Data Science in Production: Building Scalable Model Pipelines
|
Learn how to build scalable model pipelines for data science in production environments, focusing on best practices for deployment and management.
The goal of this course is to provide you with a set of tools that can be used to build predictive model services for product teams.
In this course, you’ll start by covering the different cloud environments and tools for building scalable data and model pipelines. You’ll then learn the different data sets and types of models that will be used heavily in everyday production. Throughout the course, you’ll have plenty of exercises and challenges to get you comfortable working with the diverse toolset.
Lastly, you’ll explore streaming model workflows which is crucial for building real-time data pipelines that move data between different components in a cloud environment.
After working through this course, you will have gained valuable hands-on experience with many of the tools needed to build data products. You will also have a better understanding of how to build scalable machine learning pipelines in a cloud environment.
User Reviews
Be the first to review “Data Science in Production: Building Scalable Model Pipelines”
Related Products
Master Statistics with Python
Learn the statistics behind data science, from summary statistics to regression models.
Intro to Data Analysis
Understand the principles of data analysis, including techniques for data collection, cleaning, and interpretation.
Business Intelligence Analytics
Gain insights into business intelligence tools and analytics techniques to optimize decision-making.
Business Analytics
Explore data analysis techniques to drive business performance and decision-making.
Firebase Analytics: Android
Discover how to implement and leverage Firebase Analytics for tracking user engagement and performance in Android apps.
Analyze Data with Python
Learn to analyze and visualize data using Python and statistics.
There are no reviews yet.