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.

Diabetes Classification

Train an XGBoost model on the Pima Indians Diabetes Dataset.

House Price Regression

Use dynamic workflows to train a multiregion house price prediction model using XGBoost.

MNIST Classification

Train a neural network on MNIST with PyTorch and W&B

NLP Processing with Gensim

Word embedding and topic modelling on lee background corpus with Gensim

Sales Forecasting

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.

EDA and Feature Engineering With Papermill

How to use Jupyter notebook within Flyte

Data Cleaning and Feature Serving With Feast

How to use Feast to serve data in Flyte

🧪 Bioinformatics

Perform computational biology with Flyte.

Nucleotide Sequence Querying with BLASTX

Use BLASTX to Query a Nucleotide Sequence Against a Local Protein Database

🔬 Flytelab

The open-source repository of machine learning projects using Flyte.

Weather Forecasting

Build an online weather forecasting application.