New RunLLM response generated: >>>*# Question* Hey all :) TLDR - Did anyone develop a robust, generic workflow for model development that can be shared across data scientists? we are currently upgrading our model development environment. we would like to create a workflow with the task structure of: • preprocessing data • processing features • model train • model evaluation this wf will be used by all our data scientists so it has to be very flexible in terms of different types of models and code. the researcher basically should define a generic model config, alter some of the code, then run it. did anyone develop this already? would love to learn from your experience and exchange thoughts. # Answer To develop a robust, generic workflow for model development that can be shared across data scientists, you can leverage Flyte's capabilities for creating flexible and reusable workflows. Flyte allows you to define tasks for each step of your model development process, such as data preprocessing, feature processing, model training, and evaluation. These tasks can be parameterized and strongly typed, making it easy to adapt them for different models and datasets. Flyte supports the use of container images for tasks, allowing you to encapsulate different processing environments and dependencies. This enables data scientists to modify code and configurations without affecting the underlying workflow structure. Additionally, Flyte's versioning and caching features can help manage different model configurations and reuse pre-computed results, enhancing efficiency and collaboration. Would you like more details on how to implement this in Flyte, or do you have specific questions about any of the workflow steps? Sources: - Introducing Flyte: A Cloud Native Machine Learning and Data Processing Platform Slack Link https://.slack.com/archives/CP2HDHKE1/p1731145137.136709 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.