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prerequisites
intermediate Python • basic SQL • intermediate data science • basics of deep learning
skills learned
serve our predictions with a REST API using FastAPI • validate the API query parameters by using Python’s type annotations and pydantic • track our model’s performance with MLflow
Jiahao Weng
1 week · 4-6 hours per week · INTERMEDIATE
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team

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Look inside

In this liveProject, you’ll architect a solution to serve predictions from time series forecasting models over a REST API. Once you’ve architected your solution from a high-level perspective, you’ll monitor and assess the performance of the model and potentially undertake retraining to improve accuracy.

This project is designed for learning purposes and is not a complete, production-ready application or solution.

project author

Jiahao Weng
Jiahao Weng is a machine learning practitioner and a senior data scientist at a multinational company where he delivers projects ranging from proof-of-concept to production machine learning systems. As a freelance writer, he also contributes data science Medium articles to share his knowledge with the community.

prerequisites

The liveProject series is for intermediate data scientists interested in tackling their first end-to-end machine learning project. To begin this liveProject, you will need to be familiar with the following:


TOOLS
  • Intermediate Python
  • Basic SQL
  • Basic VS Code or other Python IDE
TECHNIQUES
  • Intermediate data science
  • Basics of deep learning

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book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.

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