Development tools such as Jupyter are prevalent among data scientists because they provide an environment to explore data visually and interactively. However, when deploying a project, we must ensure the analysis can run reliably in a production environment like Airflow or Argo; this causes data scientists to move code back and forth between their notebooks and these production tools. Furthermore, data scientists have to spend time learning an unfamiliar framework and writing pipeline code, which severely delays the deployment process. Ploomber solves this problem by providing: A workflow orchestrator that automatically infers task execution order using static analysis. A sensible layout to bootstrap projects. A development environment integrated with Jupyter. Capabilities to export to production systems (Kubernetes, Airflow, and AWS Batch) without code changes. * Who and why * This talk is for data scientists (with experience developing Machine Learning projects) looking to enhance their workflow. Experience with production tools such as Airflow or Argo is not necessary. The talk has two objectives: Advocate for more development-friendly tools that let data scientists focus on analyzing data and take off the overhead of popular production tools. Demonstrate an example workflow using Ploomber where a pipeline is developed interactively (using Jupyter) and deployed without code changes.