can help optimize your workforce.

Julie Anne Gniadek, owner and founder of J. H. Language Solutions, a business that specializes in web content management and marketing.