Code Snippet:

Code Snippet:

from prefect import task, Flow
from prefect.executors import ThreadPoolExecutor
from sklearn import datasets
from sklearn.model_selection import train_test_split

with Flow('MLOps Pipeline') as flow:
    iris = task(datasets.load_iris)()
    X, y = task(train_test_split)(iris.data, iris.target, test_size=0.2)
    model = task(logistic_regression).fit(X, y)

This snippet showcases a Prefect flow that loads the Iris dataset from scikit-learn, splits it into training and testing sets, trains a logistic regression model, and does all of this in a structured, reproducible way using Prefect's task and flow concepts. By leveraging Prefect's thread pool executor, this pipeline can be run in a multi-threaded environment, significantly speeding up the computation time for large datasets.

The key benefits of this approach are:

• Reusability: The tasks can be reused across various flows and pipelines
• Modularity: The pipeline is structured in a modular way, making it easier to understand and maintain
• Scalability: Prefect supports distributed and parallel execution, making it ideal for large-scale machine learning pipelines

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