Data Science in Production：Building Scalable Model Pipelines with Python     

生产中的数据科学：用Python构建可伸缩的模型管道   


Book Description
Putting predictive models into production is one of the most direct ways that data scientists can add value to an organization. By learning how to build and deploy scalable model pipelines,data scientists can own more of the model production process and more rapidly deliver data products.
This book provides a hands-on approach to scaling up Python code to work in distributed environments in order to build robust pipelines. Readers will learn how to set up machine learning models as web endpoints,serverless functions,and streaming pipelines using multiple cloud environments. It is intended for analytics practitioners with hands-on experience with Python libraries such as Pandas and scikit-learn,and will focus on scaling up prototype models to production.
From startups to trillion dollar companies,data science is playing an important role in helping organizations maximize the value of their data. This book helps data scientists to level up their careers by:taking ownership of data products with applied examples that demonstrate how to:Translate models developed on a laptop to scalable deployments in the cloud
Develop end-to-end systems that automate data science workflows
Own a data product from conception to production
Here are the topics covered by:Data Science in Production: