What's this thing called MLOps? You may have heard about it by now, but never really understood what all the fuzz is about. Let's find out together!

In this tutorial, you will learn about MLOps and take your first steps in a hands-on way. To do so, we will be using Open Source tooling. We will be taking a simple example of Machine Learning use case and will gradually make it more ready for production 🚀.

We start with a simple time-series model in Python using scikit-learn and first add logging steps to make the performance of the model measurable. Don't worry: we will go through it step-by-step, so you won't be overwhelmed. Then, we will log our ML model and load it back into an inference step. Lastly, we will learn about deploying these actual models by Dockerizing our application 🙏.

Welcome! You will be learning about MLOps in a hands-on way. So get ready to get your hands dirty and code along! 👏🏻

Join us if you 🫵:

• Are working with Machine Learning / Data Science
• Have some experience with Python
• Are interested in MLOps and want to get some hands-on experience
• Are interested in taking your Machine Learning model to production

Contents of the tutorial 📌:

1. [15 min] MLOps: what's the fuzz about?
2. [15 min] Why Experiment tracking? 📊
3. [15 min] Hands-on: Logging metrics with MLFlow
4. [10 min] Hands-on: Comparing experiments in the MLFlow interface
5. [15 min] Hands-on: Saving a trained model with MLFlow
6. [20 min] Hands-on: Loading a model with MLFlow and running inference
7. [15 min] How to deploy our application? 🚀
8. [30 min] Hands-on: Dockerizing our application
9. [30 min] Hands-on: Deploying our application

🏡 What you will take home

At the end of the tutorial, you will be taking home the following:

• What MLOps is
• When it's applicable, and why it is important
• How you can track your Machine Learning experiments and build better models because of it
• Separate model training from model inference
• Know how you could deploy your ML model to production

❤️ Open Source Software

Many of the used tooling is Open Source. Open software for all!

🎒 Pre-requisites

Some Python knowledge is required, as well as some general Data Science knowledge: model- training and inference as well as cross-validation. We will not go into details on the Data Science part, but it is good to have a rough understanding about it 👍🏻.