Learn to build large language model applications: vector databases, langchain, fine tuning and prompt engineering. Learn more

MLOps Crash Course for Beginners


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:

  1. What is MLOps (Machine Learning¬†Operationalization) and why it’s necessary?
  2. What is a machine learning pipeline?
  3. How to create and deploy a fully reproducible MLOps pipeline from scratch.
  4. Learn the basics of continuous training, drift detection, alerts, and model deployment.
Hamza Tahir-Data Science Dojo
Hamza Tahir

Co-Creator of ZenML

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.

We are looking for passionate people willing to cultivate and inspire the next generation of leaders in tech, business, and data science. If you are one of them get in touch with us!