In addition, engineering isn’t always deemed essential for data scientists. This is a problem because engineers might no

Not only is this a waste of time and resources. It can also lead to additional confusion when stakeholders don’t know which version of the code to use, and who to turn to if they encounter any bugs.

Tech executives strongly believe in the power of AI as a whole, but that doesn’t mean that they’re convinced by every idea out there. As Algorithmia reports, a third of all business executives blame the poor deployment statistics on a lack of senior buy in.

It seems as if data scientists are still viewed as somewhat nerdy and devoid of business sense. This makes it all the more important that data scientists amp up their business skills and seek the dialog with senior execs whenever possible.

Change doesn’t come from one day to the next. For companies who are just getting started in machine learning models, it’s therefore advisable to start with a really small and simple project.

Finally, data sourcing may not be easy or even possible. This can be due to silo-structures in companies, as discussed earlier, or due to other challenges in obtaining more data.

It’s all the more important that as soon as a project is started, a benchmark is established against which the model runs now and in the future. In combination with diligent version control, data scientists can get their models reproducible.

Of course, that doesn’t mean that every data scientist suddenly needs an MBA to excel at their job. However, some key learnings from classes or business experience might serve them a long way.

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Why is Machine Learning Deployment Hard? After several AI projects, I realized that deploying Machine Learning (ML) models at scale is one of the most important…towardsdatascience.com

If 90 percent of a data scientist’s efforts lead to nothing, that’s not a good sign. This isn’t the fault of data scientists, as shown above, but rather due to inherent and organizational obstacles.

Some pipelines start in Python, continue in R, and end in Julia. Others go the other way around, or use other languages entirely. Since each language comes with unique sets of libraries and dependencies, projects quickly get hard to keep track of.

Although data scientists have an advantage if they’re able to implement their models, they should clearly communicate with the engineers about what needs to be done by whom. This way, they’ll save the company’s time and resources.

In addition, some pipelines might make use of containerization with Docker and Kubernetes, others might not. Some pipelines will deploy specific APIs, others not. And the list goes on.

Connected with the above issue is that there is, as of now, no go-to way of versioning machine learning models. It’s quite obvious that data scientists need to keep track of any changes they make, but that’s quite cumbersome these days.

For one, the hardware or cloud storage space to handle bigger datasets might not be available. In addition, modularity of machine learning models doesn’t always work the same at large scales as it does on small ones.