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Modern Data Architectures with Python

You're reading from   Modern Data Architectures with Python A practical guide to building and deploying data pipelines, data warehouses, and data lakes with Python

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Product type Paperback
Published in Sep 2023
Publisher Packt
ISBN-13 9781801070492
Length 318 pages
Edition 1st Edition
Languages
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Author (1):
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Brian Lipp Brian Lipp
Author Profile Icon Brian Lipp
Brian Lipp
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Toc

Table of Contents (19) Chapters Close

Preface 1. Part 1:Fundamental Data Knowledge
2. Chapter 1: Modern Data Processing Architecture FREE CHAPTER 3. Chapter 2: Understanding Data Analytics 4. Part 2: Data Engineering Toolset
5. Chapter 3: Apache Spark Deep Dive 6. Chapter 4: Batch and Stream Data Processing Using PySpark 7. Chapter 5: Streaming Data with Kafka 8. Part 3:Modernizing the Data Platform
9. Chapter 6: MLOps 10. Chapter 7: Data and Information Visualization 11. Chapter 8: Integrating Continous Integration into Your Workflow 12. Chapter 9: Orchestrating Your Data Workflows 13. Part 4:Hands-on Project
14. Chapter 10: Data Governance 15. Chapter 11: Building out the Groundwork 16. Chapter 12: Completing Our Project 17. Index 18. Other Books You May Enjoy

Creating our machine learning application

Here is the main ML function. It will call functions to load data, create modeling data, and train our model:

ml-jobs/ml_jobs/jobs/build_sales_model.py
from ml_jobs.utils.data_prep.get_train_test_split import get_train_test_split
from ml_jobs.utils.extract.get_table import get_table
from ml_jobs.utils.management.setup_experiment import setup_experiment
from ml_jobs.utils.model.train_sales import train_sales
def build_sales_model():
    """
    fill in
    """
     spark = SparkSession \
        .builder \
        .appName("Schema App") \
        .getOrCreate()
    gold_sales = get_table("sales")
    model_data = get_train_test_split(gold_sales)
 ...
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