Machine learning solutions can be considered a subdivision of artificial intelligence, with multiple machine learning algorithms combined to create artificial intelligence. Some of these algorithms can, however, be used to accomplish specific, limited tasks, ranging from answering phones to recognizing images. 

Machine learning (ML) solutions are normally used in situations requiring adaptability, a limited variety of responses to choose from, and where ML can learn from its mistakes. 

Adaptable machine learning solutions can be incredibly dynamic and are used to resolve various problems, ranging from recognizing spam to supporting chatbots and virtual personal assistants.

According to MIT professor Thomas W. Malone, “In just the last five or 10 years, machine learning has become a critical way, arguably the most important way, most parts of AI are done. So that’s why some people use the terms AI and machine learning almost as synonymous … most of the current advances in AI have involved machine learning.”

Machine learning solutions are currently being used to improve various business processes, but new uses are still being discovered. Additionally, using machine learning algorithms to provide services will alter old business models and promote the creation of new ones. This, in turn, can allow the business to evolve, becoming more efficient and profitable. 

The creation of a successful machine learning solution will begin with the recognition of a specific problem or business opportunity. 

Unlike traditional programming with a manually created program using input data to generate the output, machine learning uses the input data and the output data to create a program. As advances in machine learning continue to develop, the range of its uses and applications will also expand. 

An important question is, “If the machine learning solution improves sales and profitability, how long will it take for the investment to pay for itself?”

Off-the-Shelf Machine Learning Solutions

Most “off-the-shelf” ML approaches are easy to install and use. Off-the-shelf tools need little more than clicking a download button, followed by a brief wait as the software downloads. Although they are quite simple, they unfortunately come with some limitations:

• Tedious repetition: A task may require repeating the same action several times manually. Some ML solutions include “bulk download” or “bulk upload” options, but not all do.
• Manual storage: If the ML work is one small part of a much longer phase, saving the data may need to be done manually, which is time-consuming.  

Off-the-shelf software rarely offers the range of services that are needed, and some level of customization may be required. The more unique the project, the less likely an off-the-shelf solution exists. Some of the more common uses of machine learning are: 

• Recognizing spam: One of the most basic applications of machine learning. Email providers normally filter unwanted spam. They use an ML model to identify spam, based on recognizable characteristics. Neural networks use a content-based filtering process to segregate unwanted emails as spam. (Spambrella, SpamTitan, ZeroSpam)

• Product recommendations: These ML systems are used consistently by e-commerce websites, search engines, and web and mobile apps. Prominent internet retailers (for example, Amazon and eBay) often present a lineup of recommended products based on previous purchases by the consumer. (clerk.io, Nosto, Qubit) 

• Image recognition: Deep learning (a subdivision of machine learning) is used in image and video recognition. These machine learning solutions are used for various recognition tasks, including facial recognition, object detection, visual searches, text detection, and logo and landmark detection. (Infiviz, Vue.ai, Alibaba Image Search)

• Fraud: Fraudulent banking transactions have become a common occurrence. Machine learning solutions in finance have been developed that automatically build highly accurate predictive ML models that identify and separate a large variety of possible fraudulent activities. (Datavisor, Sift, NoFraud) 

• Demand forecasting: Used in multiple industries, ranging from e-commerce to manufacturing to transportation. It uses historical data to support ML models and algorithms to predict the demand for products, power, services, and usage. ML-based demand forecasting has become extremely accurate, quick, and transparent. (Logility, Fayrix, Demand Forecasting)

• Virtual personal assistants: These accept and understand voice commands and can perform simple tasks, such as making a phone call, scheduling an appointment, and turning off a light. A virtual personal assistant uses natural language processing, AI, robotic software automation, and multiple machine learning algorithms to understand verbal commands and to process them. (Siri, Aisiri, Nuance)

• Automated customer service: Managing multiple online customer interactions can be difficult and has become a point of frustration for many businesses. This is primarily because businesses lack the customer support staff needed to deal with the volume of inquiries they receive daily. (Zendesk, Onedesk, Hubspot)

(The service providers above are examples, not suggestions. Research is advised.)

Accessing Open-Source Machine Learning Solutions

To maximize efficiency, it is important to start with a business problem that is clearly defined. As part of the clarification process, the question should be asked, “Will machine learning actually help in resolving the problem?” (It should be understood the algorithms, whether off-the shelf, or newly created, may not provide the expected solution.)

GitHub acts as a central location for most current open-source projects. Scientists, researchers, businesses, and hobbyists provide a wide variety of solutions and share them there. GitHub supports a global community of collaborative researchers and can be useful when researching machine learning options.

Many of these ML solutions are well-designed and come with comprehensive documentation, including full codebases and step-by-step instructions. 

Additionally, access to the codebase is typically unrestricted (unlike subscriptions or per-use arrangements). This allows the user to understand how the solution works, and to adjust it as needed. The ML code can be adjusted and merged with the company’s codebase.

If set up properly, machine learning can streamline the resolution of customer issues and help enhance customer satisfaction.

Customizing code requires experience, and the larger the customization, the more experience is needed. Despite this, open-source machine learning solutions can be a remarkably practical resource. 

Accessing Amazon SageMaker

The AWS cloud offers a machine learning service that allows people of all skill levels to use and develop machine learning technology. This service is called SageMaker (not to be confused with the Amazon machine learning service, which is no longer accepting new members, nor being updated) and offers a complete machine learning service. It can help in creating machine learning models. There is a training guide available, along with some courses. 

SageMaker services are billed per minute of usage. There are no upfront commitments and no minimum fees.

Wrapping Up

Machine learning technology can recognize patterns, access the data, and interpret behavior. It provides customer support systems that are designed to imitate real humans in resolving a customer’s unique queries. The machine learning solutions are trained with human languages and voice variations to translate the voice to words efficiently and then offer intelligent responses.

Organizations developing cutting-edge technology (startups, tech companies, and universities) are striving to develop new, novel ways of using machine learning. Some new concepts are:

• Automating employee access control: Amazon launched a machine learning contest on Kaggle to develop an automated employee access control. They are attempting to develop a computer algorithm that can predict the employees who should be granted access to restricted resources.

• Protecting animals: Cornell University is developing an algorithm for identifying whales so ships can avoid accidentally hitting them. 

• Predicting emergency room wait times: Health care organizations have started using a machine learning solution called “Discrete Event Simulation” to predict the wait times for patients arriving at an emergency room.

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