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The Early Adoption of Machine Learning Projects

Machine Learning is in its early stages of adoption. While there are a lot of examples stating the benefits, putting a Data Science team together requires some preparation. Before implementing Machine Learning into production, there are some important steps that should be taken before any major ML Project kicks off. Here is a summary from our findings:

Business' Role:

A major stumbling block can occur when a business problem isn't clearly articulated from the very beginning. The questions asked by the business need to be matched up by whether the data is available. Clients will be well served by identifying a business champion when considering a data science strategy.

Leadership's Role:

Machine Learning Projects are very experimental-oriented. The business' leadership must foster a stronger culture of empowerment and experimentation. There must be a willingness to include data in the overall vision of the business.

Talent Identification:

Many businesses make the assumption that huge investments in new talent and skillsets are required to implement a data science strategy. This is rarely the case. It works to identify employees who are apt to be genuinely interested and have a certain skillset related to data science. Identifying someone from Engineering is a great place to start but it is advantageous that this person have multi-disciplinary experience.

Scoping Work:

As with any new initiative, there will be a lot of learning and education involved. A Pilot Project is typically identified for which a lot of data gathering needs to occur. Expect 30 - 50% of effort to properly scope out a ML Pilot Project.

Bottlenecks to be aware of:

Some bottlenecks found include:

Ability to collect the right data. For example, in predictive maintenance, the unavailability of failure history could hinder the results
Leadership vision on company data and how to use it
Lack of incentive

 

References:

[1] Darveau, P. Prognostics and Availability for Industrial Equipment Using High Performance Computing (HPC) and AI Technology. Preprints 2021, 2021090068 .

[2] Darveau, P., 1993. C programming in programmable logic controllers.

[3] Darveau, P., 2015. Wearable Air Quality Monitor. U.S. Patent Application 14/162,897.