Most data science or machine learning projects fail before deployment. It’s one skill set to deliver a data science or AI-based model, and it’s totally another to engineer software solutions. You need both.
It’s important to embrace agile development methodologies that bridge the gap between data science and DevOps. In traditional agile development, the focus is on deploying programs where the development is static and delivered as final. With machine learning, you instead deploy a learning algorithm. There is no real finality with AI delivery, just a threshold of tolerance for prediction accuracy and confidence.
Machine learning models are learning algorithms and can be thought of more like a living plant than a static web page. This means they require monitoring and maintenance, but thrive and become smarter with care. If left unattended, the model fidelity can drift and decay. Thus, more training — rather than debugging — is required in the new agile for AI.
Remember, models are just algorithms that input data and output different data. They may not even produce a visualization or way for non-technical people to interpret the insight they create. Once the model is complete, you’ll still need software engineers and designers to wrap the model into an interface so end-users may benefit.
Consider bringing in user experience designers and front end developers early in the project planning process. They design intuitive controls and graphical user interfaces so non-technical users will readily adopt the solution. If you have to write a SQL query to leverage the prediction, the solutions will fail. Adopting agile practices that account for machine learning development will ensure data science becomes a practical application.
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