Deploy a Forecasting Model you own this product

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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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.


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:

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


You choose the schedule and decide how much time to invest as you build your project.
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