This section showcases step-by-step case studies of how to combine the different features of Flyte to achieve everything from data processing, feature engineering, model training, to batch predictions. Code for all of the examples in the user guide can be found in the flytesnacks repo.
It comes with a highly customized environment to make running, documenting and contributing samples easy. If this is your first time running these examples, follow the setup guide to get started.
Want to contribute an example? Check out the Example Contribution Guide.
🤖 Model Training#
Train machine learning models from using your framework of choice.
Train an XGBoost model on the Pima Indians Diabetes Dataset.
Use dynamic workflows to train a multiregion house price prediction model using XGBoost.
Train a neural network on MNIST with PyTorch and W&B
Word embedding and topic modelling on lee background corpus with Gensim
Use the Rossmann Store data to forecast sales with distributed training using Horovod on Spark.
🛠 Feature Engineering#
Engineer the data features to improve your model accuracy.
Perform computational biology with Flyte.
Use BLASTX to Query a Nucleotide Sequence Against a Local Protein Database
The open-source repository of machine learning projects using Flyte.
Build an online weather forecasting application.