Workflows and processes are a critical need for every machine learning and AI project. When done well, they can enable you to operate more efficiently and ensure that your hard work can successfully make it into production on time. But if you\u2019re a data scientist, although workflows and processes are helpful, they\u2019re not data science.<\/p>\n\n\n\n
That raises the question \u2013 what steps can you take to make workflows and processes as easy as possible for you to spend less time on logistics, and more time on data science?<\/p>\n\n\n\n
That\u2019s the question we set out to answer as we worked on the next iteration of Comet\u2019s Workspaces and Projects. Some of the features were straightforward \u2013 for example, making it easy to create and share interactive reports with your colleagues and executives. But others \u2013 like ML Templates \u2013 were designed to help you standardize how you approach and manage your experiments, ultimately enabling you to go from planning, to experimentation, to production as efficiently as possible while still supporting reproducibility and visibility as part the process.<\/p>\n\n\n\n
With that in mind, let\u2019s take a look at the three main features of this update:<\/p>\n\n\n\n