Data Science in Production: Building Scalable Model Pipelines with Python

Price: $39.99
(as of Dec 26,2024 14:37:07 UTC – Details)

Publisher ‏ : ‎ Independently published (January 1, 2020)
Language ‏ : ‎ English
Paperback ‏ : ‎ 234 pages
ISBN-10 ‏ : ‎ 165206463X
ISBN-13 ‏ : ‎ 978-1652064633
Item Weight ‏ : ‎ 14.7 ounces
Dimensions ‏ : ‎ 6 x 0.55 x 9 inches

Data Science in Production: Building Scalable Model Pipelines with Python

In the world of data science, building models is only half the battle. To truly leverage the power of data, these models need to be deployed and maintained in production systems. This is where the concept of model pipelines comes into play.

Model pipelines are a series of interconnected steps that take raw data as input, transform it, and then use it to make predictions. These pipelines are crucial for scaling data science projects, as they allow for consistent, repeatable processes that can be easily deployed and monitored.

Python is a popular choice for building model pipelines, thanks to its extensive libraries for data manipulation and machine learning. In this post, we’ll explore how to build scalable model pipelines using Python.

1. Data Preprocessing: The first step in building a model pipeline is data preprocessing. This involves cleaning and transforming raw data into a format that can be used by machine learning algorithms. Python libraries like pandas and scikit-learn are invaluable for this task.

2. Feature Engineering: Once the data is cleaned, the next step is feature engineering. This involves creating new features or transforming existing ones to improve the performance of the model. Python libraries like feature-engine and featuretools can help automate this process.

3. Model Training: With the data preprocessed and the features engineered, it’s time to train the model. Python libraries like scikit-learn and TensorFlow offer a wide range of machine learning algorithms that can be used for this task.

4. Model Evaluation: After training the model, it’s important to evaluate its performance. Python libraries like scikit-learn and matplotlib can help visualize the results and identify areas for improvement.

5. Deployment: Once the model is trained and evaluated, it’s time to deploy it in a production environment. Python libraries like Flask and FastAPI can help build APIs for serving predictions, while tools like Docker and Kubernetes can help manage the deployment process.

By following these steps and leveraging the power of Python libraries, data scientists can build scalable model pipelines that can be easily deployed and maintained in production systems. This is essential for turning data science projects into actionable insights that drive business value.
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