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Essential PySpark for Scalable Data Analytics

Essential PySpark for Scalable Data Analytics

By : Nudurupati
4.4 (13)
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Essential PySpark for Scalable Data Analytics

Essential PySpark for Scalable Data Analytics

4.4 (13)
By: Nudurupati

Overview of this book

Apache Spark is a unified data analytics engine designed to process huge volumes of data quickly and efficiently. PySpark is Apache Spark's Python language API, which offers Python developers an easy-to-use scalable data analytics framework. Essential PySpark for Scalable Data Analytics starts by exploring the distributed computing paradigm and provides a high-level overview of Apache Spark. You'll begin your analytics journey with the data engineering process, learning how to perform data ingestion, cleansing, and integration at scale. This book helps you build real-time analytics pipelines that help you gain insights faster. You'll then discover methods for building cloud-based data lakes, and explore Delta Lake, which brings reliability to data lakes. The book also covers Data Lakehouse, an emerging paradigm, which combines the structure and performance of a data warehouse with the scalability of cloud-based data lakes. Later, you'll perform scalable data science and machine learning tasks using PySpark, such as data preparation, feature engineering, and model training and productionization. Finally, you'll learn ways to scale out standard Python ML libraries along with a new pandas API on top of PySpark called Koalas. By the end of this PySpark book, you'll be able to harness the power of PySpark to solve business problems.
Table of Contents (19 chapters)
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1
Section 1: Data Engineering
6
Section 2: Data Science
13
Section 3: Data Analysis

Tracking model versions using MLflow Model Registry

While the MLflow Tracking server lets you track all the attributes of your ML experiments, MLflow Model Registry provides a central model repository that lets you track all the aspects of your model life cycle. MLflow Model Registry consists of a user interface and APIs to track the model's version, lineage, stage transitions, annotations, and any developer comments. MLflow Model Registry also contains webhooks for CI/CD integrations and a model server for online model serving.

MLflow Model Registry provides us with a way to track and organize the many ML models that are produced and used by businesses during development, testing, and production. Model Registry provides a secure way to share models by leveraging access control lists and provides a way to integrate with model governance and approval workflows. Model Registry also allows us to monitor ML deployments and their performance via its API.

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