Data scientists at the start of their careers often have the misconception that their job will be largely focused on training and improving models in a Jupyter notebook. The reality is that most data science value is created outside of a notebook.
This crash course is intended for data scientists with basic knowledge of developing machine learning models in a Jupyter notebook setting. By the end of the session, you will know:
- What is MLOps (Machine Learning Operationalization) and why it's necessary?
- What is a machine learning pipeline?
- How to create and deploy a fully reproducible MLOps pipeline from scratch.
- Learn the basics of continuous training, drift detection, alerts, and model deployment.
Hamza Tahir is a software developer turned ML engineer. An indie hacker by heart, he loves ideating, implementing, and launching data-driven products. His previous projects include PicHance, Scrilys, BudgetML, and you-tldr. Based on his learnings from deploying ML in production for predictive maintenance use-cases in his previous startup, he co-created ZenML, an open-source MLOps framework to create reproducible ML pipelines.
