What Does A Machine Learning Developer Do?

Machine Learning: Machines are becoming increasingly intelligent because there are people who teach them to learn. We clarified with Damian Broth from the DFKI what these software experts have to bring with them and why they must be interested in themselves.

Suppose Siri understands our speech pattern in our smartphone. In that case, the auto-completion of chat programs recognizes what we intend to type earlier and earlier. Our e-mail program reliably separates spam from desired messages. Then we are dealing with machine learning. We are already encountering the sub-discipline of artificial intelligence at every turn and making rapid progress in the process. But what does the job description of a machine learning developer look like? 

As well, especially in ​​deep learning, i.e., the area of ​​machine learning based on deep neural networks, there has been significant progress since 2012. The technology is very successful when it comes to vision, translation, and speech recognition. Today, for example, it is predictable which emotions a picture will trigger and its popularity.

Do Research And Industry Have Enough Developers For Machine Learning?

Not at the moment, that’s why they’re so popular. Data analyst is one of the most important professions of the future. It is not for nothing that it is already considered the ” job of the century.”

Already today, we are surrounded by algorithm-based models of artificial intelligence. For example, when we interview a search engine, use facial recognition for pictures, or inquire about a bank loan, our rating is called up. All of this is enriched with artificial intelligence, even if we are not always aware of it. And the areas of application are becoming more and more. Artificial intelligence is considered to be the computer science of the future.

A degree in computer science with a focus on mathematics or, conversely, mathematics with a focus on computer science is ideal. This is so important because the development of algorithms is very formal. A neuroscientist who understands how the human brain is structured and contributes this knowledge is also conceivable.

But I can jump on board, for example, from physics or other disciplines.

Then Where Do I Get In With This Knowledge?

Wherever you want, Data science is the best discipline to make a career in business. The financial industry needs developers, retail, mechanical engineering with its robot technology, and human-machine communication. Everything is moving towards the digital sphere. We are still at the beginning of transforming data and information into service products. The rethinking that information can be used is only just beginning.

And Then Do I Spend My Professional Life Programming Algorithms?

But no. Collaboration is essential in research—the exchange within working groups or with students. The more people involved in a project, the more feedback and advice are needed. In addition, research is advancing at such a pace that many scientific papers are being published. There is a lot to read, learn, and discuss. But of course, there are also phases in front of the computer when you try to implement your networks and get them up and running.

That Sounds Like A Spirit Of Optimism And Excitement. What Is The Situation Outside Of Research In A Company?

That depends on how much you’re allowed to experiment. Innovative companies rely on two-speed IT. On the one hand, IT accompanies production and processes in practice, and on the other hand, isolated islands where new things can be tried out. Once a developer has found the company that suits them, even more Collaboration is required than in research: across all disciplines, from product development to the legal department and marketing. For this, we need people who can communicate across all areas, levels, and levels of detail.

What Are Other Properties Important?

A good team can handle every archetype from the genius to the loner – as long as it is only represented once per team. But you will be successful if you can find a narrative around the technology and explain where it will lead and what good it will bring for companies. Technology is always a promise to the future. Those who recognize the advantages, set up a lab, look for and find use cases, and have an entrepreneurial spirit will go their own way.

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