Machine learning is one of the hot topics in 2017. Besides, most authorities expected that machine learning would be part of every developer’s toolbox in near future. It would not just be tool for experts or researchers. However, there are still challenges for machine learning professionals.
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Even though, workflows are fully reproducible and we can add new features to models ideally. In reality, a ML model is hard to re-create and we cannot add new features to the model.
Suppose that you need to add date based sql term (e.g. sysdate – membershipdate) into input features. You would be aware of that that’s not an easy task if you have production experience. In fact, data scientists train ML models mostly in Python or R, and need to reproduce in production on Java. We should build models and train them on production. Additionally, you might evaluate to consume and call scripting languages from high level languages. For instance, NodeJS can interact with the Java, Python shell, R project, and also TensorFlow. This reveals that we should develop an interface between data science and application code.
Moreover, ML related things such as input features, model structure (layer and node size), configurations (epoch, learning rate, momentum, activation function), datasets, success metrics, and trained models (weights) are mostly stored nowhere. Common systems should be consumed to represent and manage machine learning models such as Predictive Model Markup Language and ModelDB. This approach provides to comply with standards. In this way, business manner would systematize.
One Travels Fast, Team Travel Longer
Finally, data science team roles don’t fall in place yet. Even though, data scientist and data engineer are top of the best jobs based on Glassdoor’s research in 2017, definitions of these roles are still transparent. Data engineer responsibilities are mostly expected from data scientists. Workforce vacancy in data science field might be the reason. You may be familiar with presentations including “we are hiring, too” announcement if you attend any ML summit nowadays.
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