With increasing focus on Machine Learning systems in almost every business area, it is important to build a great pipeline to train, test and deploy your models. In this session we will show a way to do that with Jupyter and Azure. The session will cover the following topics:
- creating a simple image classification service without coding
- creating a PyTorch model from scratch
- Training and Testing the PyTorch model
- Saving the model locally and on a cloud storage (with self-made versioning)
- evaluating multiple models
- deploying an API (via Docker) to get predictions from the model
- using DevOps to update the API
Please bring your own device, as we will be running the workshop on individual machines.
- make sure you have a Python environment >= 3.5 installed (preferred using Anaconda)
- we will be using Azure, so make sure you have a Microsoft account - no Azure registration needed beforehand
- optional: Install Docker on your machine, install VS Code, install Azure Storage Explorer
Daniel is a Data Engineer at Microsoft, working with customers to create services that get insights from data and take that to improve the system through Machine Learning.
visit the speaker at: Github