After training a model, data scientists often need their team, including managers, to try it out. However, team members might use different operating systems and lack familiarity with the data scientist’s programming language. Thus, it’s crucial for data scientists to containerize their model and create an intuitive API endpoint for seamless interaction. Enter BentoML, which simplifies the process of deploying models into production. This presentation demonstrates how data practitioners can containerize and share their local machine learning models effortlessly using just a few lines of Python code.
DevRel @ Prefect