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The Single Post You Need to Learn the Basics of Flyte
- Flyte is a Kubernetes-native workflow orchestrator for data processing, ML, and analytics built in Go.
- It offers simplicity, powerful tools, and flexibility for orchestrating workflows, akin to Airflow.
- Setting up Flyte locally involves installing flytectl, starting a local deployment, creating test data, and uploading files to Minio.
- Creating workflows in Flyte involves defining tasks using Python, Docker images, and annotations.
- Tasks in a workflow can load and process data, clean and aggregate data, detect outliers, and create reports.
- The workflow created in the demo showcases Flyte features with tasks like data loading, cleaning, aggregation, and outlier detection.
- Running the workflow is done using 'pyflyte run' command, with defined flags for project, domain, and workflow file.
- The workflow execution progress can be tracked on the UI, and outputs like reports can be accessed and downloaded.
- Flyte provides a straightforward way to manage workflows and tasks, making it easy to integrate into your platform.
- Learning the basics of Flyte enables users to efficiently orchestrate data processing and ML workflows.
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