In the contemporary era, huge computational systems are somewhat capable of understanding human behavior and complex structures. Moreover, machinery today can literally work with its own mind to perform specific tasks with the right set of coding and algorithms.   

Machine Learning (ML) being a sub-category of AI constructed on huge data-driven algorithms works upon automating and creating a robust method for data analysis. 

Importance of Machine Learning 

We notice population explosion in the world every year. Every decade oversees new people arising in the world, and people are multiplying in numbers. Similarly, the data also keeps on increasing within organizations.   

Today, for huge and successful organizations, data is the key. However, the human capability is not productive enough to collect, extract, grasp, clean, and analyze the mammoth amount of data existing within an organization. Moreover, not deploying robust methods like machine learning hinders the overall productivity of the organizations.     

Machine learning algorithms are capable of understanding customer behavior, trends, and business operations. Also, these algorithms can take actions without getting exclusively programmed and are self-reliant. Moreover, machine learning involves numerous calculations resulting in adaptive decision-making and generating suggestions independently.  

What are the Common Challenges Organizations Face while deploying an ML Project? 

Many data-driven organizations in various industries are using successful ML projects, these involve healthcare, marketing, manufacturing, sales, e-commerce, financial services, and government.  

However, only a few organizations are able to achieve a completely effective Machine Learning project. Furthermore, it is crucial for huge organizations to convert into data-driven organizations, utilizing their data productively and completely for overcoming the challenges involved in AI-related projects. These include; 

1. Low Success Rate 

Machine Learning projects require extensive research and a whole data science team working upon them. Usually, machine learning projects have a low success rate involved. The majority of the projects formulated, researched, and proposed aren’t even applied practically.   

For instance, out of 100 ML projects with extensive research done within each of these projects, only 15-20 would be practically applied, and 7-8 would turn out to be effective out of these. Thus, a major chunk of the ML project doesn’t provide for the organization.   

However, this is not due to the effectiveness of the Data Science team, but the difference between projects created in the lab and projects that can be practically applied.   

2. Enormous and Complex Infrastructure 

Setting up a machine learning project is not an easy task; it requires huge and complex infrastructure with computational systems involving parallel processing to process the data in real-time through using Graphics Processing Units (GPU). Moreover, it involves the use of Cloud Data Platform Servers for the management and governance of data for machine learning projects to be successful and completely run in real-time.   

However, for organizations using traditional computation systems, it is the real challenge to shift, evolve and adapt to new technology. These organizations have an even lower success rate due to hardware and tooling concerns.   

3. Not Using Proper Data Management Tools 

Machine learning projects involve processes like data preparation to clean, prepare and extract data beforehand. Proper data management tools are required for the successful application of machine learning algorithms. Otherwise, it would obstruct data accuracy and would hamper the whole analysis project involved in ML. As inaccurate data can never lead to accurate results for analysis. Thus, data management remains the most crucial process for not only ML projects, but most AI-related projects.   

4. Lack of Knowledge 

Every organization tries to employ the most skilled data, science team. However, there is a difference between data science and computer science. Most data scientists are capable of performing computational tasks and complex coding, as they are often asked by their organizations. But they aren’t experts in the field itself and always leave a margin for error.   

5. Unassertive Leadership 

If the leadership within the organization keeps a traditional mindset towards AI-related projects and uses AI as an “on and off” switch technology, then they will always remain reluctant towards new technology and would remain distant from effectively grasping the essence of robust technology.  

In conclusion, The most important aspect of an ML project is being successful. Without getting practically applied, the whole project would turn out to be unproductive and would remain a merely failed research program. Thus, leading to a waste of resources and time.