4. High Error-Susceptibility

Machine Learning is autonomous but highly susceptible to errors. Suppose you train an algorithm with data sets small enough to not be inclusive. You end up with biased predictions coming from a biased training set. This leads to irrelevant advertisements being displayed to customers. In the case of machine learning, such blunders can set off a chain of errors that can go undetected for long periods of time. And when they do get noticed, it takes quite some time to recognize the source of the issue, and even longer to correct it.

5. Ethical Issues

The idea of trusting data and algorithms more than our own judgment has its pros and cons. Obviously, we benefit from these algorithms, otherwise, we wouldn’t be using them in the first place. These algorithms allow us to automate processes by making informed judgments using available data. Sometimes, however, this means replacing someone’s job with an algorithm, which comes with ethical ramifications. Additionally, who do we blame if something goes wrong?

6. Absence of Skilled Resources

Machine learning is still a relatively new technology. From the start code to the maintenance and monitoring of the process, machine learning experts are required to maintain the process. Artificial Intelligence and machine learning industries are still freshers to the market. Finding enough resources in the form of manpower is also difficult. Hence, there is a lack of talented representatives available to develop and manage scientific substances for machine learning. Data researchers regularly need a mix of space insight just as top to bottom knowledge of mathematics, technology, and science.

7. Deficient Infrastructure

Machine learning requires a tremendous amount of data stirring abilities. Inheritance frameworks can’t deal with the responsibility and clasp under tension. You should check if your infrastructure can deal with issues in Machine Learning. If it can’t, you should hope to upgrade it completely with good hardware and adaptable storage. 

8. Slow Results and Bias

Machine learning is time-consuming. Due to an overload of data and requirements, it takes longer than expected to provide results. Focusing on particular features within the database in order to generalize the outcomes is very common in machine learning models, which lead to bias.

Conclusion

Machine Learning has taken over many aspects of our life. Although it's not perfect, machine learning is a continuously evolving field with high demand. Without human intervention, it delivers real-time results using the already existing and processed data. It generally helps analyze and assess large amounts of data with ease by developing data-driven models.  Although there are many issues in machine learning, it's an evolving field.  From medical diagnosis, vaccine development to advanced trading algorithms, machine learning has become vital to scientific progress.