When it comes to artificial intelligence (AI) and machine learning, moving from prototype to production is critical.  

In a recent survey, Gartner revealed that, on average, only 54% of AI projects make it from pilot to production. Come straight, nearly half of the AI models fail to reach the production stage. "Scaling AI continues to be a significant challenge. Organisations still struggle to connect the algorithms they are building to a business value proposition, which makes it difficult for IT and business leadership to justify the investment it requires to operationalise models," said Frances Karamouzis, distinguished VP analyst at Gartner.  

The crux - it's easy and exciting to discuss machine learning applications, in theory; however, it is very difficult to actually put machine learning models to use in production on a large scale. 

Valid reasons exist 

From fraud detection in finance to personalised customer experience in the retail - ML models have become the hot favourites for various businesses. The pain point is a lot of these models fail to see the light of day, and there are valid reasons. First, the companies tend to start building with the objective of finding a solution rather than clearly defining the problem statement. Without a comprehensive knowledge of which problems are genuinely fit for ML to tackle, CEOs get into the task of recruiting data scientists and ML practitioners to automate or optimise processes. 

The second issue starts surfacing when the model gets into the training phase. Here the issues with training data or any misfit models become the source of failure later on. The aim of the ML model is to make accurate predictions when fed with new data, as it is concerned with identifying relations between different datasets and providing trends. Therefore, the training dataset needs to be clean and representative of the real-time data that the model will eventually analyse in production. Without a doubt, investing a lot of time and effort in data labelling, cleaning, and feature engineering is necessary. 

It's crucial to define the business challenge at the onset clearly, understand what success looks like, create an end-to-end solution, and have a clear view of the importance of the company's machine learning project in relation to other priorities. As a result, a firm's project might never obtain the engineering resources required to reach production without this strategic plan. 

The road to production 

With AI business value expected to reach $3.9 trillion in 2022, it is critical for the industry to get more such models to move over the line. Several steps need to be taken into account. 

People frequently begin a data project by diving right into the technology, but it's vital first to step back and consider your priorities. Consider a business problem that needs to be resolved that has distinct KPIs attached to it. Make sure the issue is significant enough for the company to want to spend money on it. Business executives want to ensure they receive value from data projects, so consider the advantages for both internal use and end users. Similarly, it's critical to understand how the ML project will affect the business from the initial phase. Will it help with cost savings or revenue growth directly impact your customers' day-to-day tasks or employee safety? 

Furthermore, it's okay to have a broad perspective and a big picture, but it's a good idea to get the fundamentals right before scaling things up. The need is to select a significant issue for the organisation, making sure it can be resolved and will prove to have real business value. Spending too much time in the project's beginning discussing technology is a bad idea. Moving on, it's common for firms to recruit data science teams and then fail to integrate them into the rest of the organisation. Silos must be dismantled in order to incorporate data scientists with other teams, such as product and IT, rather than keeping them bottled up in a room working alone. 

Finally, some industries, including the banking sector, are significantly more advanced in their use of machine learning. Businesses must not be scared to look into and learn from what other companies have been doing. If you are starting a company, it's good to learn about some of the obstacles that have prevented growth in other industries and try not to repeat those mistakes.