How to Bring Your ML Models to Production Faster

After different AI projects, I realized how quickly building efficient Machine Learning models is becoming a core competency for companies to compete more effectively. Decision-makers are learning that managing the whole lifecycle of building, deploying, and debugging models within their existing tech stack is not straightforward and brings a new set of challenges. Based on my experience, data scientists often spend time analyzing a dataset, look for suitable algorithms, train a new model, then hand it over to data engineers to run in production. This separation can lead to problems where data scientists don't see the challenges of running the model in production, and data engineers don't know how the models are structured. I have seen many times data scientists writing applications that don't scale in production.