Common Challenges Organizations Face while Working with Machine Learning Technology

Fremont, CA: While many academics and experts feel that we are in the peak years of artificial intelligence, there are still numerous barriers and problems to face while building your idea. Groundbreaking advances in machine learning algorithms are breaking new ground and showing that robots can think and plan their future moves once and for all. Although AlphaGo and its predecessors are highly complex and specialist technology, machine learning offers many more practical applications, including video recommendations, predictive maintenance, autonomous automobiles, and many more.

With this in mind, let's take a look at some of the challenges businesses face as they work to create machine learning technologies.

• Talent Deficit

Machine learning is a fascinating and rapidly emerging topic, yet few professionals are capable of developing such technology. Even a data scientist with a strong understanding of machine learning methods is unlikely to have sufficient software engineering abilities. While a skills gap causes certain challenges for businesses, the demand for the few available professionals on the market who can design such technology increases their prices.

• High Costs of Development

While we've already discussed the high expenses of hiring AI expertise, other costs are associated with training the machine learning algorithms. This technique necessitates several hours of data annotation, and the associated expenditures might potentially ruin initiatives. As a result, many businesses elect to outsource data annotation services, allowing them to concentrate their efforts on product development. It's widespread in the automotive, healthcare, and agriculture industries, but it may also get used in other sectors. As a result, to save part of the development expenses, firms worldwide are turning to outsourcing.