Develop Production-Level Machine Learning Models using MLRun

Data Scientists and ML Engineers from students to seniors.

What are good habits in the 3 phases of a model’s life cycle – Data Preparation, Development, and Deployment.

• How MLRun is built to adopt good habits into your work.
• How MLRun integrates into existing code and enables quality of life features like Automatic Logging, Model Management, and Distributed Training.
• Use MLRun’s model development tools to train classifiers on the classic, known, and loved Iris and MNIST datasets.

• Install MLRun on your computer
• Work with Jupyter notebooks.
• Very basic SciKit-Learn and TensorFlow background.

• What is the model life cycle?
• MLRun – The Open Source MLOps Orchestration Framework.
• MLRun’s model development features – Automatic Logging, Model Management, and Distributed Training.
• Iris demo – Train and deploy an Iris classifier.
• MNIST demo – Train and deploy a handwritten digits classifier.