#java #cicd #continuous_delivery #continuous_deployment #continuous_integration #devops #groovy #hacktoberfest #jenkins #pipelines_as_code

https://github.com/jenkinsci/jenkins

#python #ai #jupyterlab #jupyterlab_extensions #kubeflow_pipelines #machine_learning #notebook_jupyter #notebooks #pipelines

https://github.com/elyra-ai/elyra

#java #airbyte #connectors #data #data_analysis #data_ingestion #data_integration #data_science #data_transfers #elt #etl #incremental_updates #integration #open_source #pipeline #pipelines #replications

https://github.com/airbytehq/airbyte

#python #cloud #data_pipelines #data_science #deployment #docker #ide #jupyter #jupyterlab #machine_learning #notebooks #orchest #pipelines #self_hosted

https://github.com/orchest/orchest

#java #apache #hop #pipelines #streaming

https://github.com/apache/hop

#python #data_engineering #data_science #jupyter #jupyter_notebooks #machine_learning #mlops #papermill #pipelines #pycharm #vscode #workflow

https://github.com/ploomber/ploomber

#python #ai #automl #data_science #deep_learning #devops_tools #hacktoberfest #llm #llmops #machine_learning #metadata_tracking #ml #mlops #pipelines #production_ready #pytorch #tensorflow #workflow #zenml

https://github.com/zenml-io/zenml

#nextflow #dsl2 #modules #nextflow #nf_core #nf_test #pipelines #workflows

Using **nf-core** helps you create and run bioinformatics pipelines easily. It provides a collection of ready-made modules for tools like `fastqc` and `bwa`, which you can add to your pipelines. This makes it easy to share and reuse code across different projects. **nf-core** also helps ensure that your analyses are reliable and can be repeated by others, which is important for scientific research. You can easily install, update, and manage these modules using **nf-core tools**, making your work more efficient and consistent.

https://github.com/nf-core/modules