I’m currently working at a startup and planning to build a data closed-loop system using Flyte. Our pipeline will cover the entire lifecycle: data collection, cleaning, labeling, feature embedding, model training, and offline inference. Since we are starting from scratch with no legacy constraints, would you recommend going straight with Flyte 2.0 or sticking with Flyte 1.x? Also, do you have any specific advice for this setup?

It seems likely that starting with Flyte 2.0 is the best choice for a new project with no legacy constraints. Flyte 2.0 offers a more flexible, Pythonic, and asynchronous workflow authoring experience, improved resource efficiency, and a streamlined API, making it well-suited for end-to-end ML/data pipelines like yours. The documentation emphasizes that Flyte 2.0 is designed for modern AI workflows and is recommended for new deployments (Flyte 2.0 User Guide, Flyte 2.0 Overview). For your setup, my understanding is that you should leverage Flyte 2.0's features such as pure Python task/workflow authoring, strong type safety, resource isolation per task, and built-in support for caching, retries, and extensibility via plugins (e.g., for Spark, PyTorch, data validation, and notebook integration). Consider using Flyte's grouping, fanout, and error handling patterns to structure your pipeline stages, and take advantage of the platform's observability and reproducibility features (Flyte 2.0 User Guide, ). Would you like more detail on migration, plugin selection, or best practices for each pipeline stage? Sources: - Flyte 2.0 User Guide - Flyte 2.0 Overview - Figure Image 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.