Golden Rules Of Machine Learning Consulting

Machine Learning Consulting

Artificial intelligence, machine learning, and the Internet of Things are all buzzwords these days. Even at the highest state level, there is a lot of chatter about them, and their introduction promises golden mountains.

But are such lofty goals really justified? What are the potential pitfalls? How can you get over them on your journey to success? First and foremost, what should you focus on?

For which practical business tasks will artificial intelligence, the Internet of Things and big data analytics be most useful?

The list of such problems is extensive. First of all, it is performance evaluation, business process modeling, efficient use of resources, predictive analytics to ensure transport security, client analytics to study people’s preferences and much more.

In other words, these are all areas where one main and universal task is required – to support the right strategic and tactical decisions based on data at both the organization and individual level.

Now many people say that data is a new oil. But how to use them? Miracles are expected from analytics. But you need to understand that this is primarily a job. The definition of the task depends on which technology should be applied.

The range of technologies is huge, and we act as a conductor who controls various tools to help management solve a certain problem. We recommend you this machine learning consulting services.

What problems make it difficult to use artificial intelligence, machine learning and big data?

The biggest issues stem from firm management’s high expectations for the usage of these technologies, as well as a lack of digital culture in the workplace.

Furthermore, getting the most out of even the most powerful and adaptable technology solutions necessitates a focus on data quality, data management, and data analytics.

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The competence of employees is very important. Our experience shows that technology is technology, but the main thing is a digital culture in the organization, which must be carefully thought out and implemented in an end-to-end way.

Thus, a few years ago, the profession of Data Scientist, i.e. data researcher, was almost unknown. And now organizations are increasingly talking about the need to create such competencies to solve the analytical problems of business units with their own resources. And these are not just experiments, there are specific examples.

What can we offer to solve the above problems?

World practice shows that one of the necessary conditions is the formation of a new digital mentality and corporate culture of working with data in the company.

This is certainly not an easy organizational and technical task, often requiring a special program of comprehensive transformations with mandatory consideration of the current level of maturity of the organization in data analytics.

What should organizations pay attention to in order to profit from AI and big data analytics? After all, all this requires investment.

The most difficult thing is the choice of strategy. Now everyone is talking about digital transformation. But any transformation involves change, and people don’t like change.

Therefore, a multi-stage approach is required here, starting with high-level tasks, and technology should be approached later, already understanding the requirements.

According to world experience, it is this top-down organizational approach that usually solves many problems, and the choice of specific platforms and analytics tools becomes a common technical and business task.

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The technical stack is more of an afterthought. And there’s no need to chase the most cutting-edge technologies here. It is critical that the tasks correspond to the infrastructure level.

But analytics is not just infrastructure. Now the center of gravity is shifting from technology towards organizational changes. Because this is a whole series of strategic and advisory issues that are absolutely necessary to solve.

You can purchase the most advanced technologies, recruit data researchers, make interesting experimental solutions, but they can be useless in terms of the company’s development.

A whole approach is important here, not a point or experimental approach. Because analytics is now actively moving from experiments to practical application. You can learn more here – Data Science UA.