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This is how smaller companies can use Machine Learning, too

Machine Learning is a partial field of artificial intelligence (AI) that uses statistical models and large amounts of data to identify patterns, derive predictions, make independent decisions, and optimize processes. For many companies, the successful implementation of machine learning can be a decisive factor to their digital transformation.

Half of all German companies are already actively involved in machine learning. 22 percent of them are already using the technology productively, in particular large corporations, as our current study shows. But smaller companies can bring the new AI technologies into successful use as well – thanks to machine learning as a service (MLaaS) offerings. 

Machine Learning is a partial field of artificial intelligence (AI) that uses statistical models and large amounts of data to identify patterns, derive predictions, make independent decisions, and optimize processes. For many companies, the successful implementation of machine learning can be a decisive factor to their digital transformation. 

Smaller companies, however, often fail to deploy machine learning due to a lack of resources. This is because the development and use of machine learning models and their training does not only require time and money, but also a team with the right data expertise. Yet these experts are in short supply all over the world. According to an article in the New York Times at the end of 2017, less than 10,000 people worldwide as of the end of 2017 have the necessary skills to conduct serious research into artificial intelligence. 

In addition, smaller companies often do not have enough data to create powerful AI models. But they can also use machine learning algorithms with fewer of their own data to gain business-critical insights. By now, there are some offers that make machine learning "as a service" easier to access for every type of company.

What is Machine Learning as a Service (MLaaS)?

MLaaS works on the same principle as Software as a Service (SaaS). The machine learning application runs directly on the vendor's platform, who is responsible for the installation, configuration and operation of the interface. Through a combination of automated and semi-automated cloud platforms, most infrastructure issues such as data pre-processing, model training and evaluation are covered with further prognoses. Forecast results can be linked to the company's own IT infrastructure via interfaces.

At present, some of the major public cloud providers offer comprehensive catalogs of micro services which companies can purchase to create their own digital platforms. The concept of digital platforms and microservices is extensive; AWS, Microsoft Azure and Google's Cloud Platform offer a wide range of as-a-service offerings from cloud computing, storage and database management via augmented and virtual reality to business productivity applications and tools for the Internet of Things. These micro services are largely API-based, enabling rapid deployment.
That's the attraction of these services. Because it is challenging and time-consuming to create a development environment when it comes to quickly scaling or creating a product. These services also include some machine learning capabilities such as text, speech, and image recognition, as well as multifunctional AI platforms such as Amazon Sagemaker, Microsoft's Azure Machine Learning Studio, and Google Cloud Machine Learning Engine.

Cucumbers bring the proof

Makoto Koike, a farmer's son from Japan, recently demonstrated how well a MLaaS solution can be implemented. His family breeds cucumbers. When he came back to the farm to help maintain the business after a case of illness, he soon found that sorting the cucumbers was taking quite a long time. Japan does not define any fixed rules for sorting vegetable classes, each farm does the classification on its own. On Makoto's parents' farm there is a nine-class system that his mother had developed to perfection. By using an MLaaS model, he himself succeeded in automating the sorting of the cucumbers, relieving his parents and making operations more efficient.

Use and applications of Machine Learning

Machine Learning can be used in almost every industry and make practically every application successful. Especially in small and medium-sized companies, the technology can simplify workflows and minimize effort. A few examples:

Marketing: Machine learning can help to improve marketing decisions. Last year alone, 43 percent of marketers used AI and machine learning to improve predictions about customer decisions.

Automation: One of the major benefits of machine learning is the automation of repetitive tasks and the resulting increase in productivity. Chatbots, for example, are a well-known machine learning application to make customer service more effective.

Security: By checking data patterns, machine learning is predestined to detect suspicious account behavior or even fraud – an important function in financial monitoring and network security.

Customer recommendations: Netflix, Spotify or Amazon – they all create personalized suggestions for their users and customers. By identifying patterns in user behavior, machine learning can increase the relevance of ads to specific users.

This type of predicting can also help smaller companies to personalize service or product recommendations.

Learn more about the successful use of machine learning in business from our latest study in collaboration with Crisp Research and Dell EMC.

Download the study for free! (available in German language only)

 
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