5. Assigning Roles and Communication
Maintaining transparent communication across data science, data engineering, DevOps, and other relevant teams is pivotal to ML models’ success. But assigning roles, giving detailed access, and monitoring for every team is complex. Strong collaboration and an overdose of communication are essential to identify risk across different areas at an early stage. Keeping data scientists deeply involved can also decide the future of the ML model.
In addition to the above challenges, unforeseen events such as the COVID-19 have to be watched out for. When the customer’s buying behaviors suddenly change, the solutions from the past cease to apply and the absence of new data to adequately train models becomes a roadblock. Scaling ML models isn’t easy. Watch out for our next piece on the best practices to productionize ML models at scale.