![]() ![]() Version control is a requirement - You can't have a reproducible project if the code and training data isn't in version control.A properly structured ML project makes it easy to use pip for packaging a model, making it easy for anyone to benefit from your work. Python already has a good package manager - We don't need overly abstracted solutions to package a trained ML model.A notebook is great for exploring, but not for production - A data science notebook is where experimentation starts, but you can't create a reproducible, collaborative ML project with just a *.ipynb file.Instead, we need guide rails to help data scientists structure projects without forcing them to also become software engineers. A Reproducible, collaborative project is a solved problem for classical software - We don't need to re-invent the wheel for machine learning projects.Iris Classification with SKLearn (Coming soon).Since whisk makes reproducibility "just work", in most cases you simply need to run whisk setup to use the models generated by the projects. Check out these examples and clone them locally. The whisk-ml GitHub org contains example whisk projects. To try out all of the features, continue the quick tour of whisk →. Generate the project directory structure.If you use a different project name, be sure to replace demo with the name of your project. Note: We use demo as the project name in the examples below. Begin a terminal session and run the commands below. Whisk does the structuring while you focus on the data science. Instead, it lets you leverage the large Python ecosystem by structuring your ML project in a Pythonic-way. Whisk doesn't lock you into a particular ML framework or require you to learn yet another ML packaging API. Release your model to the world without a software engineer. It combines a suite of lightweight tools with a logical and flexible project structure. Whisk is an open-source ML project framework that makes collaboration, reproducibility, and deployment "just work". Whisk: The easy-bake ML project structure ![]()
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