The Potential with Machine Learning
Today’s technology generates huge amounts of data, purposefully or as a side effect. Those data are extremely valuable for a broad spectrum of applications, from analyzing customer behavior to financial portfolio prediction, making recommendations, finding outliers and categorization. The more data samples are accessible, the more detailed and correct the result can be. However, with increased sizes of datasets it becomes more and more complex or too timely for humans to solve the relevant problems. Machine Learning can accomplish this task and extract simplified patterns that can be used to predict data or recognize new and unknown inputs.
Even though machine learning is often perceived as a futuristic technology, it has widespread real life applications today. For example, looking at the automotive industry, one often just thinks about self-driving robot cars. However, machine learning is already used for predictive maintenance and adaptive fuel efficiency, as well as sales prediction, part design and material optimization. This universality allows machine learning to be utilized in almost any kind and size of company, from freelancers to large cooperation, supporting individuals to streamline their work and taking over repetitive, complex or dangerous tasks.
This increased work efficiency yields a radical shift in the way we will work, which is often seen at least as significant as the industrial revolution. Similar to steam engines that replaced muscle power, machine learning will support, enhance or could even fully replace brainpower. Neither substitute was or seems appealing, but history shows that each revolution resulted in an economic leap increasing the average living standard whilst freeing up work capacity for other innovative undertakings – and we can already see the onset of these new occupations today. Data curators prepare, tag and classify training data; auditors evaluate the algorithms to eliminate bias; and privacy controllers ensure that all procured and generated data and algorithms follow current privacy laws and ISO standards. In fact, since machine learning algorithms consist of relatively simple functional units, most companies do not develop new ones and many of their software developers perform one or several of these new occupations.
Knightec’s competencies are perfectly aligned to help our customers to integrate machine learning into their companies. Our specialized software and system architects for machine learning, data science and cyber security, combined with experienced project managers, can identify possible applications and obstacles, suggest an action plan and support the implementation of machine learning models. A special strength is our cross-functional communication, which provides support, internal and external seminars and workshops, as well as comprehensive expert advice for complex assignments. From software development over application implementation until structure and data management – our consultants provide support along the full impact spectrum of machine learning.
About the author
André Dankert has a Master in Physics and holds a Ph.D. in Nanotechnology. More than 10 years of experience in RnD in both the academic and industrial sector made him highly adaptable and an efficient problem solver. He specialized in data analysis and automation with a focus on machine learning applications.