In this video, we delve into the world of building production-level ML pipelines using Kedro. If you're looking to improve the productivity of your machine learning projects and streamline your workflows, this step-by-step guide is for you.

Join us as we explore the challenges of deploying machine learning models from Jupyter notebooks and discover the importance of incorporating Kedro, an open-source Python framework widely used in industries. We'll dive into the functionalities and integration options that Kedro provides, empowering you to develop robust and standardized machine learning pipelines.

With Kedro, you no longer have to worry about tedious tasks like data import and loading. It automates these processes, allowing you to focus on writing cleaner and more efficient code. The framework also simplifies model packaging, making it easier to deploy and utilize your ML models for inferencing.

We'll walk you through the steps of setting up a Kedro project template, creating a data engineering pipeline, and leveraging Kedro's extensibility for tracking experiments or deploying with tools like MLflow, Airflow, or Kubernetes. You'll gain insights into visualizing your pipeline's flow and optimizing your code for production environments.

Whether you're a data scientist or a data engineer, Kedro provides a standardized structure that can enhance your productivity and help you build machine learning systems with industry best practices.

Don't miss this opportunity to learn how to streamline your ML workflows and build production-ready pipelines. Watch the video now and take your machine-learning projects to the next level with Kedro!


Kedro:
https://docs.kedro.org/en/stable/

Github Code:
https://github.com/karndeepsingh/kedr...

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1. LinkedIn:   / karndeepsingh  

2. Telegram Group: https://telegram.me/datascienceclubac...

3. Github: https://www.github.com/karndeepsingh

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