5 quick but proven tips to implement machine learning the right way

Businesses that realize the value of data and contemplate machine learning implementation are usually faced with similar challenges, that can be overcome by following a few simple guidelines.

Business leaders are increasingly becoming aware of data science and machine learning’s role in supporting and enhancing business growth. In an attempt to leverage the technology, businesses either jump into implementation without planning or are stuck in the planning phase for too long, both leading to suboptimal outcomes. As business and technology leaders involved in machine learning implementation, you should keep the following tips in mind to ensure that you are doing it the right way:

Use the right data

Like any other analytical or logical application, the principle of Garbage in, Garbage Out holds true for machine learning. When it comes to machine learning implementation, although the algorithm is considered to be an important factor for driving success, the data that is fed to it is equally important, if not more. The quality and relevance of data used in machine learning helps extract highly valuable insights and sets the machine learning initiative in the right direction. Choosing the right variables to track and process through the algorithm requires asking the right questions and verifying the data quality.

Experiment with algorithms

Successful machine learning implementation requires a combination of quality data and a robust algorithm. It may not be easy to get everything right with your algorithms in the first attempt, requiring you to make improvements based on trial and error. Investigating your algorithms to pick out behaviors that are desirable, as well as, the ones that are not, will enable you to modify algorithm parameters to achieve the ideal results. You should also realize that algorithms that track highly complex data, such as that associated with humans, need constant re-evaluation and re-programming to ensure sustained effectiveness.

Collaborate with research and academic institutions

One way to make up for lack of expertise in machine learning implementation is partnering with academic institutions that research and teach machine learning and analytics. This will enable organizations to make use of subsidized expertise while making progress using machine learning. An example of such collaboration between business and academia is the Prototype optimization model made by Wayne State University that saved $12 million for Ford Motor Company on the first use. As a long-term initiative, businesses should invest in data science labs, to promote such fruitful collaborations with academic institutions.

Train employees in machine learning implementation

Another way to compensate for inadequacy in know-how is to ‘upskill’ existing employees through training to make them proficient in data science applications. Employees for an upskill should be selected based on aptitude for work in question. The most suitable candidates to receive such a training are the ones with a high aptitude for math and statistics, and the ability to translate data into useful insights. Organising training seminars and workshops are among the most common ways to train employees in a chosen skill.

Hire third-party expertise

As there is high demand for data science experts, businesses can consult third-party specialists to help them initiate and integrate machine learning into the business. Experienced third-party professionals can guide your business through planning and execution of pilot projects, and educate organizational personnel on data science and machine learning.

Keeping these tips in mind will enable you, as a CIO or technology leader, to keep your machine learning implementation on the right track. To make the most of any technology, the best way is to gain enough practical knowledge about it. In addition to the tips mentioned above, you should also acquaint yourself with some machine learning best practices.