Perfect Your Machine Learning Models with these 5 Key Strategies

These strategies will help companies ease their machine learning inception in practical application

Machine learning is recently moving from the realm of research into real-life adoption. If executed well, futuristic technology can offer immense advantages which can provide any organization with a competitive edge. Unfortunately, it is not easy. Implying machine learning into a company requires both technological and strategic efforts. The main obstacle organizations face while doing so is the lack of understanding and negligence. Therefore, we have listed five strategies that could work as key to streamline machine learning in your business.

 

In the digital world, technologies like artificial intelligence and machine learning are crawling into every corner of the office. The applications of machine learning are widespread and fast becoming an internal part of different fields such as healthcare, finance, telecommunication, manufacturing, etc. However, adopting machine learning into the business and using it in the routine working system is not easy. They create the need for trusted data sources, organizational change management, iterative revalidation practices, and measuring the business value of the technology insertion. Amazon, the ‘all-product store’ is the biggest example of successful machine learning implementation. The company is currently using the technology to predict the demand for its products, set their prices, make personalized recommendations, optimize distribution routes, improve computer vision to detect fraud, etc. Therefore, before jumping to the conclusion and introducing machine learning in the company, businesses should first organize and prepare themselves to give space for the technology. In this article, we break down machine learning implementation and suggest strategies that could help companies ease their inception in practical application. 

 

Retain a better data strategy in place

Even though machine learning is the center of attraction for many businesses, the powerhouse of the technology is big data. Before implementing machine learning, companies have to make sure that they have a proper data strategy in place. The machine learning model can be put to good use only when it is trained with good data. If the data is unstructured or biased, then it will reflect on the machine learning model affecting its performance. Machine learning scientists will also end up spending their precious time doing labor-intense jobs like data clean-up and management. Therefore, before embracing any technology, businesses should create a better data culture. 

 

‘Look before you leap,’ a proverb to keep in mind

 

Investing in technology without having a vision is useless. Therefore, businesses should first categorize their need for machine learning and then look for ways to adopt it. The companies should also make sure there is no disruption between the business teams and the organization’s IT team when it comes to connecting for technology. The business teams should put forth their vision and the IT teams should try to develop on that. In a nutshell, the coordination between business and IT teams and their common goals is the success sauce behind machine learning implementation. 

 

A trick to simplify machine learning implementation

 

An easy and simple way to run machine learning models is by running them through programming languages like Python and R. Even though many suggest that it is not wise, the idea cuts down investments and demand for talents on many fronts. By enforcing this new method, companies don’t have to worry about specialized professionals in programming and algorithmic tasks. Since the professionals are not very close to the business convergence methods, they always lag behind when it comes to perfectly delivering machine learning services that could complement the company’s performance. Besides, organizations can move their machine learning initiatives to the cloud. This will open the door for more accessibility in a decentralized mode and will also dramatically reduce costs. 

 

Align ML initiatives with AI and corporate goals

 

Many executives take the wrong step of jumping into machine learning without realizing its negative impacts. Machine learning is a sophisticated technology that needs upfront investment, minimization of information asymmetry, minimizing inertia, a clear roadmap, and alignment of interests. Therefore, the key to reaping the maximum out of machine learning is by aligning it with artificial intelligence strategy and corporate-level goals. Generally, artificial intelligence programs in a company work to fulfill their business success. Henceforth, keeping AI strategy and machine learning implementation in the same line to reach product or service outcomes is a safe move. 

Try the existing methods instead of inventing new

 

In the initial stage of implementation, the IT teams can work on existing solutions or outsourced methods. However, many engineers believe that their very truly customized approach, built to fit the exact business case is a better idea. Unfortunately, it is a lengthy process that needs talented professions. As a remedy, taking a solution from the shelf and adjusting it to the particular case is a smart move. Today, many cloud providers are developing machine learning services that could mitigate the talent gap.