Technology

Challenges of Applied Machine Learning

From face and speech recognition, medical diagnosis, traffic predictions, to product recommendations, machine learning has more than ever before found many applications and continues to be used to improve human lives. Machine learning is no doubt the skill of the future as more and more businesses adopt this technology to make accurate predictions from the vast amounts of data generated from their systems. As such, the future of machine learning is promising as the world is yet to witness the full capabilities of machine learning in various sectors and applications. At the same time, the demand for machine learning professionals is set to rise.

However, the industry demands not only machine learning innovators that can come up with effective models and techniques to solve business problems. But also it needs ML professionals that understand and can apply machine learning concepts, implement ML models and use the available tools and libraries to solve existing business problems. The latter is, in essence, applied machine learning. At its core, machine learning is concerned more about products, features, and results from ML algorithms rather than the development of algorithms.

Undertaking an Applied Machine Learning online course can help you to conveniently gain knowledge about the recent advancements in practical applications of Machine Learning, and also be able to understand and implement the ML algorithms in real-world scenarios. If you can meet the demands of multinational companies with your expertise, then you will certainly boost your value and steer your career to greater heights.

What is applied machine learning and why is it important?

Machine learning can be loosely defined as the application of science and technology to develop a learning model aimed at improving human lifestyle and solving specific data-related problems. These problems are usually characterized by observations consisting of input and output data, coupled with an unknown but coherent relationship between the two. The operational principle of this model is to learn a generalized mapping between input and output data such that skillful predictions can be made for novel instances drawn from the domain where the output variable is unknown. The iterative aspect of machine learning is also important because as models are constantly exposed to new data, they are able to independently adapt and learn from previous computations to produce reliable yet repeatable decisions and results.

Applied machine learning is therefore the ability of an ML professional to understand machine learning models and apply them correctly to real-world situations to solve problems. It is in essence the more practical aspect of machine learning that, as mentioned above, is concerned about the products, features, and results that the implementation of machine learning models produce.

The importance of applied machine learning comes in that it combines the understanding of the laws of Machine Learning at a fundamental level, with a focus on technological applications to provide practical solutions to important societal problems. As such, it provides the very essential link between machine learning concepts and real-world scenarios. The multidisciplinary relevance of Applied Machine Learning, coupled with its depth and flexibility is what validates its effectiveness in problem-solving. The range of industries, sectors, devices, and systems benefiting from expertise in this field is almost unmatched in scope and importance.

Theoretical vs Applied Machine Learning

Generally, theoretical science involves the continuous formation of new theories and verification of existing ones, in which the primary purpose is to expand on what is known(knowledge) but with little significant connections to contemporary problems. Applied science in contrast involves the application of theoretical knowledge in practical or real-life situations hence its major purpose is to increase what is known about a problem, with the aim of creating a better solution. Both paths are very different and empower individuals in different ways to make significant impacts.

  • Applied Machine Learning is about understanding the Machine Learning concepts at an abstract level sufficient enough to use them to solve problems i.e. involves the building of data products or the use of existing algorithms within data science pipelines. Theoretical Machine Learning on the other hand involves the study, research, and development of new algorithms. It is quite a vast topic as compared to the Applied Machine Learning part.
  • In applied machine learning, results are seen much quicker since the problem being solved is usually at hand. However, in theoretical machine learning, results come much slower since it mainly deals with concept inventions and innovations.
  • Another major difference is that Theoretical Machine Learning, being majorly about fundamental concepts, can be learned by any freshman with no ML background whatsoever. Applied ML however can only be undertaken by a person who already has a deep understanding of Machine Learning concepts hence is only working towards applying them.

Challenges of applied machine learning

It is interesting that open problems in machine learning are mostly those that arise during its application in solving real-world problems. This is because applying machine learning in solving real-world and business problems presents challenges that are usually absent in academic and scientific research settings. These challenges include:

  • Problem definition. Defining the problem is critical in applied machine learning because it determines the technology, data sources, and people who will be working on your product. It requires numerous resources, knowledge, and skills that go beyond data science, which can integrate AI algorithms into applications used by thousands and millions of people every day. These requirements are not only challenging to get but also challenging to “get right”.
  • Getting the human resource that will work synergistically with all other resources and in tandem with the chosen strategy is usually a major challenge even for expert developers.
  • Applied machine learning is also challenging because the designing and maintaining of a perfect learning system or model for a given problem are intractable. Machine Learning models are prediction machines that find patterns in data obtained from the world and try to forecast future outcomes from current observations. As the world around us changes, so do the data patterns, and models trained on past data gradually decay, even becoming obsolete.
  • Collecting, assembling, and organizing the data required to train models. Data used to train models in academia are usually available and are only meant to prove the functionality of the model, not solve real problems. Out in the real world, high-quality and accurate data that can be used to train a working model is incredibly tricky to collect.

Conclusion

Applied Machine Learning, being a problem-solving tool for real-world issues, demands minimal room for error or negligence. In fact, its complexity increases when the unpredictable and uncontrollable variables by mother nature are factored in. Understanding the scope and nature of applied machine learning as a field is essential in projecting what it takes to be successful in it. Comprehending its differences from Theoretical Machine Learning is also crucial to avoid falling into pitfalls that come with treating them the same. By grasping and tackling the challenges of Applied Machine Learning, product leaders can not only avoid failures but also avoid repeating the failures of other organizations.

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