Best Practices: How To Build Scalable Data Pipelines For Machine Learning

Explore the future of machine learning data pipelines with Qubole. Our webinar dives into the intricate process of building scalable data pipelines, essential for powering machine learning models. With a comprehensive overview of Qubole’s platform, real-world customer architectures, and a detailed live demo, you’re in for an educational treat.

What You’ll Learn:

Introduction to Qubole:
- Discover how Qubole’s managed, cloud-native data platform caters to a wide array of data personas, offering a suite of engines such as Hive, Hadoop, Spark, and Presto for efficient data processing and machine learning.
Building Machine Learning Data Pipelines:
- Understand the step-by-step process of constructing robust machine learning data pipelines, from data exploration and building pipelines to orchestration and delivery of datasets.
Live Demonstration:
- Witness a live demo showcasing the Qubole platform in action, demonstrating how to seamlessly create and execute a machine learning data pipeline using Hive, Spark, and Airflow.
Customer Success Stories:
- Learn from the architectures of Qubole’s customers like Lyft and Ibotta, illustrating how they leverage Qubole for scalable, efficient data processing and machine learning.
Choosing the Right Engine:
- Gain insights on selecting the appropriate engines for various tasks within your data pipelines, ensuring optimal performance, scalability, and cost efficiency.

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