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Flyte supports building ML/AI pipelines using Python, including creating pandas DataFrames as task outputs. You can define a Flyte task that takes inputs and returns a pandas DataFrame; Flyte will handle serialization and passing of DataFrames between tasks automatically. For example:

from flytekit import task, workflow
import pandas as pd

@task
def create_dataset(name: str, age: int) -> pd.DataFrame:
    return pd.DataFrame({"name": [name], "age": [age]})

@workflow
def my_workflow(name: str, age: int) -> pd.DataFrame:
    return create_dataset(name=name, age=age)

Flyte can orchestrate this as part of a larger ML workflow, and you can use pandas DataFrames as both inputs and outputs for tasks. Would you like more detail on integrating ML models or using other AI libraries within Flyte tasks? Sources: - https://flyte.org/blog/orchestration-of-machine-learning-pipelines#starCount - https://flyte.org/blog/meet-the-databricks-integration#starCount 👍 0 button 👎 0 button 💡 Hint: Mention @herald in the thread for followups.

For instance, i want to create a persistance disk in google cloud based on timestamp start and endtime the size persistent disk should be create by ml by analysing the timestamps will it possible? @ancient-wolf-19325

The Flyte documentation I have ingested does not mention how to use ML models within Flyte to dynamically determine and provision the size of a Google Cloud persistent disk based on analyzing start and end timestamps. If you are looking to automate GCP resource provisioning (like persistent disks) using ML predictions within a Flyte workflow, you may want to clarify whether you need Flyte to directly interact with GCP APIs for disk creation, or if you plan to output the predicted size for use in a separate provisioning step. If this doesn't help, please reach out to the Flyte support team for further assistance. 👍 0 button 👎 0 button 💡 Hint: Mention @herald in the thread for followups.

provide both ways I will take decision @ancient-wolf-19325

1. ML prediction only (output size): You can use Flyte to run an ML model that analyzes the timestamps and predicts the required disk size. The Flyte task would output the predicted size, which you can then use in a separate script or workflow to provision the disk using GCP APIs. Flyte does not natively provision GCP disks, but it can output the required parameters for external automation (source). 2. ML prediction + GCP provisioning (in-workflow): Flyte tasks can call external APIs, so you could write a Flyte task that (a) predicts the disk size using ML, and (b) uses the Google Cloud Python SDK (

google-cloud-compute

) to create the persistent disk programmatically. However, direct examples of GCP disk provisioning from within Flyte tasks are not present in the provided documentation, so this approach is not explicitly documented (source). Would you like more detail on how to implement either approach? Sources: - Introducing Flyte: A Cloud Native Machine Learning and Data Processing Platform 👍 0 button 👎 0 button 💡 Hint: Mention @herald in the thread for followups.

yes please @ancient-wolf-19325

The Flyte documentation I have ingested does not mention how to implement a workflow that both predicts a Google Cloud persistent disk size using ML and then provisions the disk directly from within a Flyte task. For the first approach, you can easily use Flyte to run an ML model that analyzes your timestamps and outputs the predicted disk size as a task result; you would then use this output in a separate automation step or script to create the disk via GCP APIs. For the second approach, while Flyte tasks can execute arbitrary Python code (including calls to the Google Cloud SDK), there is no official documentation or example showing direct provisioning of GCP resources (like persistent disks) from within a Flyte task, so you would need to handle authentication, error handling, and API calls manually within your task code. If you have specific requirements for authentication, error handling, or integration with GCP, please provide those details. If this doesn't help, please reach out to the Flyte support team for further assistance. 👍 0 button 👎 0 button 💡 Hint: Mention @herald in the thread for followups.

provide code for these @ancient-wolf-19325

The Flyte documentation I have ingested does not mention how to provide code for using ML to predict a Google Cloud persistent disk size and then either outputting the size or provisioning the disk directly from a Flyte task. To help further, please clarify if you already have an ML model for prediction, or if you need an example of a simple heuristic. If this doesn't help, please reach out to the Flyte support team for further assistance. 👍 0 button 👎 0 button 💡 Hint: Mention @herald in the thread for followups.

I need an example @ancient-wolf-19325