To look cool, sound smart & futuristic, people in tech often start talking about solving a problem in real life using ML. It’s all the hype right now, and people can’t get enough of it. But all these fancy ML projects meaning to solve the most interesting problems are mostly bound to failure. Why?

It’s the sustainability issue!

Machine Learning has been going through a crisis because of its ROI issue. Seriously, what is the point in deploying sophisticated ML products if you can’t make money from it, or if you need to spend more developing it than what you can monetize it for?

The ROI of most ML projects becomes a hindrance in the long term survival of these products. Companies pour millions of dollars into the development of these ML products, but find it difficult to reach the break-even point. This is concerning not only to new age founders, but also to their investors hoping for a better outcome in the near future. 

But the good news is, we might (finally) be seeing the end of the tunnel. Yes, you heard it right. There are ways you can monitor and limit the expenditure on your ML projects and expect a positive ROI.

But what are the reasons why so many still fail to see a return on their ML investments? Here are the 7 causes why these ML projects are failing, and how one can overcome them.

Hiring the wrong people for the job

It’s a known fact that hiring the right candidates for a tech role is not an easy task. Many times, hiring managers onboard someone who doesn’t fit into the job requirements and waste organization time and money. It gets even more difficult to find a suitable candidate for ML projects as the supply-demand ratio for experienced ML engineers is not in the employers’ favor. Companies often end up hiring the best on paper without taking the time to look for the right match. Before investing too much into hires for ML, make sure they fit into the actual technical requirements you have for the job. You should make sure they have relevant experience & expertise to actually execute.

Not training / educating executives about data strategy

It’s important for every team to work as one. This way, both the outcome of the project and the team efficiency improve. In that spirit, it’s essential to educate your data executives and ML team members about the data strategy your organization wants to get behind. It’s vital for them to understand the delicacies and limitations of time, manpower and budget for the projects.

Data literacy is a must for every executive in a data-driven organization. Your executives must understand the importance of data and establish the ways to communicate with their data teams and stakeholders to make proper decisions regarding which data product to build, as well as how to manage data governance or data privacy.

Failing to give the proper guidance to data scientists

Data scientists are the backbone of an ML project. They are the people who decide on almost everything that’s important to build an ML product. Though they are pretty smart folks, they often need guidance and alignment with other teams (in particular, Product teams). Data Scientists are like gifted athletes: with some guidance & training, they’ll knock it out of the park.

Often, organizations fail to provide proper guidance to data scientists leading an ML project. As a result, many times they end up solving the wrong problems due to the lack of connection with the product owners. This hinders the paced growth of the project as they find it difficult to deliver because of improper understanding of the project and its ins-and-outs.

Paying too high of a premium for data scientists salaries

It is a known fact within the industry that data scientists get paid a lot of money. Often, this fact makes organizations pay a lot more money than what is required to secure even the best talent.

No job in tech is easy, especially in the data space. But recent advancements, open source libraries and globally available MLOps tools have made it a bit easy for data scientists to do their job. This helped a lot of professionals do the jobs which previously were considered to be the elite’s game in data science. But still a lot of companies pay hefty salaries to data scientists, bringing up the overall expenditures on the projects.

Failing to leverage the proper tools

There is a plethora of tools available on the market for ML professionals to use as required. Teams today need to build a lot less from scratch as this is almost always a ready-to-use tool or library out there for the purpose. But still companies fail to explore & leverage relevant tools, and instead sometimes tend to waste time, efforts & money building solutions that are already out there.

It is important (and cost-efficient) for teams and their leads to explore different tools before jumping towards building their own.

Failing to introduce and maintain best practices among data scientists

The field of data and machine learning is still relatively new, and many of the best practices for cost optimization have not yet been widely adopted or taught in educational institutions. This results in many data scientists and practitioners not following common sense cost-saving measures, such as automatically turning off their Amazon Web Services (AWS) Elastic Compute Cloud (EC2) instances when those are not in use. The lack of awareness and understanding of the financial implications of these actions can quickly add up and significantly (and negatively) impact the ROI of a machine learning project. It’s crucial for organizations to continuously train, educate and inform their staff on the best practices for cost optimization and budget management in data and Machine Learning projects.

Using datasets that are too large

It’s almost a given in ML space that you have to use as much data as possible to train an ML model; it’s considered a best practice for generalization. But the truth is, it is often not the case, as datasets themselves may have vanishing returns when they get too large!

The bigger a dataset is, the more the cost of compute and managing that dataset. The acquisition and storage of large datasets can be expensive and time-consuming, which leads to increased costs and longer project timelines. Additionally, large datasets often contain a significant amount of irrelevant, redundant, or incorrect data, which can negatively affect the performance and accuracy of Machine Learning models. This can trigger the need for additional data cleaning and preprocessing, further increasing costs and decreasing ROI.

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Conclusion:

While the advancements in Machine Learning and Artificial Intelligence have been impressive, there are still several hindrances to the growth and success of the field, and ROI is and remains one of the most significant challenges. This is why reducing the overall cost associated with an ML project in order to improve its ROI is critical to the success and widespread adoption of these technologies. By considering and incorporating those seven tips to better manage the budget and improve the ROI discussed above, organizations can ensure that their ML projects are not only technically successful but also economically viable. This not only benefits individual companies but also contributes to the growth and development of the entire AI industry. It’s important to note that while progress has been made, there is still a long way to go before ML and AI reach their full potential. By focusing on cost optimization, we can overcome some challenges and accelerate the growth and success of these fields.